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[PATCH 3/3] gnu: Add r-vegan.
|
From: |
Ben Woodcroft |
|
Subject: |
[PATCH 3/3] gnu: Add r-vegan. |
|
Date: |
Thu, 16 Jun 2016 22:47:53 +1000 |
* gnu/packages/bioinformatics.scm (r-vegan): New variable.
* gnu/packages/patches/vegan-revert-test-deletion.patch: New file.
* gnu/local.mk (dist_patch_DATA): Add it.
---
gnu/local.mk | 1 +
gnu/packages/bioinformatics.scm | 30 +
.../patches/vegan-revert-test-deletion.patch | 12227 +++++++++++++++++++
3 files changed, 12258 insertions(+)
create mode 100644 gnu/packages/patches/vegan-revert-test-deletion.patch
diff --git a/gnu/local.mk b/gnu/local.mk
index abd05ef..c6aa4fc 100644
--- a/gnu/local.mk
+++ b/gnu/local.mk
@@ -805,6 +805,7 @@ dist_patch_DATA =
\
%D%/packages/patches/util-linux-tests.patch \
%D%/packages/patches/upower-builddir.patch \
%D%/packages/patches/valgrind-enable-arm.patch \
+ %D%/packages/patches/vegan-revert-test-deletion.patch \
%D%/packages/patches/vorbis-tools-CVE-2014-9638+CVE-2014-9639.patch
\
%D%/packages/patches/vorbis-tools-CVE-2014-9640.patch \
%D%/packages/patches/vorbis-tools-CVE-2015-6749.patch \
diff --git a/gnu/packages/bioinformatics.scm b/gnu/packages/bioinformatics.scm
index 04ed769..03a4a02 100644
--- a/gnu/packages/bioinformatics.scm
+++ b/gnu/packages/bioinformatics.scm
@@ -3815,6 +3815,36 @@ data in the form of VCF files.")
;; at https://vcftools.github.io/license.html
(license license:lgpl3)))
+(define-public r-vegan
+ (package
+ (name "r-vegan")
+ (version "2.4-0")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "vegan" version))
+ (sha256
+ (base32
+ "10cygzkyg2m0y054ygivqxrkvqz792qsg6bmbdfzaqq37qv4wc7z"))
+ (patches (search-patches "vegan-revert-test-deletion.patch"))))
+ (build-system r-build-system)
+ (native-inputs
+ `(("gfortran" ,gfortran)
+ ("r-knitr" ,r-knitr)))
+ (propagated-inputs
+ `(("r-cluster" ,r-cluster)
+ ("r-lattice" ,r-lattice)
+ ("r-mgcv" ,r-mgcv)
+ ("r-permute" ,r-permute)))
+ (home-page "https://cran.r-project.org/web/packages/vegan";)
+ (synopsis "Functions for community ecology")
+ (description
+ "The vegan package provides tools for descriptive community ecology. It
+has most basic functions of diversity analysis, community ordination and
+dissimilarity analysis. Most of its multivariate tools can be used for other
+data types as well.")
+ (license license:gpl2+)))
+
(define-public vsearch
(package
(name "vsearch")
diff --git a/gnu/packages/patches/vegan-revert-test-deletion.patch
b/gnu/packages/patches/vegan-revert-test-deletion.patch
new file mode 100644
index 0000000..c93e390
--- /dev/null
+++ b/gnu/packages/patches/vegan-revert-test-deletion.patch
@@ -0,0 +1,12227 @@
+The distributed sources do not include tests with the CRAN package. Here we
+revert the commit `591d0e8ba1deaaf82445474ec6619c0b43db4e63' which deletes
+these tests. There are plans to not delete tests in future as documented at
+https://github.com/vegandevs/vegan/issues/181.
+
+diff --git a/tests/Examples/vegan-Ex.Rout.save
b/tests/Examples/vegan-Ex.Rout.save
+new file mode 100644
+index 0000000..be41f6d
+--- /dev/null
++++ b/tests/Examples/vegan-Ex.Rout.save
+@@ -0,0 +1,8896 @@
++
++R version 3.3.0 Patched (2016-05-12 r70603) -- "Supposedly Educational"
++Copyright (C) 2016 The R Foundation for Statistical Computing
++Platform: x86_64-pc-linux-gnu (64-bit)
++
++R is free software and comes with ABSOLUTELY NO WARRANTY.
++You are welcome to redistribute it under certain conditions.
++Type 'license()' or 'licence()' for distribution details.
++
++ Natural language support but running in an English locale
++
++R is a collaborative project with many contributors.
++Type 'contributors()' for more information and
++'citation()' on how to cite R or R packages in publications.
++
++Type 'demo()' for some demos, 'help()' for on-line help, or
++'help.start()' for an HTML browser interface to help.
++Type 'q()' to quit R.
++
++> pkgname <- "vegan"
++> source(file.path(R.home("share"), "R", "examples-header.R"))
++> options(warn = 1)
++> library('vegan')
++Loading required package: permute
++Loading required package: lattice
++This is vegan 2.4-0
++>
++> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
++> cleanEx()
++> nameEx("BCI")
++> ### * BCI
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: BCI
++> ### Title: Barro Colorado Island Tree Counts
++> ### Aliases: BCI BCI.env
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(BCI, BCI.env)
++> head(BCI.env)
++ UTM.EW UTM.NS Precipitation Elevation Age.cat Geology Habitat Stream
EnvHet
++1 625754 1011569 2530 120 c3 Tb OldSlope Yes
0.6272
++2 625754 1011669 2530 120 c3 Tb OldLow Yes
0.3936
++3 625754 1011769 2530 120 c3 Tb OldLow No
0.0000
++4 625754 1011869 2530 120 c3 Tb OldLow No
0.0000
++5 625754 1011969 2530 120 c3 Tb OldSlope No
0.4608
++6 625854 1011569 2530 120 c3 Tb OldLow No
0.0768
++> ## see changed species names
++> oldnames <- attr(BCI, "original.names")
++> taxa <- cbind("Old Names" = oldnames, "Current Names" = names(BCI))
++> noquote(taxa[taxa[,1] != taxa[,2], ])
++ Old Names Current Names
++ [1,] Abarema.macradenium Abarema.macradenia
++ [2,] Acacia.melanoceras Vachellia.melanoceras
++ [3,] Apeiba.aspera Apeiba.glabra
++ [4,] Aspidosperma.cruenta Aspidosperma.desmanthum
++ [5,] Cassipourea.elliptica Cassipourea.guianensis
++ [6,] Cespedezia.macrophylla Cespedesia.spathulata
++ [7,] Chlorophora.tinctoria Maclura.tinctoria
++ [8,] Coccoloba.manzanillensis Coccoloba.manzinellensis
++ [9,] Coussarea.curvigemmia Coussarea.curvigemma
++[10,] Cupania.sylvatica Cupania.seemannii
++[11,] Dipteryx.panamensis Dipteryx.oleifera
++[12,] Eugenia.coloradensis Eugenia.florida
++[13,] Eugenia.oerstedeana Eugenia.oerstediana
++[14,] Guapira.standleyana Guapira.myrtiflora
++[15,] Hyeronima.alchorneoides Hieronyma.alchorneoides
++[16,] Inga.marginata Inga.semialata
++[17,] Lonchocarpus.latifolius Lonchocarpus.heptaphyllus
++[18,] Maquira.costaricana Maquira.guianensis.costaricana
++[19,] Phoebe.cinnamomifolia Cinnamomum.triplinerve
++[20,] Swartzia.simplex.var.ochnacea Swartzia.simplex.continentalis
++[21,] Tabebuia.guayacan Handroanthus.guayacan
++>
++>
++>
++> cleanEx()
++> nameEx("CCorA")
++> ### * CCorA
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: CCorA
++> ### Title: Canonical Correlation Analysis
++> ### Aliases: CCorA biplot.CCorA
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> # Example using two mite groups. The mite data are available in vegan
++> data(mite)
++> # Two mite species associations (Legendre 2005, Fig. 4)
++> group.1 <- c(1,2,4:8,10:15,17,19:22,24,26:30)
++> group.2 <- c(3,9,16,18,23,25,31:35)
++> # Separate Hellinger transformations of the two groups of species
++> mite.hel.1 <- decostand(mite[,group.1], "hel")
++> mite.hel.2 <- decostand(mite[,group.2], "hel")
++> rownames(mite.hel.1) = paste("S",1:nrow(mite),sep="")
++> rownames(mite.hel.2) = paste("S",1:nrow(mite),sep="")
++> out <- CCorA(mite.hel.1, mite.hel.2)
++> out
++
++Canonical Correlation Analysis
++
++Call:
++CCorA(Y = mite.hel.1, X = mite.hel.2)
++
++ Y X
++Matrix Ranks 24 11
++
++Pillai's trace: 4.573009
++
++Significance of Pillai's trace:
++from F-distribution: 0.0032737
++ CanAxis1 CanAxis2 CanAxis3 CanAxis4 CanAxis5 CanAxis6
++Canonical Correlations 0.92810 0.82431 0.81209 0.74981 0.70795 0.65950
++ CanAxis7 CanAxis8 CanAxis9 CanAxis10 CanAxis11
++Canonical Correlations 0.50189 0.48179 0.41089 0.37823 0.28
++
++ Y | X X | Y
++RDA R squares 0.33224 0.5376
++adj. RDA R squares 0.20560 0.2910
++
++> biplot(out, "ob") # Two plots of objects
++> biplot(out, "v", cex=c(0.7,0.6)) # Two plots of variables
++> biplot(out, "ov", cex=c(0.7,0.6)) # Four plots (2 for objects, 2 for
variables)
++> biplot(out, "b", cex=c(0.7,0.6)) # Two biplots
++> biplot(out, xlabs = NA, plot.axes = c(3,5)) # Plot axes 3, 5. No object
names
++> biplot(out, plot.type="biplots", xlabs = NULL) # Replace object names by
numbers
++>
++> # Example using random numbers. No significant relationship is expected
++> mat1 <- matrix(rnorm(60),20,3)
++> mat2 <- matrix(rnorm(100),20,5)
++> out2 = CCorA(mat1, mat2, permutations=99)
++> out2
++
++Canonical Correlation Analysis
++
++Call:
++CCorA(Y = mat1, X = mat2, permutations = 99)
++
++ Y X
++Matrix Ranks 3 5
++
++Pillai's trace: 0.6455578
++
++Significance of Pillai's trace:
++from F-distribution: 0.70352
++based on permutations: 0.69
++Permutation: free
++Number of permutations: 99
++
++ CanAxis1 CanAxis2 CanAxis3
++Canonical Correlations 0.69691 0.38140 0.12
++
++ Y | X X | Y
++RDA R squares 0.17066 0.1368
++adj. RDA R squares -0.12553 -0.0250
++
++> biplot(out2, "b")
++>
++>
++>
++> cleanEx()
++> nameEx("MDSrotate")
++> ### * MDSrotate
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: MDSrotate
++> ### Title: Rotate First MDS Dimension Parallel to an External Variable
++> ### Aliases: MDSrotate
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> mod <- monoMDS(vegdist(varespec))
++> mod <- with(varechem, MDSrotate(mod, pH))
++> plot(mod)
++> ef <- envfit(mod ~ pH, varechem, permutations = 0)
++> plot(ef)
++> ordisurf(mod ~ pH, varechem, knots = 1, add = TRUE)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ poly(x1, 1) + poly(x2, 1)
++Total model degrees of freedom 3
++
++REML score: -3.185099
++>
++>
++>
++> cleanEx()
++> nameEx("MOStest")
++> ### * MOStest
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: MOStest
++> ### Title: Mitchell-Olds & Shaw Test for the Location of Quadratic Extreme
++> ### Aliases: MOStest plot.MOStest fieller.MOStest profile.MOStest
++> ### confint.MOStest
++> ### Keywords: models regression
++>
++> ### ** Examples
++>
++> ## The Al-Mufti data analysed in humpfit():
++> mass <- c(140,230,310,310,400,510,610,670,860,900,1050,1160,1900,2480)
++> spno <- c(1, 4, 3, 9, 18, 30, 20, 14, 3, 2, 3, 2, 5, 2)
++> mod <- MOStest(mass, spno)
++> ## Insignificant
++> mod
++
++Mitchell-Olds and Shaw test
++Null: hump of a quadratic linear predictor is at min or max
++
++Family: gaussian
++Link function: identity
++
++ hump min max
++ 46.89749 140.00000 2480.00000
++***** Caution: hump/pit not bracketed by the data ******
++
++ min/max F Pr(>F)
++hump at min 140 0.0006 0.9816
++hump at max 2480 0.3161 0.5852
++Combined 0.9924
++> ## ... but inadequate shape of the curve
++> op <- par(mfrow=c(2,2), mar=c(4,4,1,1)+.1)
++> plot(mod)
++> ## Looks rather like log-link with Poisson error and logarithmic biomass
++> mod <- MOStest(log(mass), spno, family=quasipoisson)
++> mod
++
++Mitchell-Olds and Shaw test
++Null: hump of a quadratic linear predictor is at min or max
++
++Family: quasipoisson
++Link function: log
++
++ min hump max
++4.941642 6.243371 7.816014
++
++ min/max F Pr(>F)
++hump at min 4.9416 7.1367 0.02174 *
++hump at max 7.8160 9.0487 0.01191 *
++Combined 0.03338 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> plot(mod)
++> par(op)
++> ## Confidence Limits
++> fieller.MOStest(mod)
++ 2.5 % 97.5 %
++5.255827 6.782979
++> confint(mod)
++ 2.5 % 97.5 %
++5.816021 6.574378
++> plot(profile(mod))
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("RsquareAdj")
++> ### * RsquareAdj
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: RsquareAdj
++> ### Title: Adjusted R-square
++> ### Aliases: RsquareAdj RsquareAdj.default RsquareAdj.rda RsquareAdj.cca
++> ### RsquareAdj.lm RsquareAdj.glm
++> ### Keywords: univar multivariate
++>
++> ### ** Examples
++>
++> data(mite)
++> data(mite.env)
++> ## rda
++> m <- rda(decostand(mite, "hell") ~ ., mite.env)
++> RsquareAdj(m)
++$r.squared
++[1] 0.5265047
++
++$adj.r.squared
++[1] 0.4367038
++
++> ## cca
++> m <- cca(decostand(mite, "hell") ~ ., mite.env)
++> RsquareAdj(m)
++$r.squared
++[1] 0.4471676
++
++$adj.r.squared
++[1] 0.34442
++
++> ## default method
++> RsquareAdj(0.8, 20, 5)
++[1] 0.7285714
++>
++>
++>
++> cleanEx()
++> nameEx("SSarrhenius")
++> ### * SSarrhenius
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: SSarrhenius
++> ### Title: Self-Starting nls Species-Area Models
++> ### Aliases: SSarrhenius SSlomolino SSgitay SSgleason
++> ### Keywords: models
++>
++> ### ** Examples
++>
++> ## Get species area data: sipoo.area gives the areas of islands
++> example(sipoo)
++
++sipoo> data(sipoo)
++
++sipoo> ## Areas of the islands in hectares
++sipoo> sipoo.area <- c(1.1, 2.1, 2.2, 3.1, 3.5, 5.8, 6, 6.1, 6.5, 11.4, 13,
++sipoo+ 14.5, 16.1 ,17.5, 28.7, 40.5, 104.5, 233)
++> S <- specnumber(sipoo)
++> plot(S ~ sipoo.area, xlab = "Island Area (ha)", ylab = "Number of Species",
+++ ylim = c(1, max(S)))
++> ## The Arrhenius model
++> marr <- nls(S ~ SSarrhenius(sipoo.area, k, z))
++> marr
++Nonlinear regression model
++ model: S ~ SSarrhenius(sipoo.area, k, z)
++ data: parent.frame()
++ k z
++3.4062 0.4364
++ residual sum-of-squares: 78.1
++
++Number of iterations to convergence: 5
++Achieved convergence tolerance: 1.056e-06
++> ## confidence limits from profile likelihood
++> confint(marr)
++Waiting for profiling to be done...
++ 2.5% 97.5%
++k 2.6220312 4.3033906
++z 0.3813576 0.4944693
++> ## draw a line
++> xtmp <- seq(min(sipoo.area), max(sipoo.area), len=51)
++> lines(xtmp, predict(marr, newdata=data.frame(sipoo.area = xtmp)), lwd=2)
++> ## The normal way is to use linear regression on log-log data,
++> ## but this will be different from the previous:
++> mloglog <- lm(log(S) ~ log(sipoo.area))
++> mloglog
++
++Call:
++lm(formula = log(S) ~ log(sipoo.area))
++
++Coefficients:
++ (Intercept) log(sipoo.area)
++ 1.0111 0.4925
++
++> lines(xtmp, exp(predict(mloglog, newdata=data.frame(sipoo.area=xtmp))),
+++ lty=2)
++> ## Gleason: log-linear
++> mgle <- nls(S ~ SSgleason(sipoo.area, k, slope))
++> lines(xtmp, predict(mgle, newdata=data.frame(sipoo.area=xtmp)),
+++ lwd=2, col=2)
++> ## Gitay: quadratic of log-linear
++> mgit <- nls(S ~ SSgitay(sipoo.area, k, slope))
++> lines(xtmp, predict(mgit, newdata=data.frame(sipoo.area=xtmp)),
+++ lwd=2, col = 3)
++> ## Lomolino: using original names of the parameters (Lomolino 2000):
++> mlom <- nls(S ~ SSlomolino(sipoo.area, Smax, A50, Hill))
++> mlom
++Nonlinear regression model
++ model: S ~ SSlomolino(sipoo.area, Smax, A50, Hill)
++ data: parent.frame()
++ Smax A50 Hill
++53.493 94.697 2.018
++ residual sum-of-squares: 55.37
++
++Number of iterations to convergence: 6
++Achieved convergence tolerance: 9.715e-07
++> lines(xtmp, predict(mlom, newdata=data.frame(sipoo.area=xtmp)),
+++ lwd=2, col = 4)
++> ## One canned model of standard R:
++> mmic <- nls(S ~ SSmicmen(sipoo.area, slope, Asym))
++> lines(xtmp, predict(mmic, newdata = data.frame(sipoo.area=xtmp)),
+++ lwd =2, col = 5)
++> legend("bottomright", c("Arrhenius", "log-log linear", "Gleason", "Gitay",
+++ "Lomolino", "Michaelis-Menten"), col=c(1,1,2,3,4,5), lwd=c(2,1,2,2,2,2),
+++ lty=c(1,2,1,1,1,1))
++> ## compare models (AIC)
++> allmods <- list(Arrhenius = marr, Gleason = mgle, Gitay = mgit,
+++ Lomolino = mlom, MicMen= mmic)
++> sapply(allmods, AIC)
++Arrhenius Gleason Gitay Lomolino MicMen
++ 83.49847 96.94018 80.54984 79.30718 83.02003
++>
++>
++>
++> cleanEx()
++> nameEx("add1.cca")
++> ### * add1.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: add1.cca
++> ### Title: Add or Drop Single Terms to a Constrained Ordination Model
++> ### Aliases: add1.cca drop1.cca
++> ### Keywords: multivariate models
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> ## Automatic model building based on AIC but with permutation tests
++> step(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), test="perm")
++Start: AIC=87.66
++dune ~ 1
++
++ Df AIC F Pr(>F)
+++ Moisture 3 86.608 2.2536 0.005 **
+++ Management 3 86.935 2.1307 0.005 **
+++ A1 1 87.411 2.1400 0.025 *
++<none> 87.657
+++ Manure 4 88.832 1.5251 0.045 *
+++ Use 2 89.134 1.1431 0.295
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: AIC=86.61
++dune ~ Moisture
++
++ Df AIC F Pr(>F)
++<none> 86.608
+++ Management 3 86.813 1.4565 0.045 *
+++ A1 1 86.992 1.2624 0.150
+++ Use 2 87.259 1.2760 0.120
+++ Manure 4 87.342 1.3143 0.040 *
++- Moisture 3 87.657 2.2536 0.010 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++Call: cca(formula = dune ~ Moisture, data = dune.env)
++
++ Inertia Proportion Rank
++Total 2.1153 1.0000
++Constrained 0.6283 0.2970 3
++Unconstrained 1.4870 0.7030 16
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3
++0.4187 0.1330 0.0766
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11
++0.4098 0.2259 0.1761 0.1234 0.1082 0.0908 0.0859 0.0609 0.0566 0.0467 0.0419
++ CA12 CA13 CA14 CA15 CA16
++0.0201 0.0143 0.0099 0.0085 0.0080
++
++> ## see ?ordistep to do the same, but based on permutation P-values
++> ## Not run:
++> ##D ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)),
perm.max=200)
++> ## End(Not run)
++> ## Manual model building
++> ## -- define the maximal model for scope
++> mbig <- rda(dune ~ ., dune.env)
++> ## -- define an empty model to start with
++> m0 <- rda(dune ~ 1, dune.env)
++> ## -- manual selection and updating
++> add1(m0, scope=formula(mbig), test="perm")
++ Df AIC F Pr(>F)
++<none> 89.620
++A1 1 89.591 1.9217 0.060 .
++Moisture 3 87.707 2.5883 0.005 **
++Management 3 87.082 2.8400 0.005 **
++Use 2 91.032 1.1741 0.280
++Manure 4 89.232 1.9539 0.015 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> m0 <- update(m0, . ~ . + Management)
++> add1(m0, scope=formula(mbig), test="perm")
++ Df AIC F Pr(>F)
++<none> 87.082
++A1 1 87.424 1.2965 0.175
++Moisture 3 85.567 1.9764 0.005 **
++Use 2 88.284 1.0510 0.440
++Manure 3 87.517 1.3902 0.095 .
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> m0 <- update(m0, . ~ . + Moisture)
++> ## -- included variables still significant?
++> drop1(m0, test="perm")
++ Df AIC F Pr(>F)
++<none> 85.567
++Management 3 87.707 2.1769 0.010 **
++Moisture 3 87.082 1.9764 0.005 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> add1(m0, scope=formula(mbig), test="perm")
++ Df AIC F Pr(>F)
++<none> 85.567
++A1 1 86.220 0.8359 0.645
++Use 2 86.842 0.8027 0.700
++Manure 3 85.762 1.1225 0.320
++>
++>
++>
++> cleanEx()
++> nameEx("adipart")
++> ### * adipart
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: adipart
++> ### Title: Additive Diversity Partitioning and Hierarchical Null Model
++> ### Testing
++> ### Aliases: adipart adipart.default adipart.formula hiersimu
++> ### hiersimu.default hiersimu.formula
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## NOTE: 'nsimul' argument usually needs to be >= 99
++> ## here much lower value is used for demonstration
++>
++> data(mite)
++> data(mite.xy)
++> data(mite.env)
++> ## Function to get equal area partitions of the mite data
++> cutter <- function (x, cut = seq(0, 10, by = 2.5)) {
+++ out <- rep(1, length(x))
+++ for (i in 2:(length(cut) - 1))
+++ out[which(x > cut[i] & x <= cut[(i + 1)])] <- i
+++ return(out)}
++> ## The hierarchy of sample aggregation
++> levsm <- with(mite.xy, data.frame(
+++ l1=1:nrow(mite),
+++ l2=cutter(y, cut = seq(0, 10, by = 2.5)),
+++ l3=cutter(y, cut = seq(0, 10, by = 5)),
+++ l4=cutter(y, cut = seq(0, 10, by = 10))))
++> ## Let's see in a map
++> par(mfrow=c(1,3))
++> plot(mite.xy, main="l1", col=as.numeric(levsm$l1)+1, asp = 1)
++> plot(mite.xy, main="l2", col=as.numeric(levsm$l2)+1, asp = 1)
++> plot(mite.xy, main="l3", col=as.numeric(levsm$l3)+1, asp = 1)
++> par(mfrow=c(1,1))
++> ## Additive diversity partitioning
++> adipart(mite, index="richness", nsimul=19)
++adipart object
++
++Call: adipart(y = mite, index = "richness", nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++options: index richness, weights unif
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++alpha.1 15.114 -38.43 22.344 22.032 22.300 22.608 0.05 *
++gamma 35.000 0.00 35.000 35.000 35.000 35.000 1.00
++beta.1 19.886 38.43 12.656 12.392 12.700 12.968 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> adipart(mite ~ ., levsm, index="richness", nsimul=19)
++adipart object
++
++Call: adipart(formula = mite ~ ., data = levsm, index = "richness",
++nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++options: index richness, weights unif
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++alpha.1 15.114 -46.2370 22.39624 22.12571 22.44286 22.6236 0.05 *
++alpha.2 29.750 -21.7076 34.81579 34.36250 35.00000 35.0000 0.05 *
++alpha.3 33.000 0.0000 35.00000 35.00000 35.00000 35.0000 0.05 *
++gamma 35.000 0.0000 35.00000 35.00000 35.00000 35.0000 1.00
++beta.1 14.636 9.0407 12.41955 12.00750 12.42857 12.8743 0.05 *
++beta.2 3.250 13.1373 0.18421 0.00000 0.00000 0.6375 0.05 *
++beta.3 2.000 0.0000 0.00000 0.00000 0.00000 0.0000 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Hierarchical null model testing
++> ## diversity analysis (similar to adipart)
++> hiersimu(mite, FUN=diversity, relative=TRUE, nsimul=19)
++hiersimu object
++
++Call: hiersimu(y = mite, FUN = diversity, relative = TRUE, nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++level_1 0.76064 -71.195 0.93904 0.93487 0.93856 0.9444 0.05 *
++leve_2 1.00000 0.000 1.00000 1.00000 1.00000 1.0000 1.00
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> hiersimu(mite ~., levsm, FUN=diversity, relative=TRUE, nsimul=19)
++hiersimu object
++
++Call: hiersimu(formula = mite ~ ., data = levsm, FUN = diversity,
++relative = TRUE, nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++l1 0.76064 -75.139 0.93833 0.93389 0.93819 0.9427 0.05 *
++l2 0.89736 -110.968 0.99811 0.99699 0.99814 0.9999 0.05 *
++l3 0.92791 -417.338 0.99940 0.99904 0.99943 0.9996 0.05 *
++l4 1.00000 0.000 1.00000 1.00000 1.00000 1.0000 1.00
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Hierarchical testing with the Morisita index
++> morfun <- function(x) dispindmorisita(x)$imst
++> hiersimu(mite ~., levsm, morfun, drop.highest=TRUE, nsimul=19)
++hiersimu object
++
++Call: hiersimu(formula = mite ~ ., data = levsm, FUN = morfun,
++drop.highest = TRUE, nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++l1 0.52070 8.5216 0.353253 0.322624 0.351073 0.3848 0.05 *
++l2 0.60234 14.3854 0.153047 0.096700 0.150434 0.1969 0.05 *
++l3 0.67509 20.3162 -0.182473 -0.234793 -0.195937 -0.0988 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("adonis")
++> ### * adonis
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: adonis
++> ### Title: Permutational Multivariate Analysis of Variance Using Distance
++> ### Matrices
++> ### Aliases: adonis adonis2
++> ### Keywords: multivariate nonparametric
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> ## default test by terms
++> adonis2(dune ~ Management*A1, data = dune.env)
++Permutation test for adonis under reduced model
++Terms added sequentially (first to last)
++Permutation: free
++Number of permutations: 999
++
++adonis2(formula = dune ~ Management * A1, data = dune.env)
++ Df SumOfSqs F Pr(>F)
++Management 3 1.46859 3.2629 0.002 **
++A1 1 0.44089 2.9387 0.022 *
++Management:A1 3 0.58918 1.3090 0.191
++Residual 12 1.80036
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## overall tests
++> adonis2(dune ~ Management*A1, data = dune.env, by = NULL)
++Permutation test for adonis under reduced model
++Permutation: free
++Number of permutations: 999
++
++adonis2(formula = dune ~ Management * A1, data = dune.env, by = NULL)
++ Df SumOfSqs F Pr(>F)
++Model 7 2.4987 2.3792 0.003 **
++Residual 12 1.8004
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> ### Example of use with strata, for nested (e.g., block) designs.
++> dat <- expand.grid(rep=gl(2,1), NO3=factor(c(0,10)),field=gl(3,1) )
++> dat
++ rep NO3 field
++1 1 0 1
++2 2 0 1
++3 1 10 1
++4 2 10 1
++5 1 0 2
++6 2 0 2
++7 1 10 2
++8 2 10 2
++9 1 0 3
++10 2 0 3
++11 1 10 3
++12 2 10 3
++> Agropyron <- with(dat, as.numeric(field) + as.numeric(NO3)+2) +rnorm(12)/2
++> Schizachyrium <- with(dat, as.numeric(field) - as.numeric(NO3)+2)
+rnorm(12)/2
++> total <- Agropyron + Schizachyrium
++> dotplot(total ~ NO3, dat, jitter.x=TRUE, groups=field,
+++ type=c('p','a'), xlab="NO3", auto.key=list(columns=3, lines=TRUE) )
++>
++> Y <- data.frame(Agropyron, Schizachyrium)
++> mod <- metaMDS(Y)
++Run 0 stress 0.06065252
++Run 1 stress 0.06065252
++... New best solution
++... Procrustes: rmse 3.164024e-06 max resid 6.043669e-06
++... Similar to previous best
++Run 2 stress 0.06065252
++... Procrustes: rmse 1.938469e-06 max resid 3.61423e-06
++... Similar to previous best
++Run 3 stress 0.142919
++Run 4 stress 0.1670077
++Run 5 stress 0.175151
++Run 6 stress 0.1751507
++Run 7 stress 0.06065252
++... Procrustes: rmse 3.943561e-06 max resid 7.519499e-06
++... Similar to previous best
++Run 8 stress 0.06065252
++... New best solution
++... Procrustes: rmse 2.172863e-06 max resid 4.032152e-06
++... Similar to previous best
++Run 9 stress 0.1820814
++Run 10 stress 0.06065252
++... New best solution
++... Procrustes: rmse 1.026253e-06 max resid 1.4719e-06
++... Similar to previous best
++Run 11 stress 0.06065252
++... New best solution
++... Procrustes: rmse 1.828471e-06 max resid 3.763468e-06
++... Similar to previous best
++Run 12 stress 0.06065252
++... Procrustes: rmse 3.138136e-06 max resid 6.223657e-06
++... Similar to previous best
++Run 13 stress 0.06065252
++... Procrustes: rmse 3.415889e-06 max resid 6.880638e-06
++... Similar to previous best
++Run 14 stress 0.06065252
++... Procrustes: rmse 3.066271e-06 max resid 5.908388e-06
++... Similar to previous best
++Run 15 stress 0.06065252
++... Procrustes: rmse 1.945053e-06 max resid 3.557283e-06
++... Similar to previous best
++Run 16 stress 0.06065252
++... Procrustes: rmse 5.441366e-06 max resid 9.82099e-06
++... Similar to previous best
++Run 17 stress 0.1820814
++Run 18 stress 0.1659224
++Run 19 stress 0.1820814
++Run 20 stress 0.2291418
++*** Solution reached
++> plot(mod)
++> ### Ellipsoid hulls show treatment
++> with(dat, ordiellipse(mod, field, kind = "ehull", label = TRUE))
++> ### Spider shows fields
++> with(dat, ordispider(mod, field, lty=3, col="red"))
++>
++> ### Incorrect (no strata)
++> perm <- how(nperm = 199)
++> adonis2 (Y ~ NO3, data = dat, permutations = perm)
++Permutation test for adonis under reduced model
++Terms added sequentially (first to last)
++Permutation: free
++Number of permutations: 199
++
++adonis2(formula = Y ~ NO3, data = dat, permutations = perm)
++ Df SumOfSqs F Pr(>F)
++NO3 1 0.036334 3.5509 0.04 *
++Residual 10 0.102324
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> ## Correct with strata
++> setBlocks(perm) <- with(dat, field)
++> adonis2(Y ~ NO3, data = dat, permutations = perm)
++Permutation test for adonis under reduced model
++Terms added sequentially (first to last)
++Blocks: with(dat, field)
++Permutation: free
++Number of permutations: 199
++
++adonis2(formula = Y ~ NO3, data = dat, permutations = perm)
++ Df SumOfSqs F Pr(>F)
++NO3 1 0.036334 3.5509 0.02 *
++Residual 10 0.102324
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("anosim")
++> ### * anosim
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: anosim
++> ### Title: Analysis of Similarities
++> ### Aliases: anosim summary.anosim plot.anosim
++> ### Keywords: multivariate nonparametric htest
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> dune.dist <- vegdist(dune)
++> attach(dune.env)
++> dune.ano <- anosim(dune.dist, Management)
++> summary(dune.ano)
++
++Call:
++anosim(dat = dune.dist, grouping = Management)
++Dissimilarity: bray
++
++ANOSIM statistic R: 0.2579
++ Significance: 0.017
++
++Permutation: free
++Number of permutations: 999
++
++Upper quantiles of permutations (null model):
++ 90% 95% 97.5% 99%
++0.121 0.174 0.222 0.276
++
++Dissimilarity ranks between and within classes:
++ 0% 25% 50% 75% 100% N
++Between 4 58.50 104.00 145.500 188.0 147
++BF 5 15.25 25.50 41.250 57.0 3
++HF 1 7.25 46.25 68.125 89.5 10
++NM 6 64.75 124.50 156.250 181.0 15
++SF 3 32.75 53.50 99.250 184.0 15
++
++> plot(dune.ano)
++Warning in bxp(list(stats = c(4, 58.5, 104, 145.5, 188, 5, 15.25, 25.5, :
++ some notches went outside hinges ('box'): maybe set notch=FALSE
++>
++>
++>
++> cleanEx()
++
++detaching ‘dune.env’
++
++> nameEx("anova.cca")
++> ### * anova.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: anova.cca
++> ### Title: Permutation Test for Constrained Correspondence Analysis,
++> ### Redundancy Analysis and Constrained Analysis of Principal Coordinates
++> ### Aliases: anova.cca permutest permutest.cca
++> ### Keywords: multivariate htest
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> vare.cca <- cca(varespec ~ Al + P + K, varechem)
++> ## overall test
++> anova(vare.cca)
++Permutation test for cca under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: cca(formula = varespec ~ Al + P + K, data = varechem)
++ Df ChiSquare F Pr(>F)
++Model 3 0.64413 2.984 0.001 ***
++Residual 20 1.43906
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("as.mlm")
++> ### * as.mlm
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: as.mlm.cca
++> ### Title: Refit Constrained Ordination as a Multiple Response Linear Model
++> ### Aliases: as.mlm as.mlm.cca as.mlm.rda
++> ### Keywords: models multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> mod <- cca(varespec ~ Al + P + K, data=varechem)
++> lmod <- as.mlm(mod)
++> ## Coefficients
++> lmod
++
++Call:
++lm(formula = wa ~ . - 1, data = as.data.frame(X))
++
++Coefficients:
++ CCA1 CCA2 CCA3
++Al 0.007479 -0.001884 0.003381
++P -0.006491 -0.102190 -0.022307
++K -0.006756 0.015344 0.017067
++
++> coef(mod)
++ CCA1 CCA2 CCA3
++Al 0.007478556 -0.001883637 0.003380774
++P -0.006491081 -0.102189737 -0.022306682
++K -0.006755568 0.015343662 0.017067351
++> ## Influential observations
++> influence.measures(lmod)
++Influence measures of
++ lm(formula = wa ~ . - 1, data = as.data.frame(X)) :
++
++ dfb.Al dfb.P dfb.K CCA1 CCA2 CCA3 cov.r CCA1.1
++18 -0.251387 0.00976 -0.06310 0.27401 0.1806 -0.118754 0.0265 7.38e-03
++15 0.099858 0.13864 -0.11781 -0.16542 -0.0935 0.006898 0.0319 2.86e-03
++24 -0.003448 -0.44078 0.20788 -0.48239 -0.1750 -0.260788 0.0307 2.33e-02
++27 0.071682 -0.01707 -0.03516 -0.10185 -0.1676 0.022271 0.0406 1.13e-03
++23 -0.116533 0.06900 -0.02545 0.14407 0.2918 -0.220457 0.0355 2.23e-03
++19 -0.007394 -0.01169 0.01080 0.01360 -0.2318 -0.000417 0.0359 2.02e-05
++22 0.150916 0.14845 -0.13091 -0.20466 0.3815 0.168914 0.0376 4.50e-03
++16 0.107456 0.17900 -0.09917 -0.21196 0.2250 0.194432 0.0338 4.75e-03
++28 0.332161 -0.34398 -0.05414 -0.67745 0.0742 0.620990 0.0364 4.65e-02
++13 0.366880 -1.00834 1.23685 1.33919 0.4102 0.277067 0.1124 1.89e-01
++14 0.024147 0.02512 -0.01161 -0.03608 0.1491 0.053638 0.0355 1.42e-04
++20 0.000747 -0.00560 0.00205 -0.00661 0.2935 -0.190351 0.0368 4.77e-06
++25 0.166736 -0.11049 0.09341 -0.20954 -0.1627 -0.070753 0.0346 4.66e-03
++7 -0.397145 0.15747 0.15662 -0.59116 0.5842 -0.838287 0.0327 3.51e-02
++5 -0.279996 -0.09119 -0.35616 0.73579 0.3694 -0.326563 0.0281 5.18e-02
++6 0.003191 -0.00168 -0.01550 0.02590 0.3447 0.201072 0.0400 7.34e-05
++3 -0.302851 -0.07889 0.25932 -0.41958 -0.2766 0.536017 0.0386 1.85e-02
++4 -0.058151 -0.02719 0.00870 -0.06644 0.8199 0.131003 0.0486 4.83e-04
++2 0.020380 0.00416 -0.00373 0.02055 -0.4158 -0.160401 0.0395 4.62e-05
++9 0.074217 0.09551 -0.10857 0.12712 -0.3481 0.644579 0.0383 1.75e-03
++12 -0.097825 -0.20830 0.04637 0.28644 -0.6601 0.270324 0.0280 8.19e-03
++10 0.149178 0.66594 -0.12975 0.89348 -0.2510 0.000571 0.0118 5.90e-02
++11 0.014687 0.00691 0.00105 0.01913 0.1838 -0.301086 0.0377 4.00e-05
++21 0.148213 0.15461 -0.02915 -0.25306 -0.1892 -0.318491 0.0361 6.81e-03
++ CCA2.1 CCA3.1 hat inf
++18 0.003207 1.39e-03 0.0321
++15 0.000915 4.98e-06 0.0295
++24 0.003071 6.82e-03 0.1135
++27 0.003062 5.41e-05 0.1375
++23 0.009151 5.22e-03 0.0555
++19 0.005873 1.90e-08 0.0176
++22 0.015648 3.07e-03 0.1077
++16 0.005348 3.99e-03 0.0594
++28 0.000559 3.91e-02 0.2256
++13 0.017773 8.11e-03 0.7168 *
++14 0.002424 3.13e-04 0.0158
++20 0.009421 3.96e-03 0.0393
++25 0.002810 5.31e-04 0.0670
++7 0.034274 7.06e-02 0.1635
++5 0.013057 1.02e-02 0.1588
++6 0.012996 4.42e-03 0.1169
++3 0.008056 3.02e-02 0.1833
++4 0.073491 1.88e-03 0.2741
++2 0.018909 2.81e-03 0.1063
++9 0.013158 4.51e-02 0.0978
++12 0.043487 7.29e-03 0.0409
++10 0.004658 2.41e-08 0.0768
++11 0.003694 9.91e-03 0.0632
++21 0.003807 1.08e-02 0.1009
++> plot(mod, type = "n")
++> points(mod, cex = 10*hatvalues(lmod), pch=16, xpd = TRUE)
++> text(mod, display = "bp", col = "blue")
++>
++>
++>
++> cleanEx()
++> nameEx("beals")
++> ### * beals
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: beals
++> ### Title: Beals Smoothing and Degree of Absence
++> ### Aliases: beals swan
++> ### Keywords: manip smooth
++>
++> ### ** Examples
++>
++> data(dune)
++> ## Default
++> x <- beals(dune)
++> ## Remove target species
++> x <- beals(dune, include = FALSE)
++> ## Smoothed values against presence or absence of species
++> pa <- decostand(dune, "pa")
++> boxplot(as.vector(x) ~ unlist(pa), xlab="Presence", ylab="Beals")
++> ## Remove the bias of tarbet species: Yields lower values.
++> beals(dune, type =3, include = FALSE)
++ Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord
++1 0.49590853 0.38333415 0.01157407 0.4923280 0.30827883 0.4935662 0.43263047
++2 0.47083676 0.39501120 0.03361524 0.4718807 0.34723984 0.4917791 0.42000984
++3 0.34063019 0.52738394 0.01520046 0.5309152 0.21609954 0.4033301 0.33010938
++4 0.30816435 0.51198853 0.02876960 0.5971801 0.21542662 0.4398775 0.35732610
++5 0.59949785 0.27622698 0.06632771 0.3349203 0.48876285 0.4322142 0.44309579
++6 0.58819821 0.26299306 0.05967771 0.2700508 0.53154426 0.3696613 0.39760652
++7 0.56496165 0.29412293 0.05329633 0.3403047 0.48010987 0.4051777 0.40471531
++8 0.21230502 0.66906674 0.02588333 0.5187956 0.16247716 0.2720122 0.21219877
++9 0.30323659 0.59744543 0.02213662 0.5792855 0.21896113 0.3292320 0.28613526
++10 0.54083871 0.26902092 0.07349127 0.3372958 0.42671693 0.4705094 0.42934344
++11 0.40509331 0.31656550 0.10259239 0.3185489 0.38766111 0.3713794 0.31413659
++12 0.21008725 0.66278454 0.03625297 0.5753377 0.20078932 0.2802946 0.22974415
++13 0.21850759 0.68239707 0.02191119 0.6404427 0.16737280 0.2939740 0.24942466
++14 0.13570397 0.76284476 0.02298398 0.4107645 0.12128973 0.1682755 0.13757552
++15 0.09168815 0.79412733 0.02538032 0.4505613 0.10117099 0.1420251 0.09794548
++16 0.06335463 0.87877202 0.00742115 0.5232448 0.05538377 0.1516354 0.09458531
++17 0.55254140 0.07330247 0.29233391 0.1013889 0.69331132 0.3129358 0.34982363
++18 0.37751017 0.34451209 0.08535723 0.2838834 0.36918166 0.3676424 0.30478244
++19 0.29826049 0.25952255 0.35137675 0.1934048 0.51929869 0.2237843 0.18074796
++20 0.05429986 0.76675441 0.06144615 0.4063662 0.10738280 0.1450721 0.06706410
++ Chenalbu Cirsarve Comapalu Eleopalu Elymrepe Empenigr
++1 0.025132275 0.09504980 0.000000000 0.05592045 0.4667439 0.00000000
++2 0.043866562 0.08570299 0.026548839 0.08656209 0.4407282 0.01829337
++3 0.065338638 0.08967477 0.031898812 0.16099072 0.4137888 0.01074444
++4 0.057970906 0.12920228 0.039859621 0.16112450 0.4399661 0.02527165
++5 0.026434737 0.05520104 0.015892090 0.05419613 0.3575948 0.03029752
++6 0.021256367 0.03223112 0.030347896 0.08784329 0.3138879 0.03093489
++7 0.038467708 0.04706743 0.017083997 0.06694311 0.3586644 0.02304603
++8 0.063278453 0.06688407 0.100703044 0.29777644 0.3046956 0.02102222
++9 0.069879277 0.07647268 0.045830682 0.19018562 0.3523460 0.01838883
++10 0.025686639 0.06037513 0.029746617 0.07787078 0.3736128 0.03425596
++11 0.021234732 0.05778318 0.035740922 0.11146095 0.2884798 0.07310076
++12 0.103543341 0.07799259 0.045375827 0.19518888 0.3354080 0.03413656
++13 0.122547745 0.07905124 0.056084315 0.22437598 0.3511708 0.01840390
++14 0.042990591 0.03618335 0.241811837 0.55982776 0.1428372 0.01989756
++15 0.035609053 0.04022968 0.198176675 0.53973883 0.1462975 0.02215971
++16 0.056246994 0.05184498 0.201352298 0.51523810 0.1832397 0.00742115
++17 0.007716049 0.01049383 0.009876543 0.02777778 0.1929470 0.21968254
++18 0.014640428 0.04454602 0.042890320 0.17341352 0.2651538 0.06763669
++19 0.019591245 0.03668466 0.031845637 0.12592768 0.1422725 0.26011417
++20 0.037623741 0.03453783 0.185726965 0.58476297 0.1168700 0.05905666
++ Hyporadi Juncarti Juncbufo Lolipere Planlanc Poaprat Poatriv
++1 0.07702746 0.14794933 0.1987270 0.9226190 0.40103107 0.9863946 0.8826329
++2 0.07454127 0.13017869 0.2070478 0.8272395 0.40700777 0.8972046 0.8288385
++3 0.05562332 0.22291082 0.2544828 0.7205525 0.27933493 0.8083020 0.8383185
++4 0.06985986 0.21320122 0.2318440 0.7197924 0.25797285 0.7940926 0.8197302
++5 0.10245961 0.10406655 0.2164230 0.8380779 0.52628928 0.9035899 0.8094632
++6 0.11463153 0.11631772 0.2166255 0.8000021 0.58765018 0.8666677 0.7782619
++7 0.10837376 0.11293676 0.2110045 0.8053380 0.51905808 0.8925059 0.8018775
++8 0.06550319 0.33219882 0.2323566 0.5403355 0.20596764 0.6160461 0.7101299
++9 0.05343787 0.23134366 0.2675624 0.6874068 0.25274756 0.7523318 0.8247374
++10 0.13692492 0.09080902 0.1678040 0.8102783 0.52588347 0.8915882 0.7543592
++11 0.18108995 0.13478872 0.1656396 0.7180948 0.47012501 0.8062720 0.6404351
++12 0.06777311 0.27306206 0.3231724 0.5875943 0.22110550 0.6932541 0.8199960
++13 0.04250245 0.28204736 0.3339728 0.5714581 0.18153869 0.7063028 0.7993754
++14 0.04665747 0.46685537 0.1206518 0.3356311 0.14342002 0.3817081 0.5090703
++15 0.05040404 0.51561767 0.1370235 0.3689922 0.13523214 0.4078219 0.5263520
++16 0.01731602 0.54304667 0.1776781 0.3561752 0.07269979 0.4124222 0.6071083
++17 0.36492870 0.03333333 0.1038156 0.5858415 0.59641331 0.7434618 0.5036834
++18 0.17491099 0.18956922 0.1376386 0.7124388 0.45087176 0.7368632 0.5859071
++19 0.39145281 0.13543701 0.1127832 0.4289185 0.40784415 0.5548077 0.3605827
++20 0.07795311 0.53056145 0.1192488 0.3262685 0.13059496 0.3662817 0.4523029
++ Ranuflam Rumeacet Sagiproc Salirepe Scorautu Trifprat Trifrepe
++1 0.08105273 0.3160963 0.3371121 0.02729885 0.8898317 0.21701279 0.8782576
++2 0.13042865 0.3031318 0.3302063 0.05983781 0.9349640 0.20673650 0.9125666
++3 0.22632936 0.2909068 0.4204104 0.06065155 0.9036443 0.14654749 0.8817430
++4 0.21909541 0.2610006 0.4191908 0.07579199 0.9204237 0.12896524 0.8943213
++5 0.08063087 0.3979230 0.2612828 0.07589611 0.9576838 0.34808957 0.9142360
++6 0.10909966 0.4330705 0.2539380 0.08921540 0.9590466 0.35423465 0.9110822
++7 0.10541081 0.4113622 0.2954682 0.07094548 0.9550487 0.32489503 0.9171688
++8 0.40134447 0.2331043 0.4009544 0.11569906 0.8755515 0.09897600 0.8002526
++9 0.26006489 0.3464870 0.4531178 0.07351827 0.9145996 0.16269563 0.8714833
++10 0.10355742 0.3226025 0.2732735 0.09037489 0.9568824 0.26807372 0.9003730
++11 0.13269569 0.2753878 0.3673397 0.16465286 0.9442707 0.19982976 0.8979262
++12 0.29873222 0.3507140 0.5122033 0.08041977 0.9377963 0.13849854 0.9079979
++13 0.33309468 0.3107471 0.5131337 0.06572594 0.9255312 0.11199114 0.8841739
++14 0.64674225 0.1241545 0.2528665 0.15917563 0.8477706 0.06176123 0.6485949
++15 0.64449081 0.1459458 0.3151199 0.17750323 0.8430677 0.05831084 0.7170446
++16 0.66893881 0.1508409 0.3480368 0.15783292 0.8131968 0.03916718 0.6776273
++17 0.03549383 0.2913631 0.3292030 0.25651777 0.9839744 0.27593101 0.8141660
++18 0.18805395 0.2668100 0.3154533 0.17191937 0.9554011 0.20461193 0.8600701
++19 0.14551892 0.1831168 0.4798245 0.36429493 0.9902041 0.11966159 0.8147968
++20 0.62796060 0.1098600 0.3105433 0.21674174 0.8457313 0.03708580 0.6350394
++ Vicilath Bracruta Callcusp
++1 0.17244420 0.7476589 0.003527337
++2 0.18494940 0.7415172 0.034597921
++3 0.12833142 0.7666969 0.075789630
++4 0.12550967 0.7919786 0.081110164
++5 0.16693075 0.8079786 0.023129027
++6 0.18035860 0.8387650 0.040981168
++7 0.19027523 0.8089116 0.024070054
++8 0.10213052 0.8109194 0.201958942
++9 0.08630413 0.7972178 0.092982775
++10 0.23383453 0.7660374 0.033527777
++11 0.24317802 0.8182692 0.043950322
++12 0.08049055 0.8061715 0.100954127
++13 0.06604026 0.7465509 0.122856392
++14 0.07857237 0.7238162 0.347514804
++15 0.07370069 0.7997141 0.381379395
++16 0.03353260 0.8029953 0.364128496
++17 0.20728700 0.7635487 0.009876543
++18 0.26222869 0.8397471 0.109959916
++19 0.18188455 0.8161275 0.082361157
++20 0.09111967 0.8397124 0.397924041
++> ## Uses abundance information.
++> ## Vector with beals smoothing values corresponding to the first species
++> ## in dune.
++> beals(dune, species=1, include=TRUE)
++ 1 2 3 4 5 6 7
8
++0.5923077 0.5032372 0.3499038 0.3306953 0.5944041 0.5928780 0.5824352
0.2082532
++ 9 10 11 12 13 14 15
16
++0.2960799 0.5462492 0.3659392 0.2610043 0.1982372 0.0922619 0.1140625
0.1066506
++ 17 18 19 20
++0.6020408 0.3844577 0.2865741 0.0750000
++>
++>
++>
++> cleanEx()
++> nameEx("betadisper")
++> ### * betadisper
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: betadisper
++> ### Title: Multivariate homogeneity of groups dispersions (variances)
++> ### Aliases: betadisper scores.betadisper anova.betadisper plot.betadisper
++> ### boxplot.betadisper TukeyHSD.betadisper eigenvals.betadisper
++> ### ordimedian
++> ### Keywords: methods multivariate hplot
++>
++> ### ** Examples
++>
++> data(varespec)
++>
++> ## Bray-Curtis distances between samples
++> dis <- vegdist(varespec)
++>
++> ## First 16 sites grazed, remaining 8 sites ungrazed
++> groups <- factor(c(rep(1,16), rep(2,8)), labels = c("grazed","ungrazed"))
++>
++> ## Calculate multivariate dispersions
++> mod <- betadisper(dis, groups)
++> mod
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = groups)
++
++No. of Positive Eigenvalues: 15
++No. of Negative Eigenvalues: 8
++
++Average distance to median:
++ grazed ungrazed
++ 0.3926 0.2706
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.7552 1.1334 0.4429 0.3698 0.2454 0.1961 0.1751 0.1284
++>
++> ## Perform test
++> anova(mod)
++Analysis of Variance Table
++
++Response: Distances
++ Df Sum Sq Mean Sq F value Pr(>F)
++Groups 1 0.07931 0.079306 4.6156 0.04295 *
++Residuals 22 0.37801 0.017182
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> ## Permutation test for F
++> permutest(mod, pairwise = TRUE, permutations = 99)
++
++Permutation test for homogeneity of multivariate dispersions
++Permutation: free
++Number of permutations: 99
++
++Response: Distances
++ Df Sum Sq Mean Sq F N.Perm Pr(>F)
++Groups 1 0.07931 0.079306 4.6156 99 0.04 *
++Residuals 22 0.37801 0.017182
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Pairwise comparisons:
++(Observed p-value below diagonal, permuted p-value above diagonal)
++ grazed ungrazed
++grazed 0.05
++ungrazed 0.04295
++>
++> ## Tukey's Honest Significant Differences
++> (mod.HSD <- TukeyHSD(mod))
++ Tukey multiple comparisons of means
++ 95% family-wise confidence level
++
++Fit: aov(formula = distances ~ group, data = df)
++
++$group
++ diff lwr upr p adj
++ungrazed-grazed -0.1219422 -0.2396552 -0.004229243 0.0429502
++
++> plot(mod.HSD)
++>
++> ## Plot the groups and distances to centroids on the
++> ## first two PCoA axes
++> plot(mod)
++>
++> ## with data ellipses instead of hulls
++> plot(mod, ellipse = TRUE, hull = FALSE) # 1 sd data ellipse
++> plot(mod, ellipse = TRUE, hull = FALSE, conf = 0.90) # 90% data ellipse
++>
++> ## can also specify which axes to plot, ordering respected
++> plot(mod, axes = c(3,1), seg.col = "forestgreen", seg.lty = "dashed")
++>
++> ## Draw a boxplot of the distances to centroid for each group
++> boxplot(mod)
++>
++> ## `scores` and `eigenvals` also work
++> scrs <- scores(mod)
++> str(scrs)
++List of 2
++ $ sites : num [1:24, 1:2] 0.0946 -0.3125 -0.3511 -0.3291 -0.1926 ...
++ ..- attr(*, "dimnames")=List of 2
++ .. ..$ : chr [1:24] "18" "15" "24" "27" ...
++ .. ..$ : chr [1:2] "PCoA1" "PCoA2"
++ $ centroids: num [1:2, 1:2] -0.1455 0.2786 0.0758 -0.2111
++ ..- attr(*, "dimnames")=List of 2
++ .. ..$ : chr [1:2] "grazed" "ungrazed"
++ .. ..$ : chr [1:2] "PCoA1" "PCoA2"
++> head(scores(mod, 1:4, display = "sites"))
++ PCoA1 PCoA2 PCoA3 PCoA4
++18 0.09459373 0.15914576 0.074400844 -0.202466025
++15 -0.31248809 0.10032751 -0.062243360 0.110844864
++24 -0.35106507 -0.05954096 -0.038079447 0.095060928
++27 -0.32914546 -0.17019348 0.231623720 0.019110623
++23 -0.19259443 -0.01459250 -0.005679372 -0.209718312
++19 -0.06794575 -0.14501690 -0.085645653 0.002431355
++> # group centroids/medians
++> scores(mod, 1:4, display = "centroids")
++ PCoA1 PCoA2 PCoA3 PCoA4
++grazed -0.1455200 0.07584572 -0.01366220 -0.0178990
++ungrazed 0.2786095 -0.21114993 -0.03475586 0.0220129
++> # eigenvalues from the underlying principal coordinates analysis
++> eigenvals(mod)
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7
++ 1.7552165 1.1334455 0.4429018 0.3698054 0.2453532 0.1960921 0.1751131
++ PCoA8 PCoA9 PCoA10 PCoA11 PCoA12 PCoA13 PCoA14
++ 0.1284467 0.0971594 0.0759601 0.0637178 0.0583225 0.0394934 0.0172699
++ PCoA15 PCoA16 PCoA17 PCoA18 PCoA19 PCoA20 PCoA21
++ 0.0051011 -0.0004131 -0.0064654 -0.0133147 -0.0253944 -0.0375105 -0.0480069
++ PCoA22 PCoA23
++-0.0537146 -0.0741390
++>
++> ## try out bias correction; compare with mod3
++> (mod3B <- betadisper(dis, groups, type = "median", bias.adjust=TRUE))
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = groups, type = "median", bias.adjust
++= TRUE)
++
++No. of Positive Eigenvalues: 15
++No. of Negative Eigenvalues: 8
++
++Average distance to median:
++ grazed ungrazed
++ 0.4055 0.2893
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.7552 1.1334 0.4429 0.3698 0.2454 0.1961 0.1751 0.1284
++>
++> ## should always work for a single group
++> group <- factor(rep("grazed", NROW(varespec)))
++> (tmp <- betadisper(dis, group, type = "median"))
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = group, type = "median")
++
++No. of Positive Eigenvalues: 15
++No. of Negative Eigenvalues: 8
++
++Average distance to median:
++grazed
++0.4255
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.7552 1.1334 0.4429 0.3698 0.2454 0.1961 0.1751 0.1284
++> (tmp <- betadisper(dis, group, type = "centroid"))
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = group, type = "centroid")
++
++No. of Positive Eigenvalues: 15
++No. of Negative Eigenvalues: 8
++
++Average distance to centroid:
++grazed
++0.4261
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.7552 1.1334 0.4429 0.3698 0.2454 0.1961 0.1751 0.1284
++>
++> ## simulate missing values in 'd' and 'group'
++> ## using spatial medians
++> groups[c(2,20)] <- NA
++> dis[c(2, 20)] <- NA
++> mod2 <- betadisper(dis, groups) ## warnings
++Warning in betadisper(dis, groups) :
++ missing observations due to 'group' removed
++Warning in betadisper(dis, groups) :
++ missing observations due to 'd' removed
++> mod2
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = groups)
++
++No. of Positive Eigenvalues: 14
++No. of Negative Eigenvalues: 5
++
++Average distance to median:
++ grazed ungrazed
++ 0.3984 0.3008
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.4755 0.8245 0.4218 0.3456 0.2159 0.1688 0.1150 0.1060
++> permutest(mod2, permutations = 99)
++
++Permutation test for homogeneity of multivariate dispersions
++Permutation: free
++Number of permutations: 99
++
++Response: Distances
++ Df Sum Sq Mean Sq F N.Perm Pr(>F)
++Groups 1 0.039979 0.039979 2.4237 99 0.19
++Residuals 18 0.296910 0.016495
++> anova(mod2)
++Analysis of Variance Table
++
++Response: Distances
++ Df Sum Sq Mean Sq F value Pr(>F)
++Groups 1 0.039979 0.039979 2.4237 0.1369
++Residuals 18 0.296910 0.016495
++> plot(mod2)
++> boxplot(mod2)
++> plot(TukeyHSD(mod2))
++>
++> ## Using group centroids
++> mod3 <- betadisper(dis, groups, type = "centroid")
++Warning in betadisper(dis, groups, type = "centroid") :
++ missing observations due to 'group' removed
++Warning in betadisper(dis, groups, type = "centroid") :
++ missing observations due to 'd' removed
++> mod3
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = groups, type = "centroid")
++
++No. of Positive Eigenvalues: 14
++No. of Negative Eigenvalues: 5
++
++Average distance to centroid:
++ grazed ungrazed
++ 0.4001 0.3108
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.4755 0.8245 0.4218 0.3456 0.2159 0.1688 0.1150 0.1060
++> permutest(mod3, permutations = 99)
++
++Permutation test for homogeneity of multivariate dispersions
++Permutation: free
++Number of permutations: 99
++
++Response: Distances
++ Df Sum Sq Mean Sq F N.Perm Pr(>F)
++Groups 1 0.033468 0.033468 3.1749 99 0.1
++Residuals 18 0.189749 0.010542
++> anova(mod3)
++Analysis of Variance Table
++
++Response: Distances
++ Df Sum Sq Mean Sq F value Pr(>F)
++Groups 1 0.033468 0.033468 3.1749 0.09166 .
++Residuals 18 0.189749 0.010542
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> plot(mod3)
++> boxplot(mod3)
++> plot(TukeyHSD(mod3))
++>
++>
++>
++>
++> cleanEx()
++> nameEx("betadiver")
++> ### * betadiver
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: betadiver
++> ### Title: Indices of beta Diversity
++> ### Aliases: betadiver scores.betadiver plot.betadiver
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## Raw data and plotting
++> data(sipoo)
++> m <- betadiver(sipoo)
++> plot(m)
++> ## The indices
++> betadiver(help=TRUE)
++1 "w" = (b+c)/(2*a+b+c)
++2 "-1" = (b+c)/(2*a+b+c)
++3 "c" = (b+c)/2
++4 "wb" = b+c
++5 "r" = 2*b*c/((a+b+c)^2-2*b*c)
++6 "I" = log(2*a+b+c) - 2*a*log(2)/(2*a+b+c) - ((a+b)*log(a+b) +
++(a+c)*log(a+c)) / (2*a+b+c)
++7 "e" = exp(log(2*a+b+c) - 2*a*log(2)/(2*a+b+c) - ((a+b)*log(a+b) +
++(a+c)*log(a+c)) / (2*a+b+c))-1
++8 "t" = (b+c)/(2*a+b+c)
++9 "me" = (b+c)/(2*a+b+c)
++10 "j" = a/(a+b+c)
++11 "sor" = 2*a/(2*a+b+c)
++12 "m" = (2*a+b+c)*(b+c)/(a+b+c)
++13 "-2" = pmin(b,c)/(pmax(b,c)+a)
++14 "co" = (a*c+a*b+2*b*c)/(2*(a+b)*(a+c))
++15 "cc" = (b+c)/(a+b+c)
++16 "g" = (b+c)/(a+b+c)
++17 "-3" = pmin(b,c)/(a+b+c)
++18 "l" = (b+c)/2
++19 "19" = 2*(b*c+1)/(a+b+c)/(a+b+c-1)
++20 "hk" = (b+c)/(2*a+b+c)
++21 "rlb" = a/(a+c)
++22 "sim" = pmin(b,c)/(pmin(b,c)+a)
++23 "gl" = 2*abs(b-c)/(2*a+b+c)
++24 "z" = (log(2)-log(2*a+b+c)+log(a+b+c))/log(2)
++> ## The basic Whittaker index
++> d <- betadiver(sipoo, "w")
++> ## This should be equal to Sorensen index (binary Bray-Curtis in
++> ## vegan)
++> range(d - vegdist(sipoo, binary=TRUE))
++[1] 0 0
++>
++>
++>
++> cleanEx()
++> nameEx("bgdispersal")
++> ### * bgdispersal
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: bgdispersal
++> ### Title: Coefficients of Biogeographical Dispersal Direction
++> ### Aliases: bgdispersal
++> ### Keywords: multivariate nonparametric
++>
++> ### ** Examples
++>
++> mat <- matrix(c(32,15,14,10,70,30,100,4,10,30,25,0,18,0,40,
+++ 0,0,20,0,0,0,0,4,0,30,20,0,0,0,0,25,74,42,1,45,89,5,16,16,20),
+++ 4, 10, byrow=TRUE)
++> bgdispersal(mat)
++$DD1
++ [,1] [,2] [,3] [,4]
++[1,] 0.00 0.24 0.21 0.00
++[2,] -0.24 0.00 0.08 -0.24
++[3,] -0.21 -0.08 0.00 -0.21
++[4,] 0.00 0.24 0.21 0.00
++
++$DD2
++ [,1] [,2] [,3] [,4]
++[1,] 0.0000000 0.3428571 0.3230769 0.0000000
++[2,] -0.3428571 0.0000000 0.1142857 -0.3428571
++[3,] -0.3230769 -0.1142857 0.0000000 -0.3230769
++[4,] 0.0000000 0.3428571 0.3230769 0.0000000
++
++$DD3
++ [,1] [,2] [,3] [,4]
++[1,] 0.00000000 0.1567922 0.1420408 -0.01325831
++[2,] -0.15679216 0.0000000 0.1101196 -0.20049485
++[3,] -0.14204082 -0.1101196 0.0000000 -0.13586560
++[4,] 0.01325831 0.2004949 0.1358656 0.00000000
++
++$DD4
++ [,1] [,2] [,3] [,4]
++[1,] 0.00000000 0.2513176 0.2425087 -0.01960102
++[2,] -0.25131757 0.0000000 0.1725441 -0.30993929
++[3,] -0.24250871 -0.1725441 0.0000000 -0.23381521
++[4,] 0.01960102 0.3099393 0.2338152 0.00000000
++
++$McNemar
++ [,1] [,2] [,3] [,4]
++[1,] NA 7.677938 9.0571232 0.000000
++[2,] NA NA 0.2912555 7.677938
++[3,] NA NA NA 9.057123
++[4,] NA NA NA NA
++
++$prob.McNemar
++ [,1] [,2] [,3] [,4]
++[1,] NA 0.005590001 0.002616734 1.000000000
++[2,] NA NA 0.589417103 0.005590001
++[3,] NA NA NA 0.002616734
++[4,] NA NA NA NA
++
++>
++>
++>
++> cleanEx()
++> nameEx("bioenv")
++> ### * bioenv
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: bioenv
++> ### Title: Best Subset of Environmental Variables with Maximum (Rank)
++> ### Correlation with Community Dissimilarities
++> ### Aliases: bioenv bioenv.default bioenv.formula summary.bioenv bioenvdist
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> # The method is very slow for large number of possible subsets.
++> # Therefore only 6 variables in this example.
++> data(varespec)
++> data(varechem)
++> sol <- bioenv(wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al, varechem)
++> sol
++
++Call:
++bioenv(formula = wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al,
data = varechem)
++
++Subset of environmental variables with best correlation to community data.
++
++Correlations: spearman
++Dissimilarities: bray
++Metric: euclidean
++
++Best model has 3 parameters (max. 6 allowed):
++P Ca Al
++with correlation 0.4004806
++
++> summary(sol)
++ size correlation
++P 1 0.2513
++P Al 2 0.4004
++P Ca Al 3 0.4005
++P Ca pH Al 4 0.3619
++log(N) P Ca pH Al 5 0.3216
++log(N) P K Ca pH Al 6 0.2822
++>
++>
++>
++> cleanEx()
++> nameEx("biplot.rda")
++> ### * biplot.rda
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: biplot.rda
++> ### Title: PCA biplot
++> ### Aliases: biplot.rda
++> ### Keywords: hplot
++>
++> ### ** Examples
++>
++> data(dune)
++> mod <- rda(dune, scale = TRUE)
++> biplot(mod, scaling = "symmetric")
++>
++> ## different type for species and site scores
++> biplot(mod, scaling = "symmetric", type = c("text", "points"))
++>
++>
++>
++> cleanEx()
++> nameEx("capscale")
++> ### * capscale
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: capscale
++> ### Title: [Partial] Distance-based Redundancy Analysis
++> ### Aliases: capscale oldCapscale dbrda
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> ## Basic Analysis
++> vare.cap <- capscale(varespec ~ N + P + K + Condition(Al), varechem,
+++ dist="bray")
++> vare.cap
++Call: capscale(formula = varespec ~ N + P + K + Condition(Al), data =
++varechem, distance = "bray")
++
++ Inertia Proportion Eigenvals Rank
++Total 4.5444 1.0000 4.8034
++Conditional 0.9726 0.2140 0.9772 1
++Constrained 0.9731 0.2141 0.9972 3
++Unconstrained 2.5987 0.5718 2.8290 15
++Imaginary -0.2590 8
++Inertia is squared Bray distance
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2 CAP3
++0.5413 0.3265 0.1293
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11
++0.9065 0.5127 0.3379 0.2626 0.2032 0.1618 0.1242 0.0856 0.0689 0.0583 0.0501
++ MDS12 MDS13 MDS14 MDS15
++0.0277 0.0208 0.0073 0.0013
++
++> plot(vare.cap)
++> anova(vare.cap)
++Permutation test for capscale under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = varespec ~ N + P + K + Condition(Al), data =
varechem, distance = "bray")
++ Df SumOfSqs F Pr(>F)
++Model 3 0.97314 2.3717 0.002 **
++Residual 19 2.59866
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Avoid negative eigenvalues with additive constant
++> capscale(varespec ~ N + P + K + Condition(Al), varechem,
+++ dist="bray", add =TRUE)
++Call: capscale(formula = varespec ~ N + P + K + Condition(Al), data =
++varechem, distance = "bray", add = TRUE)
++
++ Inertia Proportion Rank
++Total 6.2496 1.0000
++Conditional 1.0468 0.1675 1
++Constrained 1.1956 0.1913 3
++Unconstrained 4.0073 0.6412 19
++Inertia is Lingoes adjusted squared Bray distance
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2 CAP3
++0.6103 0.3940 0.1913
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++0.9796 0.5811 0.4077 0.3322 0.2769 0.2346 0.1962 0.1566
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++Constant added to distances: 0.07413903
++
++> ## Avoid negative eigenvalues by taking square roots of dissimilarities
++> capscale(varespec ~ N + P + K + Condition(Al), varechem,
+++ dist = "bray", sqrt.dist= TRUE)
++Call: capscale(formula = varespec ~ N + P + K + Condition(Al), data =
++varechem, distance = "bray", sqrt.dist = TRUE)
++
++ Inertia Proportion Rank
++Total 6.9500 1.0000
++Conditional 0.9535 0.1372 1
++Constrained 1.2267 0.1765 3
++Unconstrained 4.7698 0.6863 19
++Inertia is Bray distance
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2 CAP3
++0.5817 0.4086 0.2365
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++0.9680 0.6100 0.4469 0.3837 0.3371 0.3012 0.2558 0.2010
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++> ## Principal coordinates analysis with extended dissimilarities
++> capscale(varespec ~ 1, dist="bray", metaMDS = TRUE)
++Square root transformation
++Wisconsin double standardization
++Call: capscale(formula = varespec ~ 1, distance = "bray", metaMDSdist =
++TRUE)
++
++ Inertia Eigenvals Rank
++Total 2.54753 2.59500
++Unconstrained 2.54753 2.59500 19
++Imaginary -0.04747 4
++Inertia is squared Bray distance
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++0.6075 0.3820 0.3335 0.2046 0.1731 0.1684 0.1505 0.1163
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++metaMDSdist transformed data: wisconsin(sqrt(varespec))
++
++> ## dbrda
++> dbrda(varespec ~ N + P + K + Condition(Al), varechem,
+++ dist="bray")
++Call: dbrda(formula = varespec ~ N + P + K + Condition(Al), data =
++varechem, distance = "bray")
++
++ Inertia Proportion Rank RealDims
++Total 4.5444 1.0000
++Conditional 0.9726 0.2140 1
++Constrained 0.9731 0.2141 3 3
++Unconstrained 2.5987 0.5718 19 13
++Inertia is squared Bray distance
++
++Eigenvalues for constrained axes:
++dbRDA1 dbRDA2 dbRDA3
++0.5362 0.3198 0.1171
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++0.9054 0.5070 0.3336 0.2581 0.2027 0.1605 0.1221 0.0825
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++> ## avoid negative eigenvalues also with Jaccard distances
++> dbrda(varespec ~ N + P + K + Condition(Al), varechem,
+++ dist="jaccard")
++Call: dbrda(formula = varespec ~ N + P + K + Condition(Al), data =
++varechem, distance = "jaccard")
++
++ Inertia Proportion Rank
++Total 6.5044 1.0000
++Conditional 1.0330 0.1588 1
++Constrained 1.2068 0.1855 3
++Unconstrained 4.2646 0.6557 19
++Inertia is squared Jaccard distance
++
++Eigenvalues for constrained axes:
++dbRDA1 dbRDA2 dbRDA3
++0.5992 0.3994 0.2082
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++1.0388 0.6441 0.4518 0.3759 0.3239 0.2785 0.2279 0.1644
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++>
++>
++>
++> cleanEx()
++> nameEx("cascadeKM")
++> ### * cascadeKM
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: cascadeKM
++> ### Title: K-means partitioning using a range of values of K
++> ### Aliases: cascadeKM cIndexKM plot.cascadeKM orderingKM pregraphKM
++> ### Keywords: cluster
++>
++> ### ** Examples
++>
++> # Partitioning a (10 x 10) data matrix of random numbers
++> mat <- matrix(runif(100),10,10)
++> res <- cascadeKM(mat, 2, 5, iter = 25, criterion = 'calinski')
++> toto <- plot(res)
++>
++> # Partitioning an autocorrelated time series
++> vec <- sort(matrix(runif(30),30,1))
++> res <- cascadeKM(vec, 2, 5, iter = 25, criterion = 'calinski')
++> toto <- plot(res)
++>
++> # Partitioning a large autocorrelated time series
++> # Note that we remove the grid lines
++> vec <- sort(matrix(runif(1000),1000,1))
++> res <- cascadeKM(vec, 2, 7, iter = 10, criterion = 'calinski')
++> toto <- plot(res, gridcol=NA)
++>
++>
++>
++>
++> cleanEx()
++> nameEx("cca")
++> ### * cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: cca
++> ### Title: [Partial] [Constrained] Correspondence Analysis and Redundancy
++> ### Analysis
++> ### Aliases: cca cca.default cca.formula rda rda.default rda.formula
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> ## Common but bad way: use all variables you happen to have in your
++> ## environmental data matrix
++> vare.cca <- cca(varespec, varechem)
++> vare.cca
++Call: cca(X = varespec, Y = varechem)
++
++ Inertia Proportion Rank
++Total 2.0832 1.0000
++Constrained 1.4415 0.6920 14
++Unconstrained 0.6417 0.3080 9
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10 CCA11
++0.4389 0.2918 0.1628 0.1421 0.1180 0.0890 0.0703 0.0584 0.0311 0.0133 0.0084
++ CCA12 CCA13 CCA14
++0.0065 0.0062 0.0047
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9
++0.19776 0.14193 0.10117 0.07079 0.05330 0.03330 0.01887 0.01510 0.00949
++
++> plot(vare.cca)
++> ## Formula interface and a better model
++> vare.cca <- cca(varespec ~ Al + P*(K + Baresoil), data=varechem)
++> vare.cca
++Call: cca(formula = varespec ~ Al + P * (K + Baresoil), data =
++varechem)
++
++ Inertia Proportion Rank
++Total 2.083 1.000
++Constrained 1.046 0.502 6
++Unconstrained 1.038 0.498 17
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++0.3756 0.2342 0.1407 0.1323 0.1068 0.0561
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
++0.27577 0.15411 0.13536 0.11803 0.08887 0.05511 0.04919 0.03781
++(Showed only 8 of all 17 unconstrained eigenvalues)
++
++> plot(vare.cca)
++> ## `Partialling out' and `negative components of variance'
++> cca(varespec ~ Ca, varechem)
++Call: cca(formula = varespec ~ Ca, data = varechem)
++
++ Inertia Proportion Rank
++Total 2.08320 1.00000
++Constrained 0.15722 0.07547 1
++Unconstrained 1.92598 0.92453 22
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1
++0.15722
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
++0.4745 0.2939 0.2140 0.1954 0.1748 0.1171 0.1121 0.0880
++(Showed only 8 of all 22 unconstrained eigenvalues)
++
++> cca(varespec ~ Ca + Condition(pH), varechem)
++Call: cca(formula = varespec ~ Ca + Condition(pH), data = varechem)
++
++ Inertia Proportion Rank
++Total 2.0832 1.0000
++Conditional 0.1458 0.0700 1
++Constrained 0.1827 0.0877 1
++Unconstrained 1.7547 0.8423 21
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1
++0.18269
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
++0.3834 0.2749 0.2123 0.1760 0.1701 0.1161 0.1089 0.0880
++(Showed only 8 of all 21 unconstrained eigenvalues)
++
++> ## RDA
++> data(dune)
++> data(dune.env)
++> dune.Manure <- rda(dune ~ Manure, dune.env)
++> plot(dune.Manure)
++>
++>
++>
++> cleanEx()
++> nameEx("cca.object")
++> ### * cca.object
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: cca.object
++> ### Title: Result Object from Constrained Ordination with cca, rda or
++> ### capscale
++> ### Aliases: cca.object
++> ### Keywords: models multivariate
++>
++> ### ** Examples
++>
++> # Some species will be missing in the analysis, because only a subset
++> # of sites is used below.
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune[1:15,] ~ ., dune.env[1:15,])
++> # Look at the names of missing species
++> attr(mod$CCA$v, "na.action")
++Airaprae Empenigr Salirepe
++ 3 13 24
++attr(,"class")
++[1] "exclude"
++> # Look at the names of the aliased variables:
++> mod$CCA$alias
++[1] "Manure^4"
++> # Access directly constrained weighted orthonormal species and site
++> # scores, constrained eigenvalues and margin sums.
++> spec <- mod$CCA$v
++> sites <- mod$CCA$u
++> eig <- mod$CCA$eig
++> rsum <- mod$rowsum
++> csum <- mod$colsum
++>
++>
++>
++> cleanEx()
++> nameEx("clamtest")
++> ### * clamtest
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: clamtest
++> ### Title: Multinomial Species Classification Method (CLAM)
++> ### Aliases: clamtest summary.clamtest plot.clamtest
++> ### Keywords: htest
++>
++> ### ** Examples
++>
++> data(mite)
++> data(mite.env)
++> sol <- with(mite.env, clamtest(mite, Shrub=="None", alpha=0.005))
++> summary(sol)
++Two Groups Species Classification Method (CLAM)
++
++Specialization threshold = 0.6666667
++Alpha level = 0.005
++
++Estimated sample coverage:
++ FALSE TRUE
++1.0000 0.9996
++
++Minimum abundance for classification:
++FALSE TRUE
++ 27 9
++
++ Species Proportion
++Generalist 10 0.286
++Specialist_FALSE 14 0.400
++Specialist_TRUE 4 0.114
++Too_rare 7 0.200
++> head(sol)
++ Species Total_FALSE Total_TRUE Classes
++1 Brachy 534 77 Generalist
++2 PHTH 89 0 Specialist_FALSE
++3 HPAV 389 207 Generalist
++4 RARD 85 0 Specialist_FALSE
++5 SSTR 22 0 Too_rare
++6 Protopl 26 0 Too_rare
++> plot(sol)
++>
++>
++>
++> cleanEx()
++> nameEx("commsim")
++> ### * commsim
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: commsim
++> ### Title: Create an Object for Null Model Algorithms
++> ### Aliases: commsim make.commsim print.commsim
++> ### Keywords: multivariate datagen
++>
++> ### ** Examples
++>
++> ## write the r00 algorithm
++> f <- function(x, n, ...)
+++ array(replicate(n, sample(x)), c(dim(x), n))
++> (cs <- commsim("r00", fun=f, binary=TRUE,
+++ isSeq=FALSE, mode="integer"))
++An object of class “commsim”
++‘r00’ method (binary, non-sequential, integer mode)
++
++>
++> ## retrieving the sequential swap algorithm
++> (cs <- make.commsim("swap"))
++An object of class “commsim”
++‘swap’ method (binary, sequential, integer mode)
++
++>
++> ## feeding a commsim object as argument
++> make.commsim(cs)
++An object of class “commsim”
++‘swap’ method (binary, sequential, integer mode)
++
++>
++> ## structural constraints
++> diagfun <- function(x, y) {
+++ c(sum = sum(y) == sum(x),
+++ fill = sum(y > 0) == sum(x > 0),
+++ rowSums = all(rowSums(y) == rowSums(x)),
+++ colSums = all(colSums(y) == colSums(x)),
+++ rowFreq = all(rowSums(y > 0) == rowSums(x > 0)),
+++ colFreq = all(colSums(y > 0) == colSums(x > 0)))
+++ }
++> evalfun <- function(meth, x, n) {
+++ m <- nullmodel(x, meth)
+++ y <- simulate(m, nsim=n)
+++ out <- rowMeans(sapply(1:dim(y)[3],
+++ function(i) diagfun(attr(y, "data"), y[,,i])))
+++ z <- as.numeric(c(attr(y, "binary"), attr(y, "isSeq"),
+++ attr(y, "mode") == "double"))
+++ names(z) <- c("binary", "isSeq", "double")
+++ c(z, out)
+++ }
++> x <- matrix(rbinom(10*12, 1, 0.5)*rpois(10*12, 3), 12, 10)
++> algos <- make.commsim()
++> a <- t(sapply(algos, evalfun, x=x, n=10))
++> print(as.table(ifelse(a==1,1,0)), zero.print = ".")
++ binary isSeq double sum fill rowSums colSums rowFreq colFreq
++r00 1 . . 1 1 . . . .
++c0 1 . . 1 1 . 1 . 1
++r0 1 . . 1 1 1 . 1 .
++r0_old 1 . . 1 1 1 . 1 .
++r1 1 . . 1 1 1 . 1 .
++r2 1 . . 1 1 1 . 1 .
++quasiswap 1 . . 1 1 1 1 1 1
++swap 1 1 . 1 1 1 1 1 1
++tswap 1 1 . 1 1 1 1 1 1
++curveball 1 1 . 1 1 1 1 1 1
++backtrack 1 . . 1 1 1 1 1 1
++r2dtable . . . 1 . 1 1 . .
++swap_count . 1 . 1 1 1 1 . .
++quasiswap_count . . . 1 1 1 1 . .
++swsh_samp . . 1 1 1 . . 1 1
++swsh_both . . . 1 1 . . 1 1
++swsh_samp_r . . 1 1 1 1 . 1 1
++swsh_samp_c . . 1 1 1 . 1 1 1
++swsh_both_r . . . 1 1 1 . 1 1
++swsh_both_c . . . 1 1 . 1 1 1
++abuswap_r . 1 1 1 1 1 . 1 1
++abuswap_c . 1 1 1 1 . 1 1 1
++r00_samp . . 1 1 1 . . . .
++c0_samp . . 1 1 1 . 1 . 1
++r0_samp . . 1 1 1 1 . 1 .
++r00_ind . . . 1 . . . . .
++c0_ind . . . 1 . . 1 . .
++r0_ind . . . 1 . 1 . . .
++r00_both . . . 1 1 . . . .
++c0_both . . . 1 1 . 1 . 1
++r0_both . . . 1 1 1 . 1 .
++>
++>
++>
++> cleanEx()
++> nameEx("contribdiv")
++> ### * contribdiv
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: contribdiv
++> ### Title: Contribution Diversity Approach
++> ### Aliases: contribdiv plot.contribdiv
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## Artificial example given in
++> ## Table 2 in Lu et al. 2007
++> x <- matrix(c(
+++ 1/3,1/3,1/3,0,0,0,
+++ 0,0,1/3,1/3,1/3,0,
+++ 0,0,0,1/3,1/3,1/3),
+++ 3, 6, byrow = TRUE,
+++ dimnames = list(LETTERS[1:3],letters[1:6]))
++> x
++ a b c d e f
++A 0.3333333 0.3333333 0.3333333 0.0000000 0.0000000 0.0000000
++B 0.0000000 0.0000000 0.3333333 0.3333333 0.3333333 0.0000000
++C 0.0000000 0.0000000 0.0000000 0.3333333 0.3333333 0.3333333
++> ## Compare results with Table 2
++> contribdiv(x, "richness")
++ alpha beta gamma
++A 1 1.5 2.5
++B 1 0.5 1.5
++C 1 1.0 2.0
++> contribdiv(x, "simpson")
++ alpha beta gamma
++A 0.6666667 0.1851852 0.8518519
++B 0.6666667 0.1111111 0.7777778
++C 0.6666667 0.1481481 0.8148148
++> ## Relative contribution (C values), compare with Table 2
++> (cd1 <- contribdiv(x, "richness", relative = TRUE, scaled = FALSE))
++ alpha beta gamma
++A 0 0.5 0.5
++B 0 -0.5 -0.5
++C 0 0.0 0.0
++> (cd2 <- contribdiv(x, "simpson", relative = TRUE, scaled = FALSE))
++ alpha beta gamma
++A 0 0.03703704 0.03703704
++B 0 -0.03703704 -0.03703704
++C 0 0.00000000 0.00000000
++> ## Differentiation coefficients
++> attr(cd1, "diff.coef") # D_ST
++[1] 0.5
++> attr(cd2, "diff.coef") # D_DT
++[1] 0.1818182
++> ## BCI data set
++> data(BCI)
++> opar <- par(mfrow=c(2,2))
++> plot(contribdiv(BCI, "richness"), main = "Absolute")
++> plot(contribdiv(BCI, "richness", relative = TRUE), main = "Relative")
++> plot(contribdiv(BCI, "simpson"))
++> plot(contribdiv(BCI, "simpson", relative = TRUE))
++> par(opar)
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("decorana")
++> ### * decorana
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: decorana
++> ### Title: Detrended Correspondence Analysis and Basic Reciprocal Averaging
++> ### Aliases: decorana summary.decorana print.summary.decorana plot.decorana
++> ### downweight scores.decorana points.decorana text.decorana
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> vare.dca <- decorana(varespec)
++> vare.dca
++
++Call:
++decorana(veg = varespec)
++
++Detrended correspondence analysis with 26 segments.
++Rescaling of axes with 4 iterations.
++
++ DCA1 DCA2 DCA3 DCA4
++Eigenvalues 0.5235 0.3253 0.20010 0.19176
++Decorana values 0.5249 0.1572 0.09669 0.06075
++Axis lengths 2.8161 2.2054 1.54650 1.64864
++
++> summary(vare.dca)
++
++Call:
++decorana(veg = varespec)
++
++Detrended correspondence analysis with 26 segments.
++Rescaling of axes with 4 iterations.
++
++ DCA1 DCA2 DCA3 DCA4
++Eigenvalues 0.5235 0.3253 0.20010 0.19176
++Decorana values 0.5249 0.1572 0.09669 0.06075
++Axis lengths 2.8161 2.2054 1.54650 1.64864
++
++Species scores:
++
++ DCA1 DCA2 DCA3 DCA4 Totals
++Callvulg 0.04119 -1.53268 -2.55101 1.32277 45.07
++Empenigr 0.09019 0.82274 0.20569 0.30631 151.99
++Rhodtome 1.34533 2.47141 -0.34970 -1.13823 8.39
++Vaccmyrt 1.86298 1.71424 -0.60535 -0.40205 50.71
++Vaccviti 0.16641 0.71095 0.00313 -0.55801 275.03
++Pinusylv -0.73490 1.62050 -1.60275 -2.10199 4.11
++Descflex 1.97061 1.81651 1.74896 -0.91463 5.60
++Betupube 0.79745 3.36374 -0.94546 -1.01741 0.29
++Vacculig -0.08912 -1.17478 2.86624 0.87025 15.22
++Diphcomp -0.82669 -0.44195 2.58579 -0.38459 3.24
++Dicrsp 2.37743 -0.27373 -0.47099 -1.89036 40.50
++Dicrfusc 1.58267 -1.33770 -1.33563 1.47417 113.52
++Dicrpoly 0.86689 2.39519 -0.82064 -3.41534 6.06
++Hylosple 2.66242 1.19669 1.48288 -0.69978 18.04
++Pleuschr 1.64098 0.15607 0.30044 -0.26717 377.97
++Polypili -0.56213 0.14009 0.25198 0.49177 0.61
++Polyjuni 1.22244 -0.89173 0.61287 3.60066 13.85
++Polycomm 1.01545 2.08388 0.06402 0.84199 0.71
++Pohlnuta -0.00712 1.09704 -0.82126 -1.59862 2.62
++Ptilcili 0.48093 2.86420 -0.71801 -1.02698 14.01
++Barbhatc 0.58303 3.71792 -0.84212 -1.88837 3.19
++Cladarbu -0.18554 -1.18973 0.68113 0.55399 255.05
++Cladrang -0.83427 -0.78085 0.90603 0.70057 388.71
++Cladstel -1.67768 0.98907 -0.83789 -0.60206 486.71
++Cladunci 0.97686 -1.70859 -1.68281 -2.26756 56.28
++Cladcocc -0.27221 -0.76713 -0.63836 0.66927 2.79
++Cladcorn 0.29068 -0.97039 0.50414 0.95738 6.22
++Cladgrac 0.21778 -0.41879 0.06530 -0.31472 5.14
++Cladfimb 0.00889 -0.23922 -0.26505 0.33123 3.96
++Cladcris 0.37774 -1.09161 -0.55627 0.23868 7.47
++Cladchlo -0.91983 1.54955 -0.58109 -1.48643 1.16
++Cladbotr 0.66438 2.19584 -0.90331 -0.91391 0.47
++Cladamau -0.96418 -0.98992 2.71458 0.52352 0.14
++Cladsp -1.12318 -0.15330 -0.69833 0.44040 0.52
++Cetreric 0.27163 -1.28867 -0.81682 -1.93935 3.60
++Cetrisla -0.50158 2.22098 -1.16461 -1.89349 2.03
++Flavniva -1.67937 -3.67985 4.15644 3.18919 11.85
++Nepharct 2.18561 -0.82837 0.71958 5.81930 5.26
++Stersp -0.78699 -2.01214 2.31212 2.03946 17.52
++Peltapht 0.45763 -0.34395 0.09916 1.34695 0.76
++Icmaeric 0.04950 -1.97605 1.41509 2.10154 0.22
++Cladcerv -1.21585 -2.30519 2.55186 3.41532 0.10
++Claddefo 0.60517 -1.19771 -0.33388 0.22585 10.23
++Cladphyl -1.53959 1.48574 -1.43209 -1.52387 0.80
++
++Site scores:
++
++ DCA1 DCA2 DCA3 DCA4 Totals
++18 -0.1729 -0.2841 0.4775 0.2521 89.2
++15 0.8539 -0.3360 0.0708 0.0924 89.8
++24 1.2467 -0.1183 -0.1211 -0.8718 94.2
++27 1.0675 0.4169 0.2897 -0.1758 125.6
++23 0.4234 0.0112 0.2179 0.1265 90.5
++19 0.0252 0.3600 -0.0263 -0.1168 81.3
++22 1.0695 -0.3707 -0.4285 0.4145 109.8
++16 0.7724 -0.5325 -0.2856 0.5269 88.5
++28 1.6189 0.5482 0.2342 -0.3333 110.7
++13 -0.2642 -0.6851 -0.3777 0.5003 101.9
++14 0.6431 -0.9604 -0.6000 -0.2885 81.7
++20 0.4504 -0.1666 0.1850 -0.1291 64.1
++25 1.2501 -0.2248 0.0244 0.3741 94.1
++7 -0.3910 -0.7618 0.8640 0.5557 103.4
++5 -0.6407 -0.9427 0.9465 0.7769 94.8
++6 -0.4523 -0.5529 0.3988 0.2781 110.9
++3 -1.1043 0.2106 -0.0653 -0.0539 106.7
++4 -0.9454 -0.5974 0.4639 0.4889 84.8
++2 -1.1971 0.5691 -0.3246 -0.2522 119.1
++9 -1.0983 0.7850 -0.5274 -0.4848 122.6
++12 -0.8673 0.5621 -0.3254 -0.3217 119.8
++10 -1.1842 0.7442 -0.4995 -0.3917 122.4
++11 -0.4134 0.0260 0.0107 -0.0682 112.8
++21 0.3210 1.2450 -0.2541 -0.5253 99.2
++
++> plot(vare.dca)
++>
++> ### the detrending rationale:
++> gaussresp <- function(x,u) exp(-(x-u)^2/2)
++> x <- seq(0,6,length=15) ## The gradient
++> u <- seq(-2,8,len=23) ## The optima
++> pack <- outer(x,u,gaussresp)
++> matplot(x, pack, type="l", main="Species packing")
++> opar <- par(mfrow=c(2,2))
++> plot(scores(prcomp(pack)), asp=1, type="b", main="PCA")
++> plot(scores(decorana(pack, ira=1)), asp=1, type="b", main="CA")
++> plot(scores(decorana(pack)), asp=1, type="b", main="DCA")
++> plot(scores(cca(pack ~ x), dis="sites"), asp=1, type="b", main="CCA")
++>
++> ### Let's add some noise:
++> noisy <- (0.5 + runif(length(pack)))*pack
++> par(mfrow=c(2,1))
++> matplot(x, pack, type="l", main="Ideal model")
++> matplot(x, noisy, type="l", main="Noisy model")
++> par(mfrow=c(2,2))
++> plot(scores(prcomp(noisy)), type="b", main="PCA", asp=1)
++> plot(scores(decorana(noisy, ira=1)), type="b", main="CA", asp=1)
++> plot(scores(decorana(noisy)), type="b", main="DCA", asp=1)
++> plot(scores(cca(noisy ~ x), dis="sites"), asp=1, type="b", main="CCA")
++> par(opar)
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("decostand")
++> ### * decostand
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: decostand
++> ### Title: Standardization Methods for Community Ecology
++> ### Aliases: decostand wisconsin
++> ### Keywords: multivariate manip
++>
++> ### ** Examples
++>
++> data(varespec)
++> sptrans <- decostand(varespec, "max")
++> apply(sptrans, 2, max)
++Callvulg Empenigr Rhodtome Vaccmyrt Vaccviti Pinusylv Descflex Betupube
++ 1 1 1 1 1 1 1 1
++Vacculig Diphcomp Dicrsp Dicrfusc Dicrpoly Hylosple Pleuschr Polypili
++ 1 1 1 1 1 1 1 1
++Polyjuni Polycomm Pohlnuta Ptilcili Barbhatc Cladarbu Cladrang Cladstel
++ 1 1 1 1 1 1 1 1
++Cladunci Cladcocc Cladcorn Cladgrac Cladfimb Cladcris Cladchlo Cladbotr
++ 1 1 1 1 1 1 1 1
++Cladamau Cladsp Cetreric Cetrisla Flavniva Nepharct Stersp Peltapht
++ 1 1 1 1 1 1 1 1
++Icmaeric Cladcerv Claddefo Cladphyl
++ 1 1 1 1
++> sptrans <- wisconsin(varespec)
++>
++> ## Chi-square: PCA similar but not identical to CA.
++> ## Use wcmdscale for weighted analysis and identical results.
++> sptrans <- decostand(varespec, "chi.square")
++> plot(procrustes(rda(sptrans), cca(varespec)))
++>
++>
++>
++> cleanEx()
++> nameEx("designdist")
++> ### * designdist
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Encoding: UTF-8
++>
++> ### Name: designdist
++> ### Title: Design your own Dissimilarities
++> ### Aliases: designdist
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(BCI)
++> ## Four ways of calculating the same Sørensen dissimilarity
++> d0 <- vegdist(BCI, "bray", binary = TRUE)
++> d1 <- designdist(BCI, "(A+B-2*J)/(A+B)")
++> d2 <- designdist(BCI, "(b+c)/(2*a+b+c)", abcd = TRUE)
++> d3 <- designdist(BCI, "gamma/alpha - 1", alphagamma = TRUE)
++> ## Arrhenius dissimilarity: the value of z in the species-area model
++> ## S = c*A^z when combining two sites of equal areas, where S is the
++> ## number of species, A is the area, and c and z are model parameters.
++> ## The A below is not the area (which cancels out), but number of
++> ## species in one of the sites, as defined in designdist().
++> dis <- designdist(BCI, "(log(A+B-J)-log(A+B)+log(2))/log(2)")
++> ## This can be used in clustering or ordination...
++> ordiplot(cmdscale(dis))
++Warning in ordiplot(cmdscale(dis)) : Species scores not available
++> ## ... or in analysing beta diversity (without gradients)
++> summary(dis)
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 0.2733 0.3895 0.4192 0.4213 0.4537 0.5906
++>
++>
++>
++> cleanEx()
++> nameEx("deviance.cca")
++> ### * deviance.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: deviance.cca
++> ### Title: Statistics Resembling Deviance and AIC for Constrained
++> ### Ordination
++> ### Aliases: deviance.cca deviance.rda extractAIC.cca
++> ### Keywords: multivariate models
++>
++> ### ** Examples
++>
++> # The deviance of correspondence analysis equals Chi-square
++> data(dune)
++> data(dune.env)
++> chisq.test(dune)
++Warning in chisq.test(dune) :
++ Chi-squared approximation may be incorrect
++
++ Pearson's Chi-squared test
++
++data: dune
++X-squared = 1449, df = 551, p-value < 2.2e-16
++
++> deviance(cca(dune))
++[1] 1448.956
++> # Stepwise selection (forward from an empty model "dune ~ 1")
++> ord <- cca(dune ~ ., dune.env)
++> step(cca(dune ~ 1, dune.env), scope = formula(ord))
++Start: AIC=87.66
++dune ~ 1
++
++ Df AIC
+++ Moisture 3 86.608
+++ Management 3 86.935
+++ A1 1 87.411
++<none> 87.657
+++ Manure 4 88.832
+++ Use 2 89.134
++
++Step: AIC=86.61
++dune ~ Moisture
++
++ Df AIC
++<none> 86.608
+++ Management 3 86.813
+++ A1 1 86.992
+++ Use 2 87.259
+++ Manure 4 87.342
++- Moisture 3 87.657
++Call: cca(formula = dune ~ Moisture, data = dune.env)
++
++ Inertia Proportion Rank
++Total 2.1153 1.0000
++Constrained 0.6283 0.2970 3
++Unconstrained 1.4870 0.7030 16
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3
++0.4187 0.1330 0.0766
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11
++0.4098 0.2259 0.1761 0.1234 0.1082 0.0908 0.0859 0.0609 0.0566 0.0467 0.0419
++ CA12 CA13 CA14 CA15 CA16
++0.0201 0.0143 0.0099 0.0085 0.0080
++
++>
++>
++>
++> cleanEx()
++> nameEx("dispindmorisita")
++> ### * dispindmorisita
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: dispindmorisita
++> ### Title: Morisita index of intraspecific aggregation
++> ### Aliases: dispindmorisita
++> ### Keywords: multivariate spatial
++>
++> ### ** Examples
++>
++> data(dune)
++> x <- dispindmorisita(dune)
++> x
++ imor mclu muni imst pchisq
++Achimill 2.1666667 1.923488 0.327101099 0.50672636 9.157890e-03
++Agrostol 1.8085106 1.294730 0.785245032 0.51373357 1.142619e-05
++Airaprae 8.0000000 4.463082 -1.523370880 0.61382303 3.571702e-04
++Alopgeni 2.5396825 1.395781 0.711614757 0.53074307 3.024441e-08
++Anthodor 2.6666667 1.692616 0.495325824 0.52660266 5.897217e-05
++Bellpere 2.0512821 2.154361 0.158876373 0.45535255 3.451547e-02
++Bromhord 3.2380952 1.989452 0.279036892 0.53466422 1.170437e-04
++Chenalbu NaN Inf -Inf NaN NaN
++Cirsarve 20.0000000 14.852327 -9.093483518 1.00000000 5.934709e-03
++Comapalu 6.6666667 5.617442 -2.364494506 0.53647558 1.055552e-02
++Eleopalu 3.7333333 1.577180 0.579438187 0.55851854 2.958285e-10
++Elymrepe 2.7692308 1.554093 0.596260659 0.53293787 1.180195e-06
++Empenigr 20.0000000 14.852327 -9.093483518 1.00000000 5.934709e-03
++Hyporadi 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
++Juncarti 3.1372549 1.814843 0.406265675 0.53635966 2.066336e-05
++Juncbufo 4.1025641 2.154361 0.158876373 0.55458486 1.503205e-05
++Lolipere 1.5849970 1.243023 0.822921342 0.50911591 5.873839e-05
++Planlanc 2.4615385 1.554093 0.596260659 0.52459747 1.921730e-05
++Poaprat 1.1702128 1.294730 0.785245032 0.28876015 1.046531e-01
++Poatriv 1.4644137 1.223425 0.837201879 0.50641728 2.747301e-04
++Ranuflam 2.4175824 2.065564 0.223578191 0.50981405 7.010483e-03
++Rumeacet 3.9215686 1.814843 0.406265675 0.55792432 1.530085e-07
++Sagiproc 2.4210526 1.729070 0.468764025 0.51893672 4.956394e-04
++Salirepe 5.8181818 2.385233 -0.009348352 0.59744520 2.687397e-07
++Scorautu 0.9643606 1.261365 0.809556915 -0.09356972 5.823404e-01
++Trifprat 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
++Trifrepe 1.2210916 1.301138 0.780576445 0.36709402 6.335449e-02
++Vicilath 3.3333333 5.617442 -2.364494506 0.25266513 1.301890e-01
++Bracruta 1.1904762 1.288590 0.789719093 0.33001160 8.071762e-02
++Callcusp 5.3333333 2.539147 -0.121498169 0.58001287 7.982634e-06
++> y <- dispindmorisita(dune, unique.rm = TRUE)
++> y
++ imor mclu muni imst pchisq
++Achimill 2.1666667 1.923488 0.327101099 0.50672636 9.157890e-03
++Agrostol 1.8085106 1.294730 0.785245032 0.51373357 1.142619e-05
++Airaprae 8.0000000 4.463082 -1.523370880 0.61382303 3.571702e-04
++Alopgeni 2.5396825 1.395781 0.711614757 0.53074307 3.024441e-08
++Anthodor 2.6666667 1.692616 0.495325824 0.52660266 5.897217e-05
++Bellpere 2.0512821 2.154361 0.158876373 0.45535255 3.451547e-02
++Bromhord 3.2380952 1.989452 0.279036892 0.53466422 1.170437e-04
++Comapalu 6.6666667 5.617442 -2.364494506 0.53647558 1.055552e-02
++Eleopalu 3.7333333 1.577180 0.579438187 0.55851854 2.958285e-10
++Elymrepe 2.7692308 1.554093 0.596260659 0.53293787 1.180195e-06
++Hyporadi 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
++Juncarti 3.1372549 1.814843 0.406265675 0.53635966 2.066336e-05
++Juncbufo 4.1025641 2.154361 0.158876373 0.55458486 1.503205e-05
++Lolipere 1.5849970 1.243023 0.822921342 0.50911591 5.873839e-05
++Planlanc 2.4615385 1.554093 0.596260659 0.52459747 1.921730e-05
++Poaprat 1.1702128 1.294730 0.785245032 0.28876015 1.046531e-01
++Poatriv 1.4644137 1.223425 0.837201879 0.50641728 2.747301e-04
++Ranuflam 2.4175824 2.065564 0.223578191 0.50981405 7.010483e-03
++Rumeacet 3.9215686 1.814843 0.406265675 0.55792432 1.530085e-07
++Sagiproc 2.4210526 1.729070 0.468764025 0.51893672 4.956394e-04
++Salirepe 5.8181818 2.385233 -0.009348352 0.59744520 2.687397e-07
++Scorautu 0.9643606 1.261365 0.809556915 -0.09356972 5.823404e-01
++Trifprat 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
++Trifrepe 1.2210916 1.301138 0.780576445 0.36709402 6.335449e-02
++Vicilath 3.3333333 5.617442 -2.364494506 0.25266513 1.301890e-01
++Bracruta 1.1904762 1.288590 0.789719093 0.33001160 8.071762e-02
++Callcusp 5.3333333 2.539147 -0.121498169 0.58001287 7.982634e-06
++> dim(x) ## with unique species
++[1] 30 5
++> dim(y) ## unique species removed
++[1] 27 5
++>
++>
++>
++> cleanEx()
++> nameEx("dispweight")
++> ### * dispweight
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: dispweight
++> ### Title: Dispersion-based weighting of species counts
++> ### Aliases: dispweight gdispweight summary.dispweight
++> ### Keywords: multivariate manip
++>
++> ### ** Examples
++>
++> data(mite, mite.env)
++> ## dispweight and its summary
++> mite.dw <- with(mite.env, dispweight(mite, Shrub, nsimul = 99))
++> summary(mite.dw)
++ Dispersion Weight Df Pr(Disp.)
++Brachy 9.6908 0.1031909 67 0.01 **
++PHTH 3.2809 0.3047900 49 0.01 **
++HPAV 6.5263 0.1532264 67 0.01 **
++RARD 6.0477 0.1653525 49 0.01 **
++SSTR 2.2619 0.4421053 49 0.01 **
++Protopl 5.4229 0.1844031 49 0.01 **
++MEGR 4.5354 0.2204860 67 0.01 **
++MPRO 1.2687 1.0000000 67 0.07 .
++TVIE 2.5956 0.3852706 67 0.01 **
++HMIN 10.0714 0.0992906 67 0.01 **
++HMIN2 7.5674 0.1321466 49 0.01 **
++NPRA 2.6743 0.3739344 67 0.01 **
++TVEL 9.6295 0.1038474 49 0.01 **
++ONOV 11.3628 0.0880064 67 0.01 **
++SUCT 8.7372 0.1144533 67 0.01 **
++LCIL 129.4436 0.0077254 67 0.01 **
++Oribatl1 4.1250 0.2424248 67 0.01 **
++Ceratoz1 1.7150 0.5830768 67 0.01 **
++PWIL 2.2943 0.4358538 67 0.01 **
++Galumna1 2.8777 0.3474943 49 0.01 **
++Stgncrs2 3.8242 0.2614953 49 0.01 **
++HRUF 1.7575 0.5690021 67 0.01 **
++Trhypch1 14.9225 0.0670128 67 0.01 **
++PPEL 1.3628 1.0000000 49 0.07 .
++NCOR 2.5875 0.3864771 67 0.01 **
++SLAT 2.7857 0.3589744 49 0.01 **
++FSET 4.8901 0.2044944 49 0.01 **
++Lepidzts 1.6577 0.6032360 49 0.02 *
++Eupelops 1.4611 0.6844033 67 0.03 *
++Miniglmn 1.6505 0.6058733 49 0.01 **
++LRUG 12.0658 0.0828790 67 0.01 **
++PLAG2 3.2403 0.3086090 67 0.01 **
++Ceratoz3 3.5125 0.2846947 67 0.01 **
++Oppiminu 3.1680 0.3156525 67 0.01 **
++Trimalc2 10.5927 0.0944046 67 0.01 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++Based on 99 simulations on 'c0_ind' nullmodel
++> ## generalized dispersion weighting
++> mite.dw <- gdispweight(mite ~ Shrub + WatrCont, data = mite.env)
++> rda(mite.dw ~ Shrub + WatrCont, data = mite.env)
++Call: rda(formula = mite.dw ~ Shrub + WatrCont, data = mite.env)
++
++ Inertia Proportion Rank
++Total 38.1640 1.0000
++Constrained 9.2129 0.2414 3
++Unconstrained 28.9511 0.7586 35
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3
++7.986 0.748 0.480
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
++5.886 3.634 2.791 2.592 1.932 1.573 1.210 1.078
++(Showed only 8 of all 35 unconstrained eigenvalues)
++
++>
++>
++>
++> cleanEx()
++> nameEx("distconnected")
++> ### * distconnected
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: distconnected
++> ### Title: Connectedness of Dissimilarities
++> ### Aliases: distconnected no.shared
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## There are no disconnected data in vegan, and the following uses an
++> ## extremely low threshold limit for connectedness. This is for
++> ## illustration only, and not a recommended practice.
++> data(dune)
++> dis <- vegdist(dune)
++> gr <- distconnected(dis, toolong=0.4)
++Connectivity of distance matrix with threshold dissimilarity 0.4
++Data are disconnected: 6 groups
++Groups sizes
++ 1 2 3 4 5 6
++ 1 11 2 4 1 1
++> # Make sites with no shared species as NA in Manhattan dissimilarities
++> dis <- vegdist(dune, "manhattan")
++> is.na(dis) <- no.shared(dune)
++>
++>
++>
++> cleanEx()
++> nameEx("diversity")
++> ### * diversity
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: diversity
++> ### Title: Ecological Diversity Indices
++> ### Aliases: diversity fisher.alpha specnumber
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++> data(BCI)
++> H <- diversity(BCI)
++> simp <- diversity(BCI, "simpson")
++> invsimp <- diversity(BCI, "inv")
++> ## Unbiased Simpson (Hurlbert 1971, eq. 5) with rarefy:
++> unbias.simp <- rarefy(BCI, 2) - 1
++> ## Fisher alpha
++> alpha <- fisher.alpha(BCI)
++> ## Plot all
++> pairs(cbind(H, simp, invsimp, unbias.simp, alpha), pch="+", col="blue")
++> ## Species richness (S) and Pielou's evenness (J):
++> S <- specnumber(BCI) ## rowSums(BCI > 0) does the same...
++> J <- H/log(S)
++> ## beta diversity defined as gamma/alpha - 1:
++> data(dune)
++> data(dune.env)
++> alpha <- with(dune.env, tapply(specnumber(dune), Management, mean))
++> gamma <- with(dune.env, specnumber(dune, Management))
++> gamma/alpha - 1
++ BF HF NM SF
++0.5483871 0.6666667 1.6250000 1.2909091
++>
++>
++>
++> cleanEx()
++> nameEx("dune")
++> ### * dune
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: dune
++> ### Title: Vegetation and Environment in Dutch Dune Meadows.
++> ### Aliases: dune dune.env
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++>
++>
++>
++> cleanEx()
++> nameEx("dune.taxon")
++> ### * dune.taxon
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: dune.taxon
++> ### Title: Taxonomic Classification and Phylogeny of Dune Meadow Species
++> ### Aliases: dune.taxon dune.phylodis
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(dune.taxon)
++> data(dune.phylodis)
++>
++>
++>
++> cleanEx()
++> nameEx("eigenvals")
++> ### * eigenvals
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: eigenvals
++> ### Title: Extract Eigenvalues from an Ordination Object
++> ### Aliases: eigenvals eigenvals.default eigenvals.prcomp
++> ### eigenvals.princomp eigenvals.cca eigenvals.wcmdscale eigenvals.pcnm
++> ### eigenvals.dudi eigenvals.pca eigenvals.pco summary.eigenvals
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> mod <- cca(varespec ~ Al + P + K, varechem)
++> ev <- eigenvals(mod)
++> ev
++ CCA1 CCA2 CCA3 CA1 CA2 CA3 CA4
CA5
++0.3615566 0.1699600 0.1126167 0.3500372 0.2200788 0.1850741 0.1551179
0.1351054
++ CA6 CA7 CA8 CA9 CA10 CA11 CA12
CA13
++0.1002670 0.0772991 0.0536938 0.0365603 0.0350887 0.0282291 0.0170651
0.0122474
++ CA14 CA15 CA16 CA17 CA18 CA19 CA20
++0.0101910 0.0094701 0.0055090 0.0030529 0.0025118 0.0019485 0.0005178
++> summary(ev)
++Importance of components:
++ CCA1 CCA2 CCA3 CA1 CA2 CA3 CA4
++Eigenvalue 0.3616 0.16996 0.11262 0.3500 0.2201 0.18507 0.15512
++Proportion Explained 0.1736 0.08159 0.05406 0.1680 0.1056 0.08884 0.07446
++Cumulative Proportion 0.1736 0.25514 0.30920 0.4772 0.5829 0.67172 0.74618
++ CA5 CA6 CA7 CA8 CA9 CA10 CA11
++Eigenvalue 0.13511 0.10027 0.07730 0.05369 0.03656 0.03509 0.02823
++Proportion Explained 0.06485 0.04813 0.03711 0.02577 0.01755 0.01684 0.01355
++Cumulative Proportion 0.81104 0.85917 0.89627 0.92205 0.93960 0.95644 0.96999
++ CA12 CA13 CA14 CA15 CA16 CA17
++Eigenvalue 0.01707 0.01225 0.01019 0.00947 0.005509 0.003053
++Proportion Explained 0.00819 0.00588 0.00489 0.00455 0.002640 0.001470
++Cumulative Proportion 0.97818 0.98406 0.98895 0.99350 0.996140 0.997610
++ CA18 CA19 CA20
++Eigenvalue 0.002512 0.001948 0.0005178
++Proportion Explained 0.001210 0.000940 0.0002500
++Cumulative Proportion 0.998820 0.999750 1.0000000
++>
++>
++>
++> cleanEx()
++> nameEx("envfit")
++> ### * envfit
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: envfit
++> ### Title: Fits an Environmental Vector or Factor onto an Ordination
++> ### Aliases: envfit envfit.default envfit.formula vectorfit factorfit
++> ### plot.envfit scores.envfit labels.envfit
++> ### Keywords: multivariate aplot htest
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> library(MASS)
++> ord <- metaMDS(varespec)
++Square root transformation
++Wisconsin double standardization
++Run 0 stress 0.1843196
++Run 1 stress 0.2455912
++Run 2 stress 0.2169407
++Run 3 stress 0.2313231
++Run 4 stress 0.1974406
++Run 5 stress 0.1858402
++Run 6 stress 0.1948414
++Run 7 stress 0.2265717
++Run 8 stress 0.222507
++Run 9 stress 0.2023215
++Run 10 stress 0.2673177
++Run 11 stress 0.1976151
++Run 12 stress 0.1852397
++Run 13 stress 0.2341085
++Run 14 stress 0.1955867
++Run 15 stress 0.2137409
++Run 16 stress 0.2109638
++Run 17 stress 0.1825658
++... New best solution
++... Procrustes: rmse 0.04169825 max resid 0.1521436
++Run 18 stress 0.1843197
++Run 19 stress 0.2570119
++Run 20 stress 0.3760596
++*** No convergence -- monoMDS stopping criteria:
++ 20: stress ratio > sratmax
++> (fit <- envfit(ord, varechem, perm = 999))
++
++***VECTORS
++
++ NMDS1 NMDS2 r2 Pr(>r)
++N -0.05719 -0.99836 0.2537 0.039 *
++P 0.61959 0.78492 0.1938 0.129
++K 0.76629 0.64249 0.1809 0.138
++Ca 0.68506 0.72849 0.4119 0.005 **
++Mg 0.63240 0.77464 0.4271 0.005 **
++S 0.19123 0.98155 0.1752 0.133
++Al -0.87169 0.49006 0.5269 0.001 ***
++Fe -0.93613 0.35164 0.4451 0.001 ***
++Mn 0.79873 -0.60169 0.5230 0.002 **
++Zn 0.61750 0.78657 0.1879 0.122
++Mo -0.90304 0.42955 0.0609 0.524
++Baresoil 0.92503 -0.37988 0.2508 0.043 *
++Humdepth 0.93291 -0.36011 0.5200 0.001 ***
++pH -0.64809 0.76156 0.2308 0.064 .
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++Permutation: free
++Number of permutations: 999
++
++
++> scores(fit, "vectors")
++ NMDS1 NMDS2
++N -0.02880854 -0.5028710
++P 0.27276629 0.3455503
++K 0.32594190 0.2732833
++Ca 0.43965251 0.4675260
++Mg 0.41327822 0.5062343
++S 0.08004855 0.4108725
++Al -0.63277161 0.3557376
++Fe -0.62453266 0.2345938
++Mn 0.57765765 -0.4351493
++Zn 0.26766914 0.3409611
++Mo -0.22292390 0.1060370
++Baresoil 0.46323932 -0.1902392
++Humdepth 0.67274437 -0.2596855
++pH -0.31133604 0.3658438
++> plot(ord)
++> plot(fit)
++> plot(fit, p.max = 0.05, col = "red")
++> ## Adding fitted arrows to CCA. We use "lc" scores, and hope
++> ## that arrows are scaled similarly in cca and envfit plots
++> ord <- cca(varespec ~ Al + P + K, varechem)
++> plot(ord, type="p")
++> fit <- envfit(ord, varechem, perm = 999, display = "lc")
++> plot(fit, p.max = 0.05, col = "red")
++>
++> ## Class variables, formula interface, and displaying the
++> ## inter-class variability with `ordispider', and semitransparent
++> ## white background for labels (semitransparent colours are not
++> ## supported by all graphics devices)
++> data(dune)
++> data(dune.env)
++> attach(dune.env)
++> ord <- cca(dune)
++> fit <- envfit(ord ~ Moisture + A1, dune.env, perm = 0)
++> plot(ord, type = "n")
++> ordispider(ord, Moisture, col="skyblue")
++> points(ord, display = "sites", col = as.numeric(Moisture), pch=16)
++> plot(fit, cex=1.2, axis=TRUE, bg = rgb(1, 1, 1, 0.5))
++> ## Use shorter labels for factor centroids
++> labels(fit)
++$vectors
++[1] "A1"
++
++$factors
++[1] "Moisture1" "Moisture2" "Moisture4" "Moisture5"
++
++> plot(ord)
++> plot(fit, labels=list(factors = paste("M", c(1,2,4,5), sep = "")),
+++ bg = rgb(1,1,0,0.5))
++> detach(dune.env)
++>
++>
++>
++> cleanEx()
++
++detaching ‘package:MASS’
++
++> nameEx("eventstar")
++> ### * eventstar
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: eventstar
++> ### Title: Scale Parameter at the Minimum of the Tsallis Evenness Profile
++> ### Aliases: eventstar
++> ### Keywords: optimize multivariate utilities
++>
++> ### ** Examples
++>
++> data(BCI)
++> (x <- eventstar(BCI[1:5,]))
++ qstar Estar Hstar Dstar
++1 0.6146389 0.4263687 10.524584 67.03551
++2 0.6249249 0.4080263 9.534034 57.66840
++3 0.6380858 0.4062032 9.225458 57.69174
++4 0.6245808 0.4062213 10.140189 65.50247
++5 0.6404825 0.4219957 9.828138 66.96440
++> ## profiling
++> y <- as.numeric(BCI[10,])
++> (z <- eventstar(y))
++ qstar Estar Hstar Dstar
++1 0.6372529 0.4117557 9.546332 61.77715
++> q <- seq(0, 2, 0.05)
++> Eprof <- tsallis(y, scales=q, norm=TRUE)
++> Hprof <- tsallis(y, scales=q)
++> Dprof <- tsallis(y, scales=q, hill=TRUE)
++> opar <- par(mfrow=c(3,1))
++> plot(q, Eprof, type="l", main="Evenness")
++> abline(v=z$qstar, h=tsallis(y, scales=z$qstar, norm=TRUE), col=2)
++> plot(q, Hprof, type="l", main="Diversity")
++> abline(v=z$qstar, h=tsallis(y, scales=z$qstar), col=2)
++> plot(q, Dprof, type="l", main="Effective number of species")
++> abline(v=z$qstar, h=tsallis(y, scales=z$qstar, hill=TRUE), col=2)
++> par(opar)
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("fisherfit")
++> ### * fisherfit
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: fisherfit
++> ### Title: Fit Fisher's Logseries and Preston's Lognormal Model to
++> ### Abundance Data
++> ### Aliases: fisherfit as.fisher plot.fisherfit prestonfit prestondistr
++> ### as.preston plot.prestonfit lines.prestonfit plot.preston
++> ### lines.preston plot.fisher veiledspec
++> ### Keywords: univar distribution
++>
++> ### ** Examples
++>
++> data(BCI)
++> mod <- fisherfit(BCI[5,])
++> mod
++
++Fisher log series model
++No. of species: 101
++Fisher alpha: 37.96423
++
++> # prestonfit seems to need large samples
++> mod.oct <- prestonfit(colSums(BCI))
++> mod.ll <- prestondistr(colSums(BCI))
++> mod.oct
++
++Preston lognormal model
++Method: Quasi-Poisson fit to octaves
++No. of species: 225
++
++ mode width S0
++ 4.885798 2.932690 32.022923
++
++Frequencies by Octave
++ 0 1 2 3 4 5 6 7
++Observed 9.500000 16.00000 18.00000 19.000 30.00000 35.00000 31.00000 26.50000
++Fitted 7.994154 13.31175 19.73342 26.042 30.59502 31.99865 29.79321 24.69491
++ 8 9 10 11
++Observed 18.00000 13.00000 7.000000 2.0000
++Fitted 18.22226 11.97021 7.000122 3.6443
++
++> mod.ll
++
++Preston lognormal model
++Method: maximized likelihood to log2 abundances
++No. of species: 225
++
++ mode width S0
++ 4.365002 2.753531 33.458185
++
++Frequencies by Octave
++ 0 1 2 3 4 5 6
7
++Observed 9.50000 16.00000 18.00000 19.00000 30.00000 35.00000 31.00000
26.50000
++Fitted 9.52392 15.85637 23.13724 29.58961 33.16552 32.58022 28.05054
21.16645
++ 8 9 10 11
++Observed 18.00000 13.000000 7.000000 2.00000
++Fitted 13.99829 8.113746 4.121808 1.83516
++
++> plot(mod.oct)
++> lines(mod.ll, line.col="blue3") # Different
++> ## Smoothed density
++> den <- density(log2(colSums(BCI)))
++> lines(den$x, ncol(BCI)*den$y, lwd=2) # Fairly similar to mod.oct
++> ## Extrapolated richness
++> veiledspec(mod.oct)
++Extrapolated Observed Veiled
++ 235.40577 225.00000 10.40577
++> veiledspec(mod.ll)
++Extrapolated Observed Veiled
++ 230.931018 225.000000 5.931018
++>
++>
++>
++> cleanEx()
++> nameEx("goodness.cca")
++> ### * goodness.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: goodness.cca
++> ### Title: Diagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA,
++> ### CA, PCA)
++> ### Aliases: goodness goodness.cca inertcomp spenvcor intersetcor vif.cca
++> ### alias.cca
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env)
++> goodness(mod, addprevious = TRUE)
++ CCA1 CCA2 CCA3 CCA4
++Achimill 0.36630013 0.3822685 0.3838616 0.4934158
++Agrostol 0.67247051 0.6724758 0.6779597 0.7773267
++Airaprae 0.36213737 0.3698100 0.3816619 0.3908018
++Alopgeni 0.61547145 0.6966105 0.7042650 0.7212918
++Anthodor 0.24619147 0.2795001 0.3509172 0.3609709
++Bellpere 0.41185412 0.4179432 0.4847618 0.4849622
++Bromhord 0.33487622 0.3397416 0.3870032 0.5505037
++Chenalbu 0.23594716 0.2684323 0.2828928 0.2885321
++Cirsarve 0.29041563 0.3013655 0.3080671 0.3591280
++Comapalu 0.16338257 0.6836790 0.7390659 0.7963425
++Eleopalu 0.55132024 0.6099415 0.6193301 0.6259818
++Elymrepe 0.25239595 0.2710266 0.2761491 0.2882666
++Empenigr 0.27089495 0.3132399 0.3153052 0.3154203
++Hyporadi 0.31349648 0.3371809 0.3387669 0.3388716
++Juncarti 0.43923609 0.4492937 0.4871043 0.5224072
++Juncbufo 0.70439967 0.7226263 0.7228786 0.7257471
++Lolipere 0.48141171 0.5720410 0.5727299 0.6034007
++Planlanc 0.54969676 0.6084389 0.6802195 0.6826265
++Poaprat 0.40267189 0.4944813 0.5014516 0.5326546
++Poatriv 0.49694972 0.5409439 0.5468830 0.5594817
++Ranuflam 0.68677962 0.6983001 0.7020461 0.7064850
++Rumeacet 0.44788204 0.5211145 0.7673956 0.7691199
++Sagiproc 0.27039747 0.3497634 0.3553109 0.3613746
++Salirepe 0.64788354 0.7264891 0.7276110 0.7639711
++Scorautu 0.54312496 0.5510319 0.6078931 0.6140593
++Trifprat 0.37328840 0.4101104 0.6624199 0.6625703
++Trifrepe 0.03048149 0.2115857 0.3300132 0.4207437
++Vicilath 0.17824132 0.1784611 0.3762406 0.4279428
++Bracruta 0.15585567 0.1641095 0.1672797 0.2449864
++Callcusp 0.30771429 0.3143582 0.3308502 0.3518027
++> goodness(mod, addprevious = TRUE, summ = TRUE)
++ Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord
Chenalbu
++0.4934158 0.7773267 0.3908018 0.7212918 0.3609709 0.4849622 0.5505037
0.2885321
++ Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti
Juncbufo
++0.3591280 0.7963425 0.6259818 0.2882666 0.3154203 0.3388716 0.5224072
0.7257471
++ Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc
Salirepe
++0.6034007 0.6826265 0.5326546 0.5594817 0.7064850 0.7691199 0.3613746
0.7639711
++ Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp
++0.6140593 0.6625703 0.4207437 0.4279428 0.2449864 0.3518027
++> # Inertia components
++> inertcomp(mod, prop = TRUE)
++ pCCA CCA CA
++Achimill 0.34271900 0.15069678 0.5065842
++Agrostol 0.55602406 0.22130269 0.2226733
++Airaprae 0.06404726 0.32675457 0.6091982
++Alopgeni 0.34238968 0.37890210 0.2787082
++Anthodor 0.10259139 0.25837947 0.6390291
++Bellpere 0.40972447 0.07523776 0.5150378
++Bromhord 0.33046684 0.22003683 0.4494963
++Chenalbu 0.11064346 0.17788865 0.7114679
++Cirsarve 0.26649913 0.09262886 0.6408720
++Comapalu 0.16096277 0.63537969 0.2036575
++Eleopalu 0.53954819 0.08643366 0.3740182
++Elymrepe 0.22234322 0.06592337 0.7117334
++Empenigr 0.10361994 0.21180040 0.6845797
++Hyporadi 0.03889627 0.29997533 0.6611284
++Juncarti 0.43439190 0.08801527 0.4775928
++Juncbufo 0.66622672 0.05952038 0.2742529
++Lolipere 0.46273045 0.14067027 0.3965993
++Planlanc 0.51993753 0.16268893 0.3173735
++Poaprat 0.39408053 0.13857406 0.4673454
++Poatriv 0.05598349 0.50349824 0.4405183
++Ranuflam 0.68509904 0.02138594 0.2935150
++Rumeacet 0.40125987 0.36786003 0.2308801
++Sagiproc 0.26050435 0.10087025 0.6386254
++Salirepe 0.12527838 0.63869277 0.2360289
++Scorautu 0.10895437 0.50510492 0.3859407
++Trifprat 0.34544815 0.31712212 0.3374297
++Trifrepe 0.02132183 0.39942191 0.5792563
++Vicilath 0.12125433 0.30668844 0.5720572
++Bracruta 0.07222706 0.17275938 0.7550136
++Callcusp 0.29447422 0.05732850 0.6481973
++> inertcomp(mod, stat="d")
++ pCCA CCA CA
++Achimill 0.74393575 0.32711556 1.0996359
++Agrostol 0.65154706 0.25932172 0.2609277
++Airaprae 0.91341031 4.66001211 8.6880830
++Alopgeni 0.62026287 0.68640768 0.5048995
++Anthodor 0.31403667 0.79091068 1.9560957
++Bellpere 0.81483759 0.14962874 1.0242789
++Bromhord 0.82257902 0.54770300 1.1188603
++Chenalbu 2.18604651 3.51464840 14.0568809
++Cirsarve 3.79020979 1.31738817 9.1146243
++Comapalu 2.18604651 8.62913533 2.7658870
++Eleopalu 2.18604651 0.35019669 1.5153810
++Elymrepe 0.51093664 0.15148951 1.6355375
++Empenigr 2.18604651 4.46830527 14.4424224
++Hyporadi 0.35520990 2.73944513 6.0375798
++Juncarti 1.36667524 0.27691192 1.5025932
++Juncbufo 2.60346194 0.23259205 1.0717177
++Lolipere 0.43502729 0.13224850 0.3728553
++Planlanc 0.96463794 0.30183609 0.5888218
++Poaprat 0.20406096 0.07175578 0.2419987
++Poatriv 0.03136359 0.28207442 0.2467912
++Ranuflam 2.18604651 0.06823926 0.9365617
++Rumeacet 1.09684464 1.00554611 0.6311112
++Sagiproc 0.51772816 0.20047024 1.2692085
++Salirepe 0.88898328 4.53220433 1.6748757
++Scorautu 0.03887246 0.18021004 0.1376949
++Trifprat 1.74000000 1.59732360 1.6996117
++Trifrepe 0.01215418 0.22768437 0.3301962
++Vicilath 0.84063811 2.12622500 3.9659871
++Bracruta 0.04517898 0.10806328 0.4722710
++Callcusp 2.18604651 0.42558147 4.8119302
++> # vif.cca
++> vif.cca(mod)
++ Moisture.L Moisture.Q Moisture.C A1 ManagementHF ManagementNM
++ 1.504327 1.284489 1.347660 1.367328 2.238653 2.570972
++ManagementSF
++ 2.424444
++> # Aliased constraints
++> mod <- cca(dune ~ ., dune.env)
++> mod
++Call: cca(formula = dune ~ A1 + Moisture + Management + Use + Manure,
++data = dune.env)
++
++ Inertia Proportion Rank
++Total 2.1153 1.0000
++Constrained 1.5032 0.7106 12
++Unconstrained 0.6121 0.2894 7
++Inertia is mean squared contingency coefficient
++Some constraints were aliased because they were collinear (redundant)
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10 CCA11
++0.4671 0.3410 0.1761 0.1532 0.0953 0.0703 0.0589 0.0499 0.0318 0.0260 0.0228
++ CCA12
++0.0108
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7
++0.27237 0.10876 0.08975 0.06305 0.03489 0.02529 0.01798
++
++> vif.cca(mod)
++ A1 Moisture.L Moisture.Q Moisture.C ManagementHF ManagementNM
++ 2.208249 2.858927 3.072715 3.587087 6.608315 142.359372
++ManagementSF Use.L Use.Q Manure.L Manure.Q Manure.C
++ 12.862713 2.642718 3.007238 80.828330 49.294455 21.433337
++ Manure^4
++ NA
++> alias(mod)
++Model :
++dune ~ A1 + Moisture + Management + Use + Manure
++
++Complete :
++ A1 Moisture.L Moisture.Q Moisture.C ManagementHF ManagementNM
++Manure^4 8.366600
++ ManagementSF Use.L Use.Q Manure.L Manure.Q Manure.C
++Manure^4 5.291503 -4.472136 2.645751
++
++> with(dune.env, table(Management, Manure))
++ Manure
++Management 0 1 2 3 4
++ BF 0 2 1 0 0
++ HF 0 1 2 2 0
++ NM 6 0 0 0 0
++ SF 0 0 1 2 3
++> # The standard correlations (not recommended)
++> spenvcor(mod)
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
CCA8
++0.9636021 0.9486511 0.9330434 0.8734524 0.9374495 0.8362375 0.9748848
0.8392780
++ CCA9 CCA10 CCA11 CCA12
++0.8747389 0.6096594 0.6637148 0.7579916
++> intersetcor(mod)
++ CCA1 CCA2 CCA3 CCA4 CCA5
++A1 -0.5321996 -0.13912207 -0.48150939 -0.259433707 0.09911786
++Moisture.L -0.8785172 -0.18040189 0.03644505 0.182602811 0.09837305
++Moisture.Q -0.1946874 0.32977037 -0.27391819 -0.180009911 -0.26613363
++Moisture.C -0.2019848 0.09650383 0.28577179 -0.261556870 0.49109525
++ManagementHF 0.3451922 -0.01403625 -0.51180235 0.193533258 -0.30824886
++ManagementNM -0.5683132 0.61018732 0.14614372 -0.012375462 -0.04575256
++ManagementSF -0.1197518 -0.64162158 0.19778688 0.134961914 0.09680582
++Use.L -0.1875733 -0.33013591 -0.30944066 -0.372831885 -0.09587365
++Use.Q -0.1814760 0.48858573 -0.02033145 -0.009560403 -0.04810528
++Manure.L 0.3156026 -0.65867454 0.03849174 -0.026284481 0.04074238
++Manure.Q -0.4052775 0.20889368 0.49284523 -0.175984063 -0.11995731
++Manure.C 0.4667289 -0.11239457 0.29234452 -0.174073605 -0.14234406
++Manure^4 0.2220470 0.12845556 -0.12903663 0.108230657 0.02558063
++ CCA6 CCA7 CCA8 CCA9 CCA10
++A1 -0.15308984 -0.25797100 -0.19234827 0.27570098 -0.116893686
++Moisture.L -0.02975258 -0.07850852 0.10802259 -0.07239493 0.094640487
++Moisture.Q -0.11254617 -0.05055234 0.48336992 -0.06243695 -0.205959907
++Moisture.C -0.23606190 0.38701844 0.10155264 0.21874286 0.187345861
++ManagementHF -0.24233160 -0.16420200 0.14035917 -0.30903312 0.132635464
++ManagementNM -0.06594907 -0.23981367 -0.14368858 0.26879033 -0.001606144
++ManagementSF -0.01616968 0.49727982 -0.08071454 0.30247100 -0.138224363
++Use.L 0.19132865 0.44624441 0.18449673 -0.12952535 0.045337916
++Use.Q 0.13464687 -0.10362907 0.11033206 -0.41344859 -0.077048641
++Manure.L -0.22218417 0.49678602 0.16957558 0.04161905 -0.003418114
++Manure.Q -0.19580363 0.12001390 -0.17608393 0.43863133 0.089074448
++Manure.C 0.14824215 -0.07859941 -0.37823472 -0.10058265 0.106319365
++Manure^4 -0.36675058 -0.05957188 -0.40937365 0.06097317 -0.149858571
++ CCA11 CCA12
++A1 0.03742925 0.08998648
++Moisture.L -0.06274844 0.08674397
++Moisture.Q 0.21943291 -0.16867562
++Moisture.C -0.13658737 0.14298295
++ManagementHF -0.17603113 -0.13480899
++ManagementNM 0.01749194 -0.09355925
++ManagementSF 0.01483251 0.06624422
++Use.L 0.08583483 -0.32574274
++Use.Q -0.41869776 -0.04845019
++Manure.L -0.02629234 -0.13200710
++Manure.Q -0.12708874 -0.06972694
++Manure.C -0.05332901 0.41503090
++Manure^4 0.41602035 -0.01685222
++>
++>
++>
++> cleanEx()
++> nameEx("goodness.metaMDS")
++> ### * goodness.metaMDS
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: goodness.metaMDS
++> ### Title: Goodness of Fit and Shepard Plot for Nonmetric Multidimensional
++> ### Scaling
++> ### Aliases: goodness.metaMDS goodness.monoMDS stressplot
++> ### stressplot.default stressplot.monoMDS
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> mod <- metaMDS(varespec)
++Square root transformation
++Wisconsin double standardization
++Run 0 stress 0.1843196
++Run 1 stress 0.2455912
++Run 2 stress 0.2169407
++Run 3 stress 0.2313231
++Run 4 stress 0.1974406
++Run 5 stress 0.1858402
++Run 6 stress 0.1948414
++Run 7 stress 0.2265717
++Run 8 stress 0.222507
++Run 9 stress 0.2023215
++Run 10 stress 0.2673177
++Run 11 stress 0.1976151
++Run 12 stress 0.1852397
++Run 13 stress 0.2341085
++Run 14 stress 0.1955867
++Run 15 stress 0.2137409
++Run 16 stress 0.2109638
++Run 17 stress 0.1825658
++... New best solution
++... Procrustes: rmse 0.04169825 max resid 0.1521436
++Run 18 stress 0.1843197
++Run 19 stress 0.2570119
++Run 20 stress 0.3760596
++*** No convergence -- monoMDS stopping criteria:
++ 20: stress ratio > sratmax
++> stressplot(mod)
++> gof <- goodness(mod)
++> gof
++ [1] 0.02984480 0.03513719 0.04188277 0.04598591 0.04003418 0.03441705
++ [7] 0.03294386 0.03050002 0.03060565 0.02993891 0.03526891 0.02621276
++[13] 0.03831284 0.02980754 0.03370603 0.02225802 0.03561193 0.03505161
++[19] 0.06577544 0.03268484 0.03503582 0.02956571 0.05167231 0.04602312
++> plot(mod, display = "sites", type = "n")
++> points(mod, display = "sites", cex = 2*gof/mean(gof))
++>
++>
++>
++> cleanEx()
++> nameEx("humpfit")
++> ### * humpfit
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: humpfit
++> ### Title: No-interaction Model for Hump-backed Species Richness vs.
++> ### Biomass
++> ### Aliases: humpfit summary.humpfit lines.humpfit plot.humpfit
++> ### points.humpfit predict.humpfit profile.humpfit
++> ### Keywords: models regression nonlinear
++>
++> ### ** Examples
++>
++> ##
++> ## Data approximated from Al-Mufti et al. (1977)
++> ##
++>
++>
++>
++> cleanEx()
++> nameEx("indpower")
++> ### * indpower
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: indpower
++> ### Title: Indicator Power of Species
++> ### Aliases: indpower
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> ## IP values
++> ip <- indpower(dune)
++> ## and TIP values
++> diag(ip) <- NA
++> (TIP <- rowMeans(ip, na.rm=TRUE))
++i.Achimill i.Agrostol i.Airaprae i.Alopgeni i.Anthodor i.Bellpere i.Bromhord
++ 0.3186250 0.3342800 0.2168133 0.3416198 0.3567884 0.3432281 0.3665632
++i.Chenalbu i.Cirsarve i.Comapalu i.Eleopalu i.Elymrepe i.Empenigr i.Hyporadi
++ 0.2095044 0.2781640 0.1713273 0.2414787 0.3263516 0.2016196 0.2378197
++i.Juncarti i.Juncbufo i.Lolipere i.Planlanc i.Poaprat i.Poatriv i.Ranuflam
++ 0.2915850 0.3331330 0.3998442 0.3426064 0.4094319 0.3929520 0.2663080
++i.Rumeacet i.Sagiproc i.Salirepe i.Scorautu i.Trifprat i.Trifrepe i.Vicilath
++ 0.3484684 0.3788905 0.2898512 0.4362493 0.3145854 0.4503764 0.2605349
++i.Bracruta i.Callcusp
++ 0.4252676 0.2070766
++>
++> ## p value calculation for a species
++> ## from Halme et al. 2009
++> ## i is ID for the species
++> i <- 1
++> fun <- function(x, i) indpower(x)[i,-i]
++> ## 'c0' randomizes species occurrences
++> os <- oecosimu(dune, fun, "c0", i=i, nsimul=99)
++Warning in oecosimu(dune, fun, "c0", i = i, nsimul = 99) :
++ nullmodel transformed 'comm' to binary data
++> ## get z values from oecosimu output
++> z <- os$oecosimu$z
++> ## p-value
++> (p <- sum(z) / sqrt(length(z)))
++[1] -2.051592
++> ## 'heterogeneity' measure
++> (chi2 <- sum((z - mean(z))^2))
++[1] 100.6288
++> pchisq(chi2, df=length(z)-1)
++[1] 1
++> ## Halme et al.'s suggested output
++> out <- c(TIP=TIP[i],
+++ significance=p,
+++ heterogeneity=chi2,
+++ minIP=min(fun(dune, i=i)),
+++ varIP=sd(fun(dune, i=i)^2))
++> out
++TIP.i.Achimill significance heterogeneity minIP varIP
++ 0.3186250 -2.0515917 100.6288432 0.0000000 0.2142097
++>
++>
++>
++> cleanEx()
++> nameEx("isomap")
++> ### * isomap
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: isomap
++> ### Title: Isometric Feature Mapping Ordination
++> ### Aliases: isomap isomapdist plot.isomap summary.isomap
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## The following examples also overlay minimum spanning tree to
++> ## the graphics in red.
++> op <- par(mar=c(4,4,1,1)+0.2, mfrow=c(2,2))
++> data(BCI)
++> dis <- vegdist(BCI)
++> tr <- spantree(dis)
++> pl <- ordiplot(cmdscale(dis), main="cmdscale")
++Warning in ordiplot(cmdscale(dis), main = "cmdscale") :
++ Species scores not available
++> lines(tr, pl, col="red")
++> ord <- isomap(dis, k=3)
++> ord
++
++Isometric Feature Mapping (isomap)
++
++Call:
++isomap(dist = dis, k = 3)
++
++Distance method: bray shortest isomap
++Criterion: k = 3
++> pl <- plot(ord, main="isomap k=3")
++> lines(tr, pl, col="red")
++> pl <- plot(isomap(dis, k=5), main="isomap k=5")
++> lines(tr, pl, col="red")
++> pl <- plot(isomap(dis, epsilon=0.45), main="isomap epsilon=0.45")
++> lines(tr, pl, col="red")
++> par(op)
++> ## colour points and web by the dominant species
++> dom <- apply(BCI, 1, which.max)
++> ## need nine colours, but default palette has only eight
++> op <- palette(c(palette("default"), "sienna"))
++> plot(ord, pch = 16, col = dom, n.col = dom)
++> palette(op)
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("kendall.global")
++> ### * kendall.global
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: kendall.global
++> ### Title: Kendall coefficient of concordance
++> ### Aliases: kendall.global kendall.post
++> ### Keywords: multivariate nonparametric
++>
++> ### ** Examples
++>
++> data(mite)
++> mite.hel <- decostand(mite, "hel")
++>
++> # Reproduce the results shown in Table 2 of Legendre (2005), a single group
++> mite.small <- mite.hel[c(4,9,14,22,31,34,45,53,61,69),c(13:15,23)]
++> kendall.global(mite.small, nperm=49)
++$Concordance_analysis
++ Group.1
++W 0.44160305
++F 2.37252221
++Prob.F 0.04403791
++Chi2 15.89770992
++Prob.perm 0.04000000
++
++attr(,"class")
++[1] "kendall.global"
++> kendall.post(mite.small, mult="holm", nperm=49)
++$A_posteriori_tests
++ TVEL ONOV SUCT Trhypch1
++Spearman.mean 0.3265678 0.3965503 0.4570402 -0.1681251
++W.per.species 0.4949258 0.5474127 0.5927802 0.1239061
++Prob 0.1400000 0.0200000 0.0200000 0.7200000
++Corrected prob 0.2800000 0.0800000 0.0800000 0.7200000
++
++$Correction.type
++[1] "holm"
++
++attr(,"class")
++[1] "kendall.post"
++>
++> # Reproduce the results shown in Tables 3 and 4 of Legendre (2005), 2 groups
++> group
<-c(1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,2,1,2,1,1,1,1,2,1,2,1,1,1,1,1,2,2,2,2,2)
++> kendall.global(mite.hel, group=group, nperm=49)
++$Concordance_analysis
++ Group.1 Group.2
++W 3.097870e-01 2.911888e-01
++F 1.032305e+01 4.108130e+00
++Prob.F 1.177138e-85 4.676566e-22
++Corrected prob.F 2.354275e-85 4.676566e-22
++Chi2 5.130073e+02 2.210123e+02
++Prob.perm 2.000000e-02 2.000000e-02
++Corrected prob.perm 4.000000e-02 4.000000e-02
++
++$Correction.type
++[1] "holm"
++
++attr(,"class")
++[1] "kendall.global"
++> kendall.post(mite.hel, group=group, mult="holm", nperm=49)
++$A_posteriori_tests_Group
++$A_posteriori_tests_Group[[1]]
++ Brachy PHTH RARD SSTR Protopl MEGR
++Spearman.mean 0.1851177 0.4258111 0.359058 0.2505486 0.1802160 0.2833298
++W.per.species 0.2190711 0.4497357 0.385764 0.2817757 0.2143736 0.3131911
++Prob 0.0200000 0.0200000 0.020000 0.0200000 0.0200000 0.0200000
++Corrected prob 0.7000000 0.7000000 0.700000 0.7000000 0.7000000 0.7000000
++ MPRO HMIN HMIN2 NPRA TVEL ONOV
++Spearman.mean 0.09248024 0.2444656 0.4138494 0.1263751 0.4177343 0.3301159
++W.per.species 0.13029357 0.2759462 0.4382723 0.1627761 0.4419954 0.3580278
++Prob 0.22000000 0.0200000 0.0200000 0.0400000 0.0200000 0.0200000
++Corrected prob 0.70000000 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
++ SUCT Oribatl1 PWIL Galumna1 Stgncrs2 HRUF
++Spearman.mean 0.2185421 0.421216 0.2574779 0.4180699 0.3623428 0.1250230
++W.per.species 0.2511028 0.445332 0.2884163 0.4423170 0.3889118 0.1614804
++Prob 0.0200000 0.020000 0.0200000 0.0200000 0.0200000 0.0400000
++Corrected prob 0.7000000 0.700000 0.7000000 0.7000000 0.7000000 0.7000000
++ PPEL SLAT FSET Lepidzts Eupelops Miniglmn
++Spearman.mean 0.2188216 0.3016159 0.4217606 0.2577037 0.1108022 0.2301430
++W.per.species 0.2513707 0.3307153 0.4458539 0.2886327 0.1478521 0.2622203
++Prob 0.0200000 0.0200000 0.0200000 0.0200000 0.0600000 0.0200000
++Corrected prob 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
++
++$A_posteriori_tests_Group[[2]]
++ HPAV TVIE LCIL Ceratoz1 Trhypch1 NCOR
++Spearman.mean 0.1222579 0.2712078 0.1906408 0.1375601 0.1342409 0.3342345
++W.per.species 0.2020527 0.3374616 0.2642189 0.2159637 0.2129463 0.3947586
++Prob 0.0800000 0.0200000 0.0200000 0.0200000 0.0400000 0.0200000
++Corrected prob 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
++ LRUG PLAG2 Ceratoz3 Oppiminu Trimalc2
++Spearman.mean 0.3446561 0.1833099 0.3188922 0.1764232 0.2498877
++W.per.species 0.4042328 0.2575544 0.3808111 0.2512938 0.3180797
++Prob 0.0200000 0.0200000 0.0200000 0.0200000 0.0200000
++Corrected prob 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
++
++
++$Correction.type
++[1] "holm"
++
++attr(,"class")
++[1] "kendall.post"
++>
++> # NOTE: 'nperm' argument usually needs to be larger than 49.
++> # It was set to this low value for demonstration purposes.
++>
++>
++>
++> cleanEx()
++> nameEx("linestack")
++> ### * linestack
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: linestack
++> ### Title: Plots One-dimensional Diagrams without Overwriting Labels
++> ### Aliases: linestack
++> ### Keywords: hplot aplot
++>
++> ### ** Examples
++>
++> ## First DCA axis
++> data(dune)
++> ord <- decorana(dune)
++> linestack(scores(ord, choices=1, display="sp"))
++> linestack(scores(ord, choices=1, display="si"), side="left", add=TRUE)
++> title(main="DCA axis 1")
++>
++> ## Expressions as labels
++> N <- 10 # Number of sites
++> df <- data.frame(Ca = rlnorm(N, 2), NO3 = rlnorm(N, 4),
+++ SO4 = rlnorm(N, 10), K = rlnorm(N, 3))
++> ord <- rda(df, scale = TRUE)
++> ### vector of expressions for labels
++> labs <- expression(Ca^{2+phantom()},
+++ NO[3]^{-phantom()},
+++ SO[4]^{-2},
+++ K^{+phantom()})
++> scl <- "sites"
++> linestack(scores(ord, choices = 1, display = "species", scaling = scl),
+++ labels = labs, air = 2)
++> linestack(scores(ord, choices = 1, display = "site", scaling = scl),
+++ side = "left", add = TRUE)
++> title(main = "PCA axis 1")
++>
++>
++>
++> cleanEx()
++> nameEx("make.cepnames")
++> ### * make.cepnames
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: make.cepnames
++> ### Title: Abbreviates a Botanical or Zoological Latin Name into an
++> ### Eight-character Name
++> ### Aliases: make.cepnames
++> ### Keywords: character
++>
++> ### ** Examples
++>
++> make.cepnames(c("Aa maderoi", "Poa sp.", "Cladina rangiferina",
+++ "Cladonia cornuta", "Cladonia cornuta var. groenlandica",
+++ "Cladonia rangiformis", "Bryoerythrophyllum"))
++[1] "Aamade" "Poasp" "Cladrang" "Cladcorn" "Cladgroe"
++[6] "Cladrang.1" "Bryrythr"
++> data(BCI)
++> colnames(BCI) <- make.cepnames(colnames(BCI))
++>
++>
++>
++> cleanEx()
++> nameEx("mantel")
++> ### * mantel
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: mantel
++> ### Title: Mantel and Partial Mantel Tests for Dissimilarity Matrices
++> ### Aliases: mantel mantel.partial
++> ### Keywords: multivariate htest
++>
++> ### ** Examples
++>
++> ## Is vegetation related to environment?
++> data(varespec)
++> data(varechem)
++> veg.dist <- vegdist(varespec) # Bray-Curtis
++> env.dist <- vegdist(scale(varechem), "euclid")
++> mantel(veg.dist, env.dist)
++
++Mantel statistic based on Pearson's product-moment correlation
++
++Call:
++mantel(xdis = veg.dist, ydis = env.dist)
++
++Mantel statistic r: 0.3047
++ Significance: 0.001
++
++Upper quantiles of permutations (null model):
++ 90% 95% 97.5% 99%
++0.107 0.145 0.169 0.200
++Permutation: free
++Number of permutations: 999
++
++> mantel(veg.dist, env.dist, method="spear")
++
++Mantel statistic based on Spearman's rank correlation rho
++
++Call:
++mantel(xdis = veg.dist, ydis = env.dist, method = "spear")
++
++Mantel statistic r: 0.2838
++ Significance: 0.001
++
++Upper quantiles of permutations (null model):
++ 90% 95% 97.5% 99%
++0.108 0.141 0.169 0.196
++Permutation: free
++Number of permutations: 999
++
++>
++>
++>
++> cleanEx()
++> nameEx("mantel.correlog")
++> ### * mantel.correlog
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: mantel.correlog
++> ### Title: Mantel Correlogram
++> ### Aliases: mantel.correlog plot.mantel.correlog
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> # Mite data available in "vegan"
++> data(mite)
++> data(mite.xy)
++> mite.hel <- decostand(mite, "hellinger")
++>
++> # Detrend the species data by regression on the site coordinates
++> mite.hel.resid <- resid(lm(as.matrix(mite.hel) ~ ., data=mite.xy))
++>
++> # Compute the detrended species distance matrix
++> mite.hel.D <- dist(mite.hel.resid)
++>
++> # Compute Mantel correlogram with cutoff, Pearson statistic
++> mite.correlog <- mantel.correlog(mite.hel.D, XY=mite.xy, nperm=49)
++> summary(mite.correlog)
++ Length Class Mode
++mantel.res 65 -none- numeric
++n.class 1 -none- numeric
++break.pts 14 -none- numeric
++mult 1 -none- character
++n.tests 1 -none- numeric
++call 4 -none- call
++> mite.correlog
++
++Mantel Correlogram Analysis
++
++Call:
++
++mantel.correlog(D.eco = mite.hel.D, XY = mite.xy, nperm = 49)
++
++ class.index n.dist Mantel.cor Pr(Mantel) Pr(corrected)
++D.cl.1 0.514182 358.000000 0.135713 0.02 0.02 *
++D.cl.2 1.242546 650.000000 0.118174 0.02 0.04 *
++D.cl.3 1.970910 796.000000 0.037820 0.04 0.06 .
++D.cl.4 2.699274 696.000000 -0.098605 0.02 0.08 .
++D.cl.5 3.427638 500.000000 -0.112682 0.02 0.10 .
++D.cl.6 4.156002 468.000000 -0.107603 0.02 0.12
++D.cl.7 4.884366 364.000000 -0.022264 0.16 0.16
++D.cl.8 5.612730 326.000000 NA NA NA
++D.cl.9 6.341094 260.000000 NA NA NA
++D.cl.10 7.069458 184.000000 NA NA NA
++D.cl.11 7.797822 130.000000 NA NA NA
++D.cl.12 8.526186 66.000000 NA NA NA
++D.cl.13 9.254550 32.000000 NA NA NA
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> # or: print(mite.correlog)
++> # or: print.mantel.correlog(mite.correlog)
++> plot(mite.correlog)
++>
++> # Compute Mantel correlogram without cutoff, Spearman statistic
++> mite.correlog2 <- mantel.correlog(mite.hel.D, XY=mite.xy, cutoff=FALSE,
+++ r.type="spearman", nperm=49)
++> summary(mite.correlog2)
++ Length Class Mode
++mantel.res 65 -none- numeric
++n.class 1 -none- numeric
++break.pts 14 -none- numeric
++mult 1 -none- character
++n.tests 1 -none- numeric
++call 6 -none- call
++> mite.correlog2
++
++Mantel Correlogram Analysis
++
++Call:
++
++mantel.correlog(D.eco = mite.hel.D, XY = mite.xy, cutoff = FALSE, r.type
= "spearman", nperm = 49)
++
++ class.index n.dist Mantel.cor Pr(Mantel) Pr(corrected)
++D.cl.1 0.514182 358.000000 0.134229 0.02 0.02 *
++D.cl.2 1.242546 650.000000 0.121270 0.02 0.04 *
++D.cl.3 1.970910 796.000000 0.035413 0.04 0.06 .
++D.cl.4 2.699274 696.000000 -0.095899 0.02 0.08 .
++D.cl.5 3.427638 500.000000 -0.118692 0.02 0.10 .
++D.cl.6 4.156002 468.000000 -0.117148 0.02 0.12
++D.cl.7 4.884366 364.000000 -0.031123 0.06 0.14
++D.cl.8 5.612730 326.000000 0.026064 0.06 0.16
++D.cl.9 6.341094 260.000000 0.050573 0.04 0.18
++D.cl.10 7.069458 184.000000 0.057017 0.02 0.20
++D.cl.11 7.797822 130.000000 0.036195 0.08 0.22
++D.cl.12 8.526186 66.000000 -0.054242 0.04 0.24
++D.cl.13 9.254550 32.000000 -0.066677 0.02 0.26
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> plot(mite.correlog2)
++>
++> # NOTE: 'nperm' argument usually needs to be larger than 49.
++> # It was set to this low value for demonstration purposes.
++>
++>
++>
++>
++> cleanEx()
++> nameEx("metaMDS")
++> ### * metaMDS
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: metaMDS
++> ### Title: Nonmetric Multidimensional Scaling with Stable Solution from
++> ### Random Starts, Axis Scaling and Species Scores
++> ### Aliases: metaMDS metaMDSdist metaMDSiter metaMDSredist initMDS postMDS
++> ### plot.metaMDS points.metaMDS text.metaMDS scores.metaMDS
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## The recommended way of running NMDS (Minchin 1987)
++> ##
++> data(dune)
++> # Global NMDS using monoMDS
++> sol <- metaMDS(dune)
++Run 0 stress 0.1192678
++Run 1 stress 0.1886532
++Run 2 stress 0.1192678
++... New best solution
++... Procrustes: rmse 4.497004e-05 max resid 0.0001297349
++... Similar to previous best
++Run 3 stress 0.1192678
++... New best solution
++... Procrustes: rmse 1.801542e-05 max resid 5.078152e-05
++... Similar to previous best
++Run 4 stress 0.1183186
++... New best solution
++... Procrustes: rmse 0.02025988 max resid 0.06488321
++Run 5 stress 0.1192685
++Run 6 stress 0.1939202
++Run 7 stress 0.1808919
++Run 8 stress 0.1183186
++... Procrustes: rmse 1.538285e-05 max resid 4.789705e-05
++... Similar to previous best
++Run 9 stress 0.1192679
++Run 10 stress 0.1183186
++... New best solution
++... Procrustes: rmse 1.244871e-05 max resid 3.745684e-05
++... Similar to previous best
++Run 11 stress 0.1192686
++Run 12 stress 0.1192683
++Run 13 stress 0.1886532
++Run 14 stress 0.1183186
++... Procrustes: rmse 2.260742e-06 max resid 5.293932e-06
++... Similar to previous best
++Run 15 stress 0.1192678
++Run 16 stress 0.1183186
++... Procrustes: rmse 3.225938e-06 max resid 7.314283e-06
++... Similar to previous best
++Run 17 stress 0.2075713
++Run 18 stress 0.1192678
++Run 19 stress 0.1192685
++Run 20 stress 0.1183186
++... Procrustes: rmse 2.295609e-05 max resid 6.384852e-05
++... Similar to previous best
++*** Solution reached
++> sol
++
++Call:
++metaMDS(comm = dune)
++
++global Multidimensional Scaling using monoMDS
++
++Data: dune
++Distance: bray
++
++Dimensions: 2
++Stress: 0.1183186
++Stress type 1, weak ties
++Two convergent solutions found after 20 tries
++Scaling: centring, PC rotation, halfchange scaling
++Species: expanded scores based on ‘dune’
++
++> plot(sol, type="t")
++> ## Start from previous best solution
++> sol <- metaMDS(dune, previous.best = sol)
++Starting from 2-dimensional configuration
++Run 0 stress 0.1183186
++Run 1 stress 0.1183186
++... Procrustes: rmse 1.39695e-05 max resid 4.609836e-05
++... Similar to previous best
++Run 2 stress 0.181294
++Run 3 stress 0.1183186
++... Procrustes: rmse 1.222929e-05 max resid 3.671397e-05
++... Similar to previous best
++Run 4 stress 0.1192678
++Run 5 stress 0.1192679
++Run 6 stress 0.1183186
++... Procrustes: rmse 3.046112e-05 max resid 9.621436e-05
++... Similar to previous best
++Run 7 stress 0.1183186
++... New best solution
++... Procrustes: rmse 8.248765e-06 max resid 2.709666e-05
++... Similar to previous best
++Run 8 stress 0.1192679
++Run 9 stress 0.1183186
++... Procrustes: rmse 4.987329e-05 max resid 0.0001617922
++... Similar to previous best
++Run 10 stress 0.1183186
++... Procrustes: rmse 6.258948e-05 max resid 0.0001841984
++... Similar to previous best
++Run 11 stress 0.1183186
++... Procrustes: rmse 2.298969e-05 max resid 7.777073e-05
++... Similar to previous best
++Run 12 stress 0.1183186
++... Procrustes: rmse 9.953516e-05 max resid 0.0002971112
++... Similar to previous best
++Run 13 stress 0.1192679
++Run 14 stress 0.1183186
++... Procrustes: rmse 0.0001064944 max resid 0.0003485089
++... Similar to previous best
++Run 15 stress 0.1192678
++Run 16 stress 0.1183187
++... Procrustes: rmse 6.269441e-05 max resid 0.0001989035
++... Similar to previous best
++Run 17 stress 0.1192685
++Run 18 stress 0.1192679
++Run 19 stress 0.1192683
++Run 20 stress 0.1809581
++*** Solution reached
++> ## Local NMDS and stress 2 of monoMDS
++> sol2 <- metaMDS(dune, model = "local", stress=2)
++Run 0 stress 0.1928489
++Run 1 stress 0.1928478
++... New best solution
++... Procrustes: rmse 0.000227748 max resid 0.0006739712
++... Similar to previous best
++Run 2 stress 0.1928479
++... Procrustes: rmse 0.0005683554 max resid 0.001654113
++... Similar to previous best
++Run 3 stress 0.1928529
++... Procrustes: rmse 0.001497169 max resid 0.004237256
++... Similar to previous best
++Run 4 stress 0.1928479
++... Procrustes: rmse 0.0002205786 max resid 0.000527516
++... Similar to previous best
++Run 5 stress 0.1928542
++... Procrustes: rmse 0.001703784 max resid 0.00489439
++... Similar to previous best
++Run 6 stress 0.192857
++... Procrustes: rmse 0.002035406 max resid 0.005757312
++... Similar to previous best
++Run 7 stress 0.1928513
++... Procrustes: rmse 0.0003555387 max resid 0.0009295202
++... Similar to previous best
++Run 8 stress 0.1928514
++... Procrustes: rmse 0.00131401 max resid 0.003754444
++... Similar to previous best
++Run 9 stress 0.1928527
++... Procrustes: rmse 0.001508577 max resid 0.004304102
++... Similar to previous best
++Run 10 stress 0.1928516
++... Procrustes: rmse 0.001200753 max resid 0.003381483
++... Similar to previous best
++Run 11 stress 0.1928477
++... New best solution
++... Procrustes: rmse 0.0002147781 max resid 0.0005379322
++... Similar to previous best
++Run 12 stress 0.1928476
++... New best solution
++... Procrustes: rmse 8.797899e-05 max resid 0.0002368433
++... Similar to previous best
++Run 13 stress 0.1928518
++... Procrustes: rmse 0.001194318 max resid 0.003382898
++... Similar to previous best
++Run 14 stress 0.1928479
++... Procrustes: rmse 0.0001251706 max resid 0.0003371929
++... Similar to previous best
++Run 15 stress 0.1928498
++... Procrustes: rmse 0.000713671 max resid 0.002074812
++... Similar to previous best
++Run 16 stress 0.1928581
++... Procrustes: rmse 0.001956972 max resid 0.005484317
++... Similar to previous best
++Run 17 stress 0.1928499
++... Procrustes: rmse 0.0008529383 max resid 0.002406656
++... Similar to previous best
++Run 18 stress 0.1928478
++... Procrustes: rmse 0.0003445826 max resid 0.0009688855
++... Similar to previous best
++Run 19 stress 0.1928493
++... Procrustes: rmse 0.0005050439 max resid 0.001350483
++... Similar to previous best
++Run 20 stress 0.1928475
++... New best solution
++... Procrustes: rmse 4.656629e-05 max resid 0.0001065303
++... Similar to previous best
++*** Solution reached
++> sol2
++
++Call:
++metaMDS(comm = dune, model = "local", stress = 2)
++
++local Multidimensional Scaling using monoMDS
++
++Data: dune
++Distance: bray
++
++Dimensions: 2
++Stress: 0.1928475
++Stress type 2, weak ties
++Two convergent solutions found after 20 tries
++Scaling: centring, PC rotation, halfchange scaling
++Species: expanded scores based on ‘dune’
++
++> ## Use Arrhenius exponent 'z' as a binary dissimilarity measure
++> sol <- metaMDS(dune, distfun = betadiver, distance = "z")
++Run 0 stress 0.1067169
++Run 1 stress 0.1073148
++Run 2 stress 0.1073148
++Run 3 stress 0.1067169
++... New best solution
++... Procrustes: rmse 9.860777e-06 max resid 3.045935e-05
++... Similar to previous best
++Run 4 stress 0.1067169
++... Procrustes: rmse 2.720552e-05 max resid 7.599563e-05
++... Similar to previous best
++Run 5 stress 0.1073148
++Run 6 stress 0.1653581
++Run 7 stress 0.1073148
++Run 8 stress 0.1067169
++... Procrustes: rmse 1.047284e-05 max resid 2.581037e-05
++... Similar to previous best
++Run 9 stress 0.1073148
++Run 10 stress 0.1073148
++Run 11 stress 0.1067169
++... Procrustes: rmse 4.569546e-05 max resid 9.658124e-05
++... Similar to previous best
++Run 12 stress 0.107471
++Run 13 stress 0.107471
++Run 14 stress 0.1073148
++Run 15 stress 0.107471
++Run 16 stress 0.1073148
++Run 17 stress 0.1069788
++... Procrustes: rmse 0.006831076 max resid 0.0241299
++Run 18 stress 0.1067169
++... New best solution
++... Procrustes: rmse 4.325792e-06 max resid 1.459835e-05
++... Similar to previous best
++Run 19 stress 0.1067169
++... Procrustes: rmse 1.880583e-05 max resid 4.343547e-05
++... Similar to previous best
++Run 20 stress 0.1067169
++... New best solution
++... Procrustes: rmse 1.608418e-06 max resid 3.595822e-06
++... Similar to previous best
++*** Solution reached
++> sol
++
++Call:
++metaMDS(comm = dune, distance = "z", distfun = betadiver)
++
++global Multidimensional Scaling using monoMDS
++
++Data: dune
++Distance: beta.z
++
++Dimensions: 2
++Stress: 0.1067169
++Stress type 1, weak ties
++Two convergent solutions found after 20 tries
++Scaling: centring, PC rotation, halfchange scaling
++Species: expanded scores based on ‘dune’
++
++>
++>
++>
++> cleanEx()
++> nameEx("mite")
++> ### * mite
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: mite
++> ### Title: Oribatid Mite Data with Explanatory Variables
++> ### Aliases: mite mite.env mite.pcnm mite.xy
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(mite)
++>
++>
++>
++> cleanEx()
++> nameEx("model.matrix.cca")
++> ### * model.matrix.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: model.matrix.cca
++> ### Title: Reconstruct Model Frame and Model Matrices of Constrained
++> ### Ordination
++> ### Aliases: model.matrix.cca model.frame.cca
++> ### Keywords: models multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune ~ poly(A1, 2) + Management + Use, dune.env)
++> model.frame(mod)
++ poly(A1, 2).1 poly(A1, 2).2 Management Use
++1 -0.21581339 0.31584574 SF Haypastu
++2 -0.14212101 0.10689011 BF Haypastu
++3 -0.05790115 -0.08310263 SF Haypastu
++4 -0.06842864 -0.06220100 SF Haypastu
++5 0.15264849 -0.33028722 HF Hayfield
++6 -0.05790115 -0.08310263 HF Haypastu
++7 -0.21581339 0.31584574 HF Pasture
++8 -0.06842864 -0.06220100 HF Pasture
++9 -0.12106605 0.05451056 HF Hayfield
++10 -0.16317598 0.16252391 BF Hayfield
++11 -0.14212101 0.10689011 BF Pasture
++12 0.10001108 -0.29899963 SF Haypastu
++13 0.12106605 -0.31395535 SF Haypastu
++14 0.46847296 -0.09089291 NM Pasture
++15 0.70007758 0.55002015 NM Haypastu
++16 0.08948360 -0.29030142 SF Pasture
++17 -0.08948360 -0.01795706 NM Hayfield
++18 -0.02631871 -0.14092613 NM Hayfield
++19 -0.12106605 0.05451056 NM Hayfield
++20 -0.14212101 0.10689011 NM Hayfield
++> model.matrix(mod)
++ poly(A1, 2)1 poly(A1, 2)2 ManagementHF ManagementNM ManagementSF
++1 -0.21581339 0.31584574 0 0 1
++2 -0.14212101 0.10689011 0 0 0
++3 -0.05790115 -0.08310263 0 0 1
++4 -0.06842864 -0.06220100 0 0 1
++5 0.15264849 -0.33028722 1 0 0
++6 -0.05790115 -0.08310263 1 0 0
++7 -0.21581339 0.31584574 1 0 0
++8 -0.06842864 -0.06220100 1 0 0
++9 -0.12106605 0.05451056 1 0 0
++10 -0.16317598 0.16252391 0 0 0
++11 -0.14212101 0.10689011 0 0 0
++12 0.10001108 -0.29899963 0 0 1
++13 0.12106605 -0.31395535 0 0 1
++14 0.46847296 -0.09089291 0 1 0
++15 0.70007758 0.55002015 0 1 0
++16 0.08948360 -0.29030142 0 0 1
++17 -0.08948360 -0.01795706 0 1 0
++18 -0.02631871 -0.14092613 0 1 0
++19 -0.12106605 0.05451056 0 1 0
++20 -0.14212101 0.10689011 0 1 0
++ Use.L Use.Q
++1 -7.850462e-17 -0.8164966
++2 -7.850462e-17 -0.8164966
++3 -7.850462e-17 -0.8164966
++4 -7.850462e-17 -0.8164966
++5 -7.071068e-01 0.4082483
++6 -7.850462e-17 -0.8164966
++7 7.071068e-01 0.4082483
++8 7.071068e-01 0.4082483
++9 -7.071068e-01 0.4082483
++10 -7.071068e-01 0.4082483
++11 7.071068e-01 0.4082483
++12 -7.850462e-17 -0.8164966
++13 -7.850462e-17 -0.8164966
++14 7.071068e-01 0.4082483
++15 -7.850462e-17 -0.8164966
++16 7.071068e-01 0.4082483
++17 -7.071068e-01 0.4082483
++18 -7.071068e-01 0.4082483
++19 -7.071068e-01 0.4082483
++20 -7.071068e-01 0.4082483
++>
++>
++>
++> cleanEx()
++> nameEx("monoMDS")
++> ### * monoMDS
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: monoMDS
++> ### Title: Global and Local Non-metric Multidimensional Scaling and Linear
++> ### and Hybrid Scaling
++> ### Aliases: monoMDS scores.monoMDS plot.monoMDS
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> dis <- vegdist(dune)
++> m <- monoMDS(dis, model = "loc")
++> m
++
++Call:
++monoMDS(dist = dis, model = "loc")
++
++Local non-metric Multidimensional Scaling
++
++20 points, dissimilarity ‘bray’, call ‘vegdist(x = dune)’
++
++Dimensions: 2
++Stress: 0.1238005
++Stress type 1, weak ties
++Scores scaled to unit root mean square, rotated to principal components
++Stopped after 73 iterations: Stress nearly unchanged (ratio > sratmax)
++> plot(m)
++>
++>
++>
++> cleanEx()
++> nameEx("mrpp")
++> ### * mrpp
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: mrpp
++> ### Title: Multi Response Permutation Procedure and Mean Dissimilarity
++> ### Matrix
++> ### Aliases: mrpp meandist summary.meandist plot.meandist
++> ### Keywords: multivariate nonparametric htest
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> dune.mrpp <- with(dune.env, mrpp(dune, Management))
++> dune.mrpp
++
++Call:
++mrpp(dat = dune, grouping = Management)
++
++Dissimilarity index: euclidean
++Weights for groups: n
++
++Class means and counts:
++
++ BF HF NM SF
++delta 10.03 11.08 10.66 12.27
++n 3 5 6 6
++
++Chance corrected within-group agreement A: 0.1246
++Based on observed delta 11.15 and expected delta 12.74
++
++Significance of delta: 0.002
++Permutation: free
++Number of permutations: 999
++
++>
++> # Save and change plotting parameters
++> def.par <- par(no.readonly = TRUE)
++> layout(matrix(1:2,nr=1))
++>
++> plot(dune.ord <- metaMDS(dune), type="text", display="sites" )
++Run 0 stress 0.1192678
++Run 1 stress 0.2059799
++Run 2 stress 0.1192678
++... New best solution
++... Procrustes: rmse 3.906075e-05 max resid 0.0001192009
++... Similar to previous best
++Run 3 stress 0.1192679
++... Procrustes: rmse 0.0001096816 max resid 0.0003383007
++... Similar to previous best
++Run 4 stress 0.2375284
++Run 5 stress 0.1183186
++... New best solution
++... Procrustes: rmse 0.02027545 max resid 0.0650119
++Run 6 stress 0.1192678
++Run 7 stress 0.1192678
++Run 8 stress 0.2069991
++Run 9 stress 0.1183186
++... Procrustes: rmse 0.0001003641 max resid 0.0002670036
++... Similar to previous best
++Run 10 stress 0.1922258
++Run 11 stress 0.1192682
++Run 12 stress 0.1192683
++Run 13 stress 0.1809577
++Run 14 stress 0.1886532
++Run 15 stress 0.1192679
++Run 16 stress 0.1192679
++Run 17 stress 0.1192679
++Run 18 stress 0.1192678
++Run 19 stress 0.1183186
++... Procrustes: rmse 0.0001010439 max resid 0.00034752
++... Similar to previous best
++Run 20 stress 0.1192687
++*** Solution reached
++> with(dune.env, ordihull(dune.ord, Management))
++>
++> with(dune.mrpp, {
+++ fig.dist <- hist(boot.deltas, xlim=range(c(delta,boot.deltas)),
+++ main="Test of Differences Among Groups")
+++ abline(v=delta);
+++ text(delta, 2*mean(fig.dist$counts), adj = -0.5,
+++ expression(bold(delta)), cex=1.5 ) }
+++ )
++> par(def.par)
++> ## meandist
++> dune.md <- with(dune.env, meandist(vegdist(dune), Management))
++> dune.md
++ BF HF NM SF
++BF 0.4159972 0.4736637 0.7296979 0.6247169
++HF 0.4736637 0.4418115 0.7217933 0.5673664
++NM 0.7296979 0.7217933 0.6882438 0.7723367
++SF 0.6247169 0.5673664 0.7723367 0.5813015
++attr(,"class")
++[1] "meandist" "matrix"
++attr(,"n")
++grouping
++BF HF NM SF
++ 3 5 6 6
++> summary(dune.md)
++
++Mean distances:
++ Average
++within groups 0.5746346
++between groups 0.6664172
++overall 0.6456454
++
++Summary statistics:
++ Statistic
++MRPP A weights n 0.1423836
++MRPP A weights n-1 0.1339124
++MRPP A weights n(n-1) 0.1099842
++Classification strength 0.1127012
++> plot(dune.md)
++> plot(dune.md, kind="histogram")
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("mso")
++> ### * mso
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: mso
++> ### Title: Functions for performing and displaying a spatial partitioning
++> ### of cca or rda results
++> ### Aliases: mso msoplot
++> ### Keywords: spatial multivariate
++>
++> ### ** Examples
++>
++> ## Reconstruct worked example of Wagner (submitted):
++> X <- matrix(c(1, 2, 3, 2, 1, 0), 3, 2)
++> Y <- c(3, -1, -2)
++> tmat <- c(1:3)
++> ## Canonical correspondence analysis (cca):
++> Example.cca <- cca(X, Y)
++> Example.cca <- mso(Example.cca, tmat)
++Set of permutations < 'minperm'. Generating entire set.
++> msoplot(Example.cca)
++> Example.cca$vario
++ H Dist n All Sum CA CCA se
++1 1 1 2 0.25 0.3456633 0.07461735 0.2710459 0
++2 2 2 1 1.00 0.8086735 0.01147959 0.7971939 NA
++>
++> ## Correspondence analysis (ca):
++> Example.ca <- mso(cca(X), tmat)
++Set of permutations < 'minperm'. Generating entire set.
++> msoplot(Example.ca)
++>
++> ## Unconstrained ordination with test for autocorrelation
++> ## using oribatid mite data set as in Wagner (2004)
++> data(mite)
++> data(mite.env)
++> data(mite.xy)
++>
++> mite.cca <- cca(log(mite + 1))
++> mite.cca <- mso(mite.cca, mite.xy, grain = 1, permutations = 99)
++> msoplot(mite.cca)
++> mite.cca
++Call: mso(object.cca = mite.cca, object.xy = mite.xy, grain = 1,
++permutations = 99)
++
++ Inertia Rank
++Total 1.164
++Unconstrained 1.164 34
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
++0.3662 0.1328 0.0723 0.0658 0.0559 0.0481 0.0418 0.0391
++(Showed only 8 of all 34 unconstrained eigenvalues)
++
++mso variogram:
++
++ H Dist n All CA CA.signif
++0 0 0.3555 63 0.6250 0.6250 0.01
++1 1 1.0659 393 0.7556 0.7556 0.01
++2 2 2.0089 534 0.8931 0.8931 0.01
++3 3 2.9786 417 1.0988 1.0988 0.03
++4 4 3.9817 322 1.3321 1.3321 0.01
++5 5 5.0204 245 1.5109 1.5109 0.01
++10 10 6.8069 441 1.7466 1.7466 0.01
++
++Permutation: free
++Number of permutations: 99
++
++>
++> ## Constrained ordination with test for residual autocorrelation
++> ## and scale-invariance of species-environment relationships
++> mite.cca <- cca(log(mite + 1) ~ SubsDens + WatrCont + Substrate + Shrub +
Topo, mite.env)
++> mite.cca <- mso(mite.cca, mite.xy, permutations = 99)
++> msoplot(mite.cca)
++Error variance of regression model underestimated by 0.4 percent
++> mite.cca
++Call: mso(object.cca = mite.cca, object.xy = mite.xy, permutations =
++99)
++
++ Inertia Proportion Rank
++Total 1.1638 1.0000
++Constrained 0.5211 0.4478 11
++Unconstrained 0.6427 0.5522 34
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9
CCA10
++0.31207 0.06601 0.04117 0.02938 0.02438 0.01591 0.01201 0.00752 0.00612
0.00373
++ CCA11
++0.00284
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
++0.07888 0.06752 0.05457 0.04023 0.03855 0.03491 0.03233 0.02692
++(Showed only 8 of all 34 unconstrained eigenvalues)
++
++mso variogram:
++
++ H Dist n All Sum CA CCA se CA.signif
++0 0 0.3555 63 0.6250 0.7479 0.5512 0.1967 0.03506 0.01
++1 1 1.0659 393 0.7556 0.8820 0.6339 0.2482 0.01573 0.17
++2 2 2.0089 534 0.8931 0.9573 0.6473 0.3100 0.01487 0.71
++3 3 2.9786 417 1.0988 1.1010 0.6403 0.4607 0.01858 0.46
++4 4 3.9817 322 1.3321 1.2548 0.6521 0.6027 0.02439 0.99
++5 5 5.0204 245 1.5109 1.4564 0.6636 0.7928 0.02801 0.42
++10 10 6.8069 441 1.7466 1.6266 0.6914 0.9351 0.02052 0.21
++
++Permutation: free
++Number of permutations: 99
++
++>
++>
++>
++> cleanEx()
++> nameEx("multipart")
++> ### * multipart
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: multipart
++> ### Title: Multiplicative Diversity Partitioning
++> ### Aliases: multipart multipart.default multipart.formula
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## NOTE: 'nsimul' argument usually needs to be >= 99
++> ## here much lower value is used for demonstration
++>
++> data(mite)
++> data(mite.xy)
++> data(mite.env)
++> ## Function to get equal area partitions of the mite data
++> cutter <- function (x, cut = seq(0, 10, by = 2.5)) {
+++ out <- rep(1, length(x))
+++ for (i in 2:(length(cut) - 1))
+++ out[which(x > cut[i] & x <= cut[(i + 1)])] <- i
+++ return(out)}
++> ## The hierarchy of sample aggregation
++> levsm <- with(mite.xy, data.frame(
+++ l1=1:nrow(mite),
+++ l2=cutter(y, cut = seq(0, 10, by = 2.5)),
+++ l3=cutter(y, cut = seq(0, 10, by = 5)),
+++ l4=cutter(y, cut = seq(0, 10, by = 10))))
++> ## Multiplicative diversity partitioning
++> multipart(mite, levsm, index="renyi", scales=1, nsimul=19)
++multipart object
++
++Call: multipart(y = mite, x = levsm, index = "renyi", scales = 1,
++nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++options: index renyi, scales 1, global FALSE
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++alpha.1 8.0555 -50.069 12.19396 12.06155 12.19041 12.3400 0.05 *
++alpha.2 11.2353 -91.662 14.09161 14.05255 14.08685 14.1485 0.05 *
++alpha.3 12.0064 -391.053 14.13939 14.13048 14.13932 14.1485 0.05 *
++gamma 14.1603 0.000 14.16027 14.16027 14.16027 14.1603 1.00
++beta.1 1.3568 27.320 1.15770 1.14474 1.15859 1.1683 0.05 *
++beta.2 1.0710 30.431 1.00339 0.99912 1.00340 1.0059 0.05 *
++beta.3 1.1794 460.550 1.00148 1.00083 1.00148 1.0021 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19)
++multipart object
++
++Call: multipart(formula = mite ~ ., data = levsm, index = "renyi",
++scales = 1, nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++options: index renyi, scales 1, global FALSE
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++alpha.1 8.0555 -66.643 12.2056 12.1143 12.2120 12.2910 0.05 *
++alpha.2 11.2353 -94.626 14.0818 14.0246 14.0917 14.1193 0.05 *
++alpha.3 12.0064 -355.962 14.1391 14.1283 14.1374 14.1485 0.05 *
++gamma 14.1603 0.000 14.1603 14.1603 14.1603 14.1603 1.00
++beta.1 1.3568 35.028 1.1566 1.1478 1.1565 1.1660 0.05 *
++beta.2 1.0710 33.423 1.0041 1.0015 1.0035 1.0078 0.05 *
++beta.3 1.1794 419.166 1.0015 1.0008 1.0016 1.0023 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19,
relative=TRUE)
++multipart object
++
++Call: multipart(formula = mite ~ ., data = levsm, index = "renyi",
++scales = 1, relative = TRUE, nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++options: index renyi, scales 1, global FALSE
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++alpha.1 8.055481 -53.544 12.204883 12.071168 12.198307 12.3689 0.05 *
++alpha.2 11.235261 -99.506 14.079276 14.032767 14.076147 14.1247 0.05 *
++alpha.3 12.006443 -343.946 14.135245 14.123604 14.136297 14.1438 0.05 *
++gamma 14.160271 0.000 14.160271 14.160271 14.160271 14.1603 1.00
++beta.1 0.078594 19.568 0.068267 0.067236 0.068392 0.0691 0.05 *
++beta.2 0.535514 35.966 0.501994 0.500294 0.502062 0.5035 0.05 *
++beta.3 0.589695 404.814 0.500885 0.500583 0.500848 0.5013 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, global=TRUE)
++multipart object
++
++Call: multipart(formula = mite ~ ., data = levsm, index = "renyi",
++scales = 1, global = TRUE, nsimul = 19)
++
++nullmodel method ‘r2dtable’ with 19 simulations
++options: index renyi, scales 1, global TRUE
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++alpha.1 8.0555 -69.630 12.19342 12.09434 12.18504 12.3160 0.05 *
++alpha.2 11.2353 -81.676 14.09224 14.05248 14.09208 14.1625 0.05 *
++alpha.3 12.0064 -321.419 14.13850 14.12429 14.13991 14.1456 0.05 *
++gamma 14.1603 0.000 14.16027 14.16027 14.16027 14.1603 1.00
++beta.1 1.7578 105.552 1.16133 1.14975 1.16210 1.1708 0.05 *
++beta.2 1.2603 102.695 1.00483 0.99985 1.00484 1.0077 0.05 *
++beta.3 1.1794 378.335 1.00154 1.00104 1.00144 1.0025 0.05 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("nestedtemp")
++> ### * nestedtemp
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: nestedtemp
++> ### Title: Nestedness Indices for Communities of Islands or Patches
++> ### Aliases: nestedtemp nestedchecker nestedn0 nesteddisc nestednodf
++> ### nestedbetasor nestedbetajac plot.nestedtemp plot.nestednodf
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++> data(sipoo)
++> ## Matrix temperature
++> out <- nestedtemp(sipoo)
++> out
++nestedness temperature: 10.24036
++with matrix fill 0.2233333
++> plot(out)
++> plot(out, kind="incid")
++> ## Use oecosimu to assess the non-randomness of checker board units
++> nestedchecker(sipoo)
++Checkerboard Units : 2767
++C-score (species mean): 2.258776
++> oecosimu(sipoo, nestedchecker, "quasiswap")
++oecosimu object
++
++Call: oecosimu(comm = sipoo, nestfun = nestedchecker, method =
++"quasiswap")
++
++nullmodel method ‘quasiswap’ with 99 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++Checkerboard Units : 2767
++C-score (species mean): 2.258776
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++checkerboards 2767 0.84129 2698.1 2584.4 2676.0 2858.1 0.39
++> ## Another Null model and standardized checkerboard score
++> oecosimu(sipoo, nestedchecker, "r00", statistic = "C.score")
++oecosimu object
++
++Call: oecosimu(comm = sipoo, nestfun = nestedchecker, method = "r00",
++statistic = "C.score")
++
++nullmodel method ‘r00’ with 99 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++Checkerboard Units : 2767
++C-score (species mean): 2.258776
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++C.score 2.2588 -28.92 9.2234 8.6935 9.2384 9.6053 0.01 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("nullmodel")
++> ### * nullmodel
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: nullmodel
++> ### Title: Null Model and Simulation
++> ### Aliases: nullmodel simmat print.nullmodel simulate.nullmodel
++> ### update.nullmodel str.nullmodel print.simmat smbind
++> ### Keywords: multivariate datagen
++>
++> ### ** Examples
++>
++> data(mite)
++> x <- as.matrix(mite)[1:12, 21:30]
++>
++> ## non-sequential nullmodel
++> (nm <- nullmodel(x, "r00"))
++An object of class “nullmodel”
++‘r00’ method (binary, non-sequential)
++12 x 10 matrix
++
++> (sm <- simulate(nm, nsim=10))
++An object of class “simmat”
++‘r00’ method (binary, non-sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++
++>
++> ## sequential nullmodel
++> (nm <- nullmodel(x, "swap"))
++An object of class “nullmodel”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Iterations = 0
++
++> (sm1 <- simulate(nm, nsim=10, thin=5))
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++Start = 5, End = 50, Thin = 5
++
++> (sm2 <- simulate(nm, nsim=10, thin=5))
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++Start = 55, End = 100, Thin = 5
++
++>
++> ## sequential nullmodel with burnin and extra updating
++> (nm <- nullmodel(x, "swap"))
++An object of class “nullmodel”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Iterations = 0
++
++> (sm1 <- simulate(nm, burnin=10, nsim=10, thin=5))
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++Start = 15, End = 60, Thin = 5
++
++> (sm2 <- simulate(nm, nsim=10, thin=5))
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++Start = 5, End = 50, Thin = 5
++
++>
++> ## sequential nullmodel with separate initial burnin
++> (nm <- nullmodel(x, "swap"))
++An object of class “nullmodel”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Iterations = 0
++
++> nm <- update(nm, nsim=10)
++> (sm2 <- simulate(nm, nsim=10, thin=5))
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++Start = 15, End = 60, Thin = 5
++
++>
++> ## combining multiple simmat objects
++>
++> ## stratification
++> nm1 <- nullmodel(x[1:6,], "r00")
++> sm1 <- simulate(nm1, nsim=10)
++> nm2 <- nullmodel(x[7:12,], "r00")
++> sm2 <- simulate(nm2, nsim=10)
++> smbind(sm1, sm2, MARGIN=1)
++An object of class “simmat”
++‘r00’ method (binary, non-sequential)
++12 x 10 matrix
++Number of permuted matrices = 10
++
++>
++> ## binding subsequent samples from sequential algorithms
++> ## start, end, thin retained
++> nm <- nullmodel(x, "swap")
++> nm <- update(nm, nsim=10)
++> sm1 <- simulate(nm, nsim=10, thin=5)
++> sm2 <- simulate(nm, nsim=20, thin=5)
++> sm3 <- simulate(nm, nsim=10, thin=5)
++> smbind(sm3, sm2, sm1, MARGIN=3)
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 40
++Start = 15, End = 210, Thin = 5
++
++>
++> ## 'replicate' based usage which is similar to the output
++> ## of 'parLapply' or 'mclapply' in the 'parallel' package
++> ## start, end, thin are set, also noting number of chains
++> smfun <- function(x, burnin, nsim, thin) {
+++ nm <- nullmodel(x, "swap")
+++ nm <- update(nm, nsim=burnin)
+++ simulate(nm, nsim=nsim, thin=thin)
+++ }
++> smlist <- replicate(3, smfun(x, burnin=50, nsim=10, thin=5), simplify=FALSE)
++> smbind(smlist, MARGIN=3) # Number of permuted matrices = 30
++An object of class “simmat”
++‘swap’ method (binary, sequential)
++12 x 10 matrix
++Number of permuted matrices = 30
++Start = 55, End = 100, Thin = 5 (3 chains)
++
++>
++> ## Not run:
++> ##D ## parallel null model calculations
++> ##D library(parallel)
++> ##D
++> ##D if (.Platform$OS.type == "unix") {
++> ##D ## forking on Unix systems
++> ##D smlist <- mclapply(1:3, function(i) smfun(x, burnin=50, nsim=10,
thin=5))
++> ##D smbind(smlist, MARGIN=3)
++> ##D }
++> ##D
++> ##D ## socket type cluster, works on all platforms
++> ##D cl <- makeCluster(3)
++> ##D clusterEvalQ(cl, library(vegan))
++> ##D clusterExport(cl, c("smfun", "x"))
++> ##D smlist <- parLapply(cl, 1:3, function(i) smfun(x, burnin=50, nsim=10,
thin=5))
++> ##D stopCluster(cl)
++> ##D smbind(smlist, MARGIN=3)
++> ## End(Not run)
++>
++>
++>
++> cleanEx()
++> nameEx("oecosimu")
++> ### * oecosimu
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: oecosimu
++> ### Title: Evaluate Statistics with Null Models of Biological Communities
++> ### Aliases: oecosimu as.ts.oecosimu as.mcmc.oecosimu
++> ### Keywords: multivariate datagen nonparametric
++>
++> ### ** Examples
++>
++> ## Use the first eigenvalue of correspondence analysis as an index
++> ## of structure: a model for making your own functions.
++> data(sipoo)
++> ## Traditional nestedness statistics (number of checkerboard units)
++> oecosimu(sipoo, nestedchecker, "r0")
++oecosimu object
++
++Call: oecosimu(comm = sipoo, nestfun = nestedchecker, method = "r0")
++
++nullmodel method ‘r0’ with 99 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++Checkerboard Units : 2767
++C-score (species mean): 2.258776
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++checkerboards 2767 -17.768 8034.6 7529.9 8052.0 8518.5 0.01 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## sequential model, one-sided test, a vector statistic
++> out <- oecosimu(sipoo, decorana, "swap", burnin=100, thin=10,
+++ statistic="evals", alt = "greater")
++> out
++oecosimu object
++
++Call: oecosimu(comm = sipoo, nestfun = decorana, method = "swap",
++burnin = 100, thin = 10, statistic = "evals", alternative = "greater")
++
++nullmodel method ‘swap’ with 99 simulations
++options: thin 10, burnin 100
++alternative hypothesis: statistic is greater than simulated values
++
++
++Call:
++nestfun(veg = comm)
++
++Detrended correspondence analysis with 26 segments.
++Rescaling of axes with 4 iterations.
++
++ DCA1 DCA2 DCA3 DCA4
++Eigenvalues 0.3822 0.2612 0.1668 0.08723
++Decorana values 0.4154 0.2465 0.1391 0.04992
++Axis lengths 2.9197 2.5442 2.7546 1.78074
++
++
++ statistic SES mean 50% 95% Pr(sim.)
++DCA1 0.382249 1.8658 0.32404 0.32373 0.3726 0.02 *
++DCA2 0.261208 1.5772 0.21939 0.21617 0.2637 0.09 .
++DCA3 0.166788 0.5209 0.15594 0.15572 0.1859 0.30
++DCA4 0.087226 -1.9822 0.13015 0.12649 0.1649 0.99
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Inspect the swap sequence as a time series object
++> plot(as.ts(out))
++> lag.plot(as.ts(out))
++> acf(as.ts(out))
++> ## Density plot
++> densityplot(permustats(out), as.table = TRUE, layout = c(1,4))
++> ## Use quantitative null models to compare
++> ## mean Bray-Curtis dissimilarities
++> data(dune)
++> meandist <- function(x) mean(vegdist(x, "bray"))
++> mbc1 <- oecosimu(dune, meandist, "r2dtable")
++> mbc1
++oecosimu object
++
++Call: oecosimu(comm = dune, nestfun = meandist, method = "r2dtable")
++
++nullmodel method ‘r2dtable’ with 99 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++statistic 0.64565 16.876 0.46651 0.44565 0.46652 0.4826 0.01 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> ## Define your own null model as a 'commsim' function: shuffle cells
++> ## in each row
++> foo <- function(x, n, nr, nc, ...) {
+++ out <- array(0, c(nr, nc, n))
+++ for (k in seq_len(n))
+++ out[,,k] <- apply(x, 2, function(z) sample(z, length(z)))
+++ out
+++ }
++> cf <- commsim("myshuffle", foo, isSeq = FALSE, binary = FALSE,
+++ mode = "double")
++> oecosimu(dune, meandist, cf)
++oecosimu object
++
++Call: oecosimu(comm = dune, nestfun = meandist, method = cf)
++
++nullmodel method ‘myshuffle’ with 99 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++statistic 0.64565 2.7402 0.63542 0.62941 0.63519 0.6433 0.03 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("ordiArrowTextXY")
++> ### * ordiArrowTextXY
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordiArrowTextXY
++> ### Title: Support Functions for Drawing Vectors
++> ### Aliases: ordiArrowMul ordiArrowTextXY
++> ### Keywords: utilities
++>
++> ### ** Examples
++>
++> ## Scale arrows by hand to fill 80% of the plot
++> ## Biplot arrows by hand
++> data(varespec, varechem)
++> ord <- cca(varespec ~ Al + P + K, varechem)
++> plot(ord, display = c("species","sites"))
++>
++> ## biplot scores
++> bip <- scores(ord, choices = 1:2, display = "bp")
++>
++> ## scaling factor for arrows to fill 80% of plot
++> (mul <- ordiArrowMul(bip, fill = 0.8))
++[1] 2.074256
++> bip.scl <- bip * mul # Scale the biplot scores
++> labs <- rownames(bip) # Arrow labels
++>
++> ## calculate coordinate of labels for arrows
++> (bip.lab <- ordiArrowTextXY(bip.scl, rescale = FALSE, labels = labs))
++ [,1] [,2]
++Al 1.8804734 -0.3506655
++P -0.9169247 -1.6419989
++K -0.9932187 -0.3713153
++>
++> ## draw arrows and text labels
++> arrows(0, 0, bip.scl[,1], bip.scl[,2], length = 0.1)
++> text(bip.lab, labels = labs)
++>
++> ## Handling of ordination objects directly
++> mul2 <- ordiArrowMul(ord, display = "bp", fill = 0.8)
++> stopifnot(all.equal(mul, mul2))
++>
++>
++>
++> cleanEx()
++> nameEx("ordiarrows")
++> ### * ordiarrows
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordiarrows
++> ### Title: Add Arrows and Line Segments to Ordination Diagrams
++> ### Aliases: ordiarrows ordisegments ordigrid
++> ### Keywords: aplot
++>
++> ### ** Examples
++>
++> example(pyrifos)
++
++pyrifs> data(pyrifos)
++
++pyrifs> ditch <- gl(12, 1, length=132)
++
++pyrifs> week <- gl(11, 12, labels=c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24))
++
++pyrifs> dose <- factor(rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6),
11))
++> mod <- rda(pyrifos)
++> plot(mod, type = "n")
++> ## Annual succession by ditches, colour by dose
++> ordiarrows(mod, ditch, label = TRUE, col = as.numeric(dose))
++> legend("topright", levels(dose), lty=1, col=1:5, title="Dose")
++> ## Show only control and highest Pyrifos treatment
++> plot(mod, type = "n")
++> ordiarrows(mod, ditch, label = TRUE,
+++ show.groups = c("2", "3", "5", "11"))
++> ordiarrows(mod, ditch, label = TRUE, show = c("6", "9"),
+++ col = 2)
++> legend("topright", c("Control", "Pyrifos 44"), lty = 1, col = c(1,2))
++>
++>
++>
++> cleanEx()
++> nameEx("ordihull")
++> ### * ordihull
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordihull
++> ### Title: Display Groups or Factor Levels in Ordination Diagrams
++> ### Aliases: ordihull ordispider ordiellipse ordibar ordicluster
++> ### weights.cca weights.rda weights.decorana summary.ordihull
++> ### scores.ordihull summary.ordiellipse ordiareatest
++> ### Keywords: aplot
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune ~ Management, dune.env)
++> attach(dune.env)
++> plot(mod, type="n", scaling = "symmetric")
++> ## Catch the invisible result of ordihull...
++> pl <- ordihull(mod, Management, scaling = "symmetric", label = TRUE)
++> ## ... and find centres and areas of the hulls
++> summary(pl)
++ BF HF NM SF
++CCA1 0.2917476 0.36826105 -1.3505642 0.2762936
++CCA2 0.8632208 0.09419919 0.2681515 -0.8139398
++Area 0.1951715 0.59943363 1.7398193 1.0144372
++> ## use more colours and add ellipsoid hulls
++> plot(mod, type = "n")
++> pl <- ordihull(mod, Management, scaling = "symmetric", col = 1:4,
+++ draw="polygon", label =TRUE)
++> ordiellipse(mod, Management, scaling = "symmetric", kind = "ehull",
+++ col = 1:4, lwd=3)
++> ## ordispider to connect WA and LC scores
++> plot(mod, dis=c("wa","lc"), type="p")
++> ordispider(mod)
++> ## Other types of plots
++> plot(mod, type = "p", display="sites")
++> cl <- hclust(vegdist(dune))
++> ordicluster(mod, cl, prune=3, col = cutree(cl, 4))
++> ## confidence ellipse: location of the class centroids
++> plot(mod, type="n", display = "sites")
++> text(mod, display="sites", labels = as.character(Management),
+++ col=as.numeric(Management))
++> pl <- ordiellipse(mod, Management, kind="se", conf=0.95, lwd=2,
+++ draw = "polygon", col=1:4, border=1:4, alpha=63)
++> summary(pl)
++ BF HF NM SF
++CCA1 0.4312652 0.5583211 -1.87848340 0.5601499
++CCA2 1.3273917 0.6373120 -0.05503211 -1.3859924
++Area 1.4559842 1.3806668 2.73667419 1.5559135
++> ## add confidence bars
++> ordibar(mod, Management, kind="se", conf=0.95, lwd=2, col=1:4, label=TRUE)
++>
++>
++>
++> cleanEx()
++
++detaching ‘dune.env’
++
++> nameEx("ordilabel")
++> ### * ordilabel
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordilabel
++> ### Title: Add Text on Non-transparent Label to an Ordination Plot.
++> ### Aliases: ordilabel
++> ### Keywords: aplot
++>
++> ### ** Examples
++>
++> data(dune)
++> ord <- cca(dune)
++> plot(ord, type = "n")
++> ordilabel(ord, dis="sites", cex=1.2, font=3, fill="hotpink", col="blue")
++> ## You may prefer separate plots, but here species as well
++> ordilabel(ord, dis="sp", font=2, priority=colSums(dune))
++>
++>
++>
++> cleanEx()
++> nameEx("ordiplot")
++> ### * ordiplot
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordiplot
++> ### Title: Alternative plot and identify Functions for Ordination
++> ### Aliases: ordiplot identify.ordiplot scores.ordiplot points.ordiplot
++> ### text.ordiplot
++> ### Keywords: hplot iplot aplot
++>
++> ### ** Examples
++>
++> # Draw a plot for a non-vegan ordination (cmdscale).
++> data(dune)
++> dune.dis <- vegdist(wisconsin(dune))
++> dune.mds <- cmdscale(dune.dis, eig = TRUE)
++> dune.mds$species <- wascores(dune.mds$points, dune, expand = TRUE)
++> fig <- ordiplot(dune.mds, type = "none")
++> points(fig, "sites", pch=21, col="red", bg="yellow")
++> text(fig, "species", col="blue", cex=0.9)
++> # Default plot of the previous using identify to label selected points
++> ## Not run:
++> ##D fig <- ordiplot(dune.mds)
++> ##D identify(fig, "spec")
++> ## End(Not run)
++>
++>
++>
++>
++> cleanEx()
++> nameEx("ordipointlabel")
++> ### * ordipointlabel
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordipointlabel
++> ### Title: Ordination Plots with Points and Optimized Locations for Text
++> ### Aliases: ordipointlabel plot.ordipointlabel
++> ### Keywords: hplot aplot
++>
++> ### ** Examples
++>
++> data(dune)
++> ord <- cca(dune)
++> plt <- ordipointlabel(ord)
++>
++> ## set scaling - should be no warnings!
++> ordipointlabel(ord, scaling = "sites")
++>
++> ## plot then add
++> plot(ord, scaling = "symmetric", type = "n")
++> ordipointlabel(ord, display = "species", scaling = "symm", add = TRUE)
++> ordipointlabel(ord, display = "sites", scaling = "symm", add = TRUE)
++>
++> ## redraw plot without rerunning SANN optimisation
++> plot(plt)
++>
++>
++>
++> cleanEx()
++> nameEx("ordiresids")
++> ### * ordiresids
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordiresids
++> ### Title: Plots of Residuals and Fitted Values for Constrained Ordination
++> ### Aliases: ordiresids
++> ### Keywords: hplot
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> mod <- cca(varespec ~ Al + P + K, varechem)
++> ordiresids(mod)
++>
++>
++>
++> cleanEx()
++> nameEx("ordistep")
++> ### * ordistep
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordistep
++> ### Title: Choose a Model by Permutation Tests in Constrained Ordination
++> ### Aliases: ordistep ordiR2step
++> ### Keywords: multivariate models
++>
++> ### ** Examples
++>
++> ## See add1.cca for another example
++>
++> ### Dune data
++> data(dune)
++> data(dune.env)
++> mod0 <- rda(dune ~ 1, dune.env) # Model with intercept only
++> mod1 <- rda(dune ~ ., dune.env) # Model with all explanatory variables
++>
++> ## With scope present, the default direction is "both"
++> ordistep(mod0, scope = formula(mod1), perm.max = 200)
++
++Start: dune ~ 1
++
++ Df AIC F Pr(>F)
+++ Management 3 87.082 2.8400 0.005 **
+++ Moisture 3 87.707 2.5883 0.005 **
+++ Manure 4 89.232 1.9539 0.015 *
+++ A1 1 89.591 1.9217 0.040 *
+++ Use 2 91.032 1.1741 0.340
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: dune ~ Management
++
++ Df AIC F Pr(>F)
++- Management 3 89.62 2.84 0.005 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++ Df AIC F Pr(>F)
+++ Moisture 3 85.567 1.9764 0.010 **
+++ Manure 3 87.517 1.3902 0.115
+++ A1 1 87.424 1.2965 0.200
+++ Use 2 88.284 1.0510 0.340
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: dune ~ Management + Moisture
++
++ Df AIC F Pr(>F)
++- Moisture 3 87.082 1.9764 0.015 *
++- Management 3 87.707 2.1769 0.005 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++ Df AIC F Pr(>F)
+++ Manure 3 85.762 1.1225 0.275
+++ A1 1 86.220 0.8359 0.585
+++ Use 2 86.842 0.8027 0.700
++
++Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
++
++ Inertia Proportion Rank
++Total 84.1237 1.0000
++Constrained 46.4249 0.5519 6
++Unconstrained 37.6988 0.4481 13
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
++21.588 14.075 4.123 3.163 2.369 1.107
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
++8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
++
++>
++> ## Example without scope. Default direction is "backward"
++> ordistep(mod1, perm.max = 200)
++
++Start: dune ~ A1 + Moisture + Management + Use + Manure
++
++ Df AIC F Pr(>F)
++- Use 2 86.056 0.8330 0.640
++- A1 1 85.933 0.7933 0.580
++- Manure 3 87.357 1.0737 0.400
++- Management 2 87.672 1.1976 0.305
++- Moisture 3 88.818 1.3320 0.175
++
++Step: dune ~ A1 + Moisture + Management + Manure
++
++ Df AIC F Pr(>F)
++- A1 1 85.762 0.8015 0.615
++- Manure 3 86.220 1.0829 0.395
++- Management 2 86.688 1.1728 0.275
++- Moisture 3 87.779 1.4140 0.125
++
++Step: dune ~ Moisture + Management + Manure
++
++ Df AIC F Pr(>F)
++- Manure 3 85.567 1.1225 0.325
++- Management 2 86.060 1.1986 0.235
++- Moisture 3 87.517 1.5788 0.065 .
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: dune ~ Moisture + Management
++
++ Df AIC F Pr(>F)
++- Moisture 3 87.082 1.9764 0.015 *
++- Management 3 87.707 2.1769 0.010 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Call: rda(formula = dune ~ Moisture + Management, data = dune.env)
++
++ Inertia Proportion Rank
++Total 84.1237 1.0000
++Constrained 46.4249 0.5519 6
++Unconstrained 37.6988 0.4481 13
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
++21.588 14.075 4.123 3.163 2.369 1.107
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
++8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
++
++>
++> ## Example of ordistep, forward
++> ## Not run:
++> ##D ordistep(mod0, scope = formula(mod1), direction="forward", perm.max =
200)
++> ## End(Not run)
++> ### Mite data
++> data(mite)
++> data(mite.env)
++> mite.hel = decostand(mite, "hel")
++> mod0 <- rda(mite.hel ~ 1, mite.env) # Model with intercept only
++> mod1 <- rda(mite.hel ~ ., mite.env) # Model with all explanatory variables
++>
++> ## Example of ordiR2step with default direction = "both"
++> ## (This never goes "backward" but evaluates included terms.)
++> step.res <- ordiR2step(mod0, mod1, perm.max = 200)
++Step: R2.adj= 0
++Call: mite.hel ~ 1
++
++ R2.adjusted
++<All variables> 0.43670383
+++ WatrCont 0.26084533
+++ Shrub 0.20716190
+++ Topo 0.15205437
+++ Substrate 0.07718348
+++ SubsDens 0.02632468
++<none> 0.00000000
++
++ Df AIC F Pr(>F)
+++ WatrCont 1 -84.336 25.35 0.002 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: R2.adj= 0.2608453
++Call: mite.hel ~ WatrCont
++
++ R2.adjusted
++<All variables> 0.4367038
+++ Shrub 0.3177536
+++ Topo 0.3120057
+++ Substrate 0.3091579
+++ SubsDens 0.3066715
++<none> 0.2608453
++- WatrCont 0.0000000
++
++ Df AIC F Pr(>F)
+++ Shrub 2 -88.034 3.836 0.002 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: R2.adj= 0.3177536
++Call: mite.hel ~ WatrCont + Shrub
++
++ R2.adjusted
++<All variables> 0.4367038
+++ Substrate 0.3653551
+++ Topo 0.3525851
+++ SubsDens 0.3446967
++<none> 0.3177536
++- Shrub 0.2608453
++- WatrCont 0.2071619
++
++ Df AIC F Pr(>F)
+++ Substrate 6 -87.768 1.8251 0.002 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: R2.adj= 0.3653551
++Call: mite.hel ~ WatrCont + Shrub + Substrate
++
++ R2.adjusted
++<All variables> 0.4367038
+++ Topo 0.4004249
+++ SubsDens 0.3901844
++<none> 0.3653551
++- Substrate 0.3177536
++- Shrub 0.3091579
++- WatrCont 0.2616484
++
++ Df AIC F Pr(>F)
+++ Topo 1 -90.924 4.5095 0.004 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: R2.adj= 0.4004249
++Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo
++
++ R2.adjusted
++<All variables> 0.4367038
+++ SubsDens 0.4367038
++<none> 0.4004249
++- Topo 0.3653551
++- Shrub 0.3591790
++- Substrate 0.3525851
++- WatrCont 0.3145444
++
++ Df AIC F Pr(>F)
+++ SubsDens 1 -94.489 4.7999 0.002 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: R2.adj= 0.4367038
++Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo + SubsDens
++
++ R2.adjusted
++<All variables> 0.4367038
++<none> 0.4367038
++- Shrub 0.4079297
++- SubsDens 0.4004249
++- Substrate 0.3951235
++- Topo 0.3901844
++- WatrCont 0.3357858
++
++> step.res$anova # Summary table
++ R2.adj Df AIC F Pr(>F)
+++ WatrCont 0.26085 1 -84.336 25.3499 0.002 **
+++ Shrub 0.31775 2 -88.034 3.8360 0.002 **
+++ Substrate 0.36536 6 -87.768 1.8251 0.002 **
+++ Topo 0.40042 1 -90.924 4.5095 0.004 **
+++ SubsDens 0.43670 1 -94.489 4.7999 0.002 **
++<All variables> 0.43670
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> ## Example of ordiR2step with direction = "forward"
++> ## Not run:
++> ##D step.res <- ordiR2step(mod0, scope = formula(mod1),
direction="forward")
++> ##D step.res$anova # Summary table
++> ## End(Not run)
++>
++>
++>
++> cleanEx()
++> nameEx("ordisurf")
++> ### * ordisurf
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordisurf
++> ### Title: Fit and Plot Smooth Surfaces of Variables on Ordination.
++> ### Aliases: ordisurf ordisurf.default ordisurf.formula calibrate.ordisurf
++> ### plot.ordisurf
++> ### Keywords: multivariate aplot
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> vare.dist <- vegdist(varespec)
++> vare.mds <- monoMDS(vare.dist)
++> ordisurf(vare.mds ~ Baresoil, varechem, bubble = 5)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++5.63 total = 6.63
++
++REML score: 92.96761
++>
++> ## as above but without the extra penalties on smooth terms,
++> ## and using GCV smoothness selection (old behaviour of `ordisurf()`):
++> ordisurf(vare.mds ~ Baresoil, varechem, col = "blue", add = TRUE,
+++ select = FALSE, method = "GCV.Cp")
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++6.45 total = 7.45
++
++GCV score: 144.0039
++>
++> ## Cover of Cladina arbuscula
++> fit <- ordisurf(vare.mds ~ Cladarbu, varespec, family=quasipoisson)
++> ## Get fitted values
++> calibrate(fit)
++ 18 15 24 27 23 19 22
16
++21.254026 5.675259 3.679616 3.898510 9.643821 7.698310 7.729203
9.758046
++ 28 13 14 20 25 7 5
6
++ 2.743212 29.700556 11.971747 8.310960 5.390564 24.905994 11.007652
25.330656
++ 3 4 2 9 12 10 11
21
++ 6.520708 9.410616 4.519672 4.090948 11.557930 4.314850 11.693692
14.243453
++>
++> ## Variable selection via additional shrinkage penalties
++> ## This allows non-significant smooths to be selected out
++> ## of the model not just to a linear surface. There are 2
++> ## options available:
++> ## - option 1: `select = TRUE` --- the *default*
++> ordisurf(vare.mds ~ Baresoil, varechem, method = "REML", select = TRUE)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++5.63 total = 6.63
++
++REML score: 92.96761
++> ## - option 2: use a basis with shrinkage
++> ordisurf(vare.mds ~ Baresoil, varechem, method = "REML", bs = "ts")
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "ts", fx = FALSE)
++
++Estimated degrees of freedom:
++4.43 total = 5.43
++
++REML score: 96.2345
++> ## or bs = "cs" with `isotropic = FALSE`
++>
++> ## Plot method
++> plot(fit, what = "contour")
++>
++> ## Plotting the "gam" object
++> plot(fit, what = "gam") ## 'col' and 'cex' not passed on
++> ## or via plot.gam directly
++> library(mgcv)
++Loading required package: nlme
++This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.
++> plot.gam(fit, cex = 2, pch = 1, col = "blue")
++> ## 'col' effects all objects drawn...
++>
++> ### controlling the basis functions used
++> ## Use Duchon splines
++> ordisurf(vare.mds ~ Baresoil, varechem, bs = "ds")
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "ds", fx = FALSE)
++
++Estimated degrees of freedom:
++5.63 total = 6.63
++
++REML score: 93.17149
++>
++> ## A fixed degrees of freedom smooth, must use 'select = FALSE'
++> ordisurf(vare.mds ~ Baresoil, varechem, knots = 4,
+++ fx = TRUE, select = FALSE)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 4, bs = "tp", fx = TRUE)
++
++Estimated degrees of freedom:
++3 total = 4
++
++REML score: 81.86011
++>
++> ## An anisotropic smoother with cubic regression spline bases
++> ordisurf(vare.mds ~ Baresoil, varechem, isotropic = FALSE,
+++ bs = "cr", knots = 4)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ te(x1, x2, k = c(4, 4), bs = c("cr", "cr"), fx = c(FALSE,
++ FALSE))
++
++Estimated degrees of freedom:
++2.99 total = 3.99
++
++REML score: 90.86099
++>
++> ## An anisotropic smoother with cubic regression spline with
++> ## shrinkage bases & different degrees of freedom in each dimension
++> ordisurf(vare.mds ~ Baresoil, varechem, isotropic = FALSE,
+++ bs = "cs", knots = c(3,4), fx = TRUE,
+++ select = FALSE)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ te(x1, x2, k = c(3, 4), bs = c("cs", "cs"), fx = c(TRUE,
++ TRUE))
++
++Estimated degrees of freedom:
++11 total = 12
++
++REML score: 39.58245
++>
++>
++>
++> cleanEx()
++
++detaching ‘package:mgcv’, ‘package:nlme’
++
++> nameEx("orditkplot")
++> ### * orditkplot
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: orditkplot
++> ### Title: Ordination Plot with Movable Labels
++> ### Aliases: orditkplot plot.orditkplot scores.orditkplot points.orditkplot
++> ### text.orditkplot
++> ### Keywords: iplot dynamic
++>
++> ### ** Examples
++>
++> ## The example needs user interaction and is not executed directly.
++> ## It should work when pasted to the window.
++> ## Not run:
++> ##D data(varespec)
++> ##D ord <- cca(varespec)
++> ##D ## Do something with the graph and end by clicking "Dismiss"
++> ##D orditkplot(ord, mar = c(4,4,1,1)+.1, font=3)
++> ##D ## Use ordipointlabel to produce a plot that has both species and site
++> ##D ## scores in different colors and plotting symbols
++> ##D pl <- ordipointlabel(ord)
++> ##D orditkplot(pl)
++> ## End(Not run)
++>
++>
++>
++> cleanEx()
++> nameEx("orditorp")
++> ### * orditorp
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: orditorp
++> ### Title: Add Text or Points to Ordination Plots
++> ### Aliases: orditorp
++> ### Keywords: aplot hplot
++>
++> ### ** Examples
++>
++> ## A cluttered ordination plot :
++> data(BCI)
++> mod <- cca(BCI)
++> plot(mod, dis="sp", type="t")
++> # Now with orditorp and abbreviated species names
++> cnam <- make.cepnames(names(BCI))
++> plot(mod, dis="sp", type="n")
++> stems <- colSums(BCI)
++> orditorp(mod, "sp", label = cnam, priority=stems, pch="+", pcol="grey")
++>
++> ## show select in action
++> set.seed(1)
++> take <- sample(ncol(BCI), 50)
++> plot(mod, dis="sp", type="n")
++> stems <- colSums(BCI)
++> orditorp(mod, "sp", label = cnam, priority=stems, select = take,
+++ pch="+", pcol="grey")
++> ## Don't show:
++> ## example(orditorp) should not set random seed in the user session
++> rm(.Random.seed)
++> ## End(Don't show)
++>
++>
++>
++> cleanEx()
++> nameEx("ordixyplot")
++> ### * ordixyplot
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: ordixyplot
++> ### Title: Trellis (Lattice) Plots for Ordination
++> ### Aliases: ordixyplot ordisplom ordicloud panel.ordi panel.ordiarrows
++> ### panel.ordi3d prepanel.ordi3d ordilattice.getEnvfit
++> ### Keywords: hplot
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> ord <- cca(dune)
++> ## Pairs plots
++> ordisplom(ord)
++> ordisplom(ord, data=dune.env, choices=1:2)
++> ordisplom(ord, data=dune.env, form = ~ . | Management, groups=Manure)
++> ## Scatter plot
++> ordixyplot(ord, data=dune.env, form = CA1 ~ CA2 | Management,
+++ groups=Manure)
++> ## Choose a different scaling
++> ordixyplot(ord, scaling = "symmetric")
++> ## ... Slices of third axis
++> ordixyplot(ord, form = CA1 ~ CA2 | equal.count(CA3, 4), type = c("g","p"))
++> ## Display environemntal variables
++> ordixyplot(ord, envfit = envfit(ord ~ Management + A1, dune.env,
choices=1:3))
++> ## 3D Scatter plots
++> ordicloud(ord, form = CA2 ~ CA3*CA1, groups = Manure, data = dune.env)
++> ordicloud(ord, form = CA2 ~ CA3*CA1 | Management, groups = Manure,
+++ data = dune.env, auto.key = TRUE, type = c("p","h"))
++>
++>
++>
++> cleanEx()
++> nameEx("pcnm")
++> ### * pcnm
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: pcnm
++> ### Title: Principal Coordinates of Neighbourhood Matrix
++> ### Aliases: pcnm scores.pcnm
++> ### Keywords: spatial multivariate
++>
++> ### ** Examples
++>
++> ## Example from Borcard & Legendre (2002)
++> data(mite.xy)
++> pcnm1 <- pcnm(dist(mite.xy))
++> op <- par(mfrow=c(1,3))
++> ## Map of PCNMs in the sample plot
++> ordisurf(mite.xy, scores(pcnm1, choi=1), bubble = 4, main = "PCNM 1")
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++8.71 total = 9.71
++
++REML score: -120.7705
++> ordisurf(mite.xy, scores(pcnm1, choi=2), bubble = 4, main = "PCNM 2")
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++7.18 total = 8.18
++
++REML score: -103.4662
++> ordisurf(mite.xy, scores(pcnm1, choi=3), bubble = 4, main = "PCNM 3")
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++8.32 total = 9.32
++
++REML score: -94.19053
++> par(op)
++> ## Plot first PCNMs against each other
++> ordisplom(pcnm1, choices=1:4)
++> ## Weighted PCNM for CCA
++> data(mite)
++> rs <- rowSums(mite)/sum(mite)
++> pcnmw <- pcnm(dist(mite.xy), w = rs)
++> ord <- cca(mite ~ scores(pcnmw))
++> ## Multiscale ordination: residual variance should have no distance
++> ## trend
++> msoplot(mso(ord, mite.xy))
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("permatfull")
++> ### * permatfull
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: permat
++> ### Title: Matrix Permutation Algorithms for Presence-Absence and Count
++> ### Data
++> ### Aliases: permatfull permatswap summary.permat print.summary.permat
++> ### print.permat plot.permat lines.permat as.ts.permat as.mcmc.permat
++> ### Keywords: multivariate datagen
++>
++> ### ** Examples
++>
++> ## A simple artificial community data matrix.
++> m <- matrix(c(
+++ 1,3,2,0,3,1,
+++ 0,2,1,0,2,1,
+++ 0,0,1,2,0,3,
+++ 0,0,0,1,4,3
+++ ), 4, 6, byrow=TRUE)
++> ## Using the quasiswap algorithm to create a
++> ## list of permuted matrices, where
++> ## row/columns sums and matrix fill are preserved:
++> x1 <- permatswap(m, "quasiswap")
++> summary(x1)
++Summary of object of class 'permat'
++
++Call: permatswap(m = m, method = "quasiswap")
++
++Matrix type: count
++Permutation type: swap
++Method: quasiswap_count, burnin: 0, thin: 1
++Restricted: FALSE
++Fixed margins: both
++
++Matrix dimensions: 4 rows, 6 columns
++Sum of original matrix: 30
++Fill of original matrix: 0.62
++Number of permuted matrices: 99
++
++Matrix sums retained: 100 %
++Matrix fill retained: 100 %
++Row sums retained: 100 %
++Column sums retained: 100 %
++Row incidences retained: 2.02 %
++Column incidences retained: 6.06 %
++
++Bray-Curtis dissimilarities among original and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 0.2000 0.3667 0.4333 0.4202 0.4667 0.6333
++
++Chi-squared for original matrix: 18.55
++Chi-squared values among expected and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 15.63 19.38 21.19 21.53 23.44 29.08
++> ## Unrestricted permutation retaining
++> ## row/columns sums but not matrix fill:
++> x2 <- permatfull(m)
++> summary(x2)
++Summary of object of class 'permat'
++
++Call: permatfull(m = m)
++
++Matrix type: count
++Permutation type: full
++Method: r2dtable
++Restricted: FALSE
++Fixed margins: both
++
++Matrix dimensions: 4 rows, 6 columns
++Sum of original matrix: 30
++Fill of original matrix: 0.62
++Number of permuted matrices: 99
++
++Matrix sums retained: 100 %
++Matrix fill retained: 18.18 %
++Row sums retained: 100 %
++Column sums retained: 100 %
++Row incidences retained: 0 %
++Column incidences retained: 1.01 %
++
++Bray-Curtis dissimilarities among original and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 0.1667 0.3333 0.4000 0.3754 0.4333 0.5667
++
++Chi-squared for original matrix: 18.55
++Chi-squared values among expected and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 7.857 12.640 15.650 16.410 20.240 33.150
++> ## Unrestricted permutation of presence-absence type
++> ## not retaining row/columns sums:
++> x3 <- permatfull(m, "none", mtype="prab")
++> x3$orig ## note: original matrix is binarized!
++ [,1] [,2] [,3] [,4] [,5] [,6]
++[1,] 1 1 1 0 1 1
++[2,] 0 1 1 0 1 1
++[3,] 0 0 1 1 0 1
++[4,] 0 0 0 1 1 1
++> summary(x3)
++Summary of object of class 'permat'
++
++Call: permatfull(m = m, fixedmar = "none", mtype = "prab")
++
++Matrix type: prab
++Permutation type: full
++Method: r00
++Restricted: FALSE
++Fixed margins: none
++Individuals and samples are shuffled
++
++Matrix dimensions: 4 rows, 6 columns
++Sum of original matrix: 15
++Fill of original matrix: 0.62
++Number of permuted matrices: 99
++
++Matrix sums retained: 100 %
++Matrix fill retained: 100 %
++Row sums retained: 2.02 %
++Column sums retained: 0 %
++Row incidences retained: 2.02 %
++Column incidences retained: 0 %
++
++Bray-Curtis dissimilarities among original and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 0.2000 0.3333 0.4000 0.3643 0.4000 0.5333
++
++Chi-squared for original matrix: 8.4
++Chi-squared values among expected and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 8.354 13.340 15.060 15.280 17.180 21.330
++> ## Restricted permutation,
++> ## check sums within strata:
++> x4 <- permatfull(m, strata=c(1,1,2,2))
++> summary(x4)
++Summary of object of class 'permat'
++
++Call: permatfull(m = m, strata = c(1, 1, 2, 2))
++
++Matrix type: count
++Permutation type: full
++Method: r2dtable
++Restricted: TRUE
++Fixed margins: both
++
++Matrix dimensions: 4 rows, 6 columns
++Sum of original matrix: 30
++Fill of original matrix: 0.62
++Number of permuted matrices: 99
++
++Matrix sums retained: 100 %
++Matrix fill retained: 49.49 %
++Row sums retained: 100 %
++Column sums retained: 100 %
++Row incidences retained: 7.07 %
++Column incidences retained: 3.03 %
++Sums within strata retained: 100 %
++
++Bray-Curtis dissimilarities among original and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++0.06667 0.20000 0.20000 0.21950 0.26670 0.40000
++
++Chi-squared for original matrix: 18.55
++Chi-squared values among expected and permuted matrices:
++ Min. 1st Qu. Median Mean 3rd Qu. Max.
++ 14.21 18.28 19.84 20.85 23.01 32.63
++>
++> ## NOTE: 'times' argument usually needs to be >= 99
++> ## here much lower value is used for demonstration
++>
++> ## Not sequential algorithm
++> data(BCI)
++> a <- permatswap(BCI, "quasiswap", times=19)
++> ## Sequential algorithm
++> b <- permatswap(BCI, "abuswap", fixedmar="col",
+++ burnin=0, thin=100, times=19)
++> opar <- par(mfrow=c(2,2))
++> plot(a, main="Not sequential")
++> plot(b, main="Sequential")
++> plot(a, "chisq")
++> plot(b, "chisq")
++> par(opar)
++> ## Extract Bray-Curtis dissimilarities
++> ## as time series
++> bc <- as.ts(b)
++> ## Lag plot
++> lag.plot(bc)
++> ## First order autoregressive model
++> mar <- arima(bc, c(1,0,0))
++> mar
++
++Call:
++arima(x = bc, order = c(1, 0, 0))
++
++Coefficients:
++ ar1 intercept
++ 0.9914 0.1847
++s.e. 0.0122 0.1343
++
++sigma^2 estimated as 0.0003348: log likelihood = 47.03, aic = -88.06
++> ## Ljung-Box test of residuals
++> Box.test(residuals(mar))
++
++ Box-Pierce test
++
++data: residuals(mar)
++X-squared = 0.0010721, df = 1, p-value = 0.9739
++
++> ## Graphical diagnostics
++> tsdiag(mar)
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("permustats")
++> ### * permustats
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: permustats
++> ### Title: Extract, Analyse and Display Permutation Results
++> ### Aliases: permustats permustats.adonis permustats.anosim
++> ### permustats.anova.cca permustats.CCorA permustats.envfit
++> ### permustats.factorfit permustats.mantel permustats.mrpp permustats.mso
++> ### permustats.oecosimu permustats.ordiareatest
++> ### permustats.permutest.betadisper permustats.permutest.cca
++> ### permustats.protest permustats.vectorfit summary.permustats
++> ### c.permustats densityplot.permustats density.permustats
++> ### qqnorm.permustats qqmath.permustats
++> ### Keywords: distribution smooth
++>
++> ### ** Examples
++>
++> data(dune, dune.env)
++> mod <- adonis2(dune ~ Management + A1, data = dune.env)
++> ## use permustats
++> perm <- permustats(mod)
++> summary(perm)
++
++ statistic SES mean lower median upper Pr(perm)
++Management 3.0730 4.5815 1.0462 0.9470 1.8475 0.006 **
++A1 2.7676 2.6322 1.0364 0.8851 2.3280 0.025 *
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++(Interval (Upper - Lower) = 0.95)
++> densityplot(perm)
++> qqmath(perm)
++>
++> ## example of multiple types of statistic
++> mod <- with(dune.env, betadisper(vegdist(dune), Management))
++> pmod <- permutest(mod, nperm = 99, pairwise = TRUE)
++> perm <- permustats(pmod)
++> summary(perm, interval = 0.90)
++
++ statistic SES mean lower median upper Pr(perm)
++Overall (F) 1.9506 0.6046 1.2061 0.8375 2.7743 0.184
++BF-HF (t) -0.5634 -0.4179 -0.0584 -2.0011 -0.0974 1.8273 0.615
++BF-NM (t) -2.2387 -1.8228 -0.0941 -2.1192 -0.0944 1.7339 0.085 .
++BF-SF (t) -1.1675 -0.8886 -0.0928 -2.1442 -0.0866 1.8232 0.333
++HF-NM (t) -2.1017 -1.8899 -0.0158 -1.8740 0.0412 1.6319 0.067 .
++HF-SF (t) -0.8789 -0.7166 -0.0321 -1.8973 -0.0076 1.8362 0.431
++NM-SF (t) 0.9485 0.8309 -0.0036 -1.8411 -0.0531 1.9024 0.367
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++(Interval (Upper - Lower) = 0.9)
++>
++>
++>
++> cleanEx()
++> nameEx("permutest.betadisper")
++> ### * permutest.betadisper
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: permutest.betadisper
++> ### Title: Permutation test of multivariate homogeneity of groups
++> ### dispersions (variances)
++> ### Aliases: permutest.betadisper
++> ### Keywords: methods multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++>
++> ## Bray-Curtis distances between samples
++> dis <- vegdist(varespec)
++>
++> ## First 16 sites grazed, remaining 8 sites ungrazed
++> groups <- factor(c(rep(1,16), rep(2,8)), labels = c("grazed","ungrazed"))
++>
++> ## Calculate multivariate dispersions
++> mod <- betadisper(dis, groups)
++> mod
++
++ Homogeneity of multivariate dispersions
++
++Call: betadisper(d = dis, group = groups)
++
++No. of Positive Eigenvalues: 15
++No. of Negative Eigenvalues: 8
++
++Average distance to median:
++ grazed ungrazed
++ 0.3926 0.2706
++
++Eigenvalues for PCoA axes:
++ PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
++1.7552 1.1334 0.4429 0.3698 0.2454 0.1961 0.1751 0.1284
++>
++> ## Perform test
++> anova(mod)
++Analysis of Variance Table
++
++Response: Distances
++ Df Sum Sq Mean Sq F value Pr(>F)
++Groups 1 0.07931 0.079306 4.6156 0.04295 *
++Residuals 22 0.37801 0.017182
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> ## Permutation test for F
++> pmod <- permutest(mod, permutations = 99, pairwise = TRUE)
++>
++> ## Tukey's Honest Significant Differences
++> (mod.HSD <- TukeyHSD(mod))
++ Tukey multiple comparisons of means
++ 95% family-wise confidence level
++
++Fit: aov(formula = distances ~ group, data = df)
++
++$group
++ diff lwr upr p adj
++ungrazed-grazed -0.1219422 -0.2396552 -0.004229243 0.0429502
++
++> plot(mod.HSD)
++>
++> ## Has permustats() method
++> pstat <- permustats(pmod)
++> densityplot(pstat, scales = list(x = list(relation = "free")))
++> qqmath(pstat, scales = list(relation = "free"))
++>
++>
++>
++> cleanEx()
++> nameEx("plot.cca")
++> ### * plot.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: plot.cca
++> ### Title: Plot or Extract Results of Constrained Correspondence Analysis
++> ### or Redundancy Analysis
++> ### Aliases: plot.cca text.cca points.cca scores.cca scores.rda summary.cca
++> ### print.summary.cca ade2vegancca head.summary.cca tail.summary.cca
++> ### Keywords: hplot aplot
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune ~ A1 + Moisture + Management, dune.env)
++> plot(mod, type="n")
++> text(mod, dis="cn")
++> points(mod, pch=21, col="red", bg="yellow", cex=1.2)
++> text(mod, "species", col="blue", cex=0.8)
++> ## Limited output of 'summary'
++> head(summary(mod), tail=2)
++
++Call:
++cca(formula = dune ~ A1 + Moisture + Management, data = dune.env)
++
++Partitioning of mean squared contingency coefficient:
++ Inertia Proportion
++Total 2.1153 1.0000
++Constrained 1.1392 0.5385
++Unconstrained 0.9761 0.4615
++
++Eigenvalues, and their contribution to the mean squared contingency
coefficient
++
++Importance of components:
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
++Eigenvalue 0.4483 0.3001 0.14995 0.10733 0.05668 0.04335 0.03345
++Proportion Explained 0.2119 0.1419 0.07089 0.05074 0.02680 0.02050 0.01581
++Cumulative Proportion 0.2119 0.3538 0.42470 0.47544 0.50223 0.52273 0.53855
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7
++Eigenvalue 0.3064 0.13191 0.11516 0.10947 0.07724 0.07575 0.04871
++Proportion Explained 0.1448 0.06236 0.05444 0.05175 0.03652 0.03581 0.02303
++Cumulative Proportion 0.6834 0.74574 0.80018 0.85194 0.88845 0.92427 0.94730
++ CA8 CA9 CA10 CA11 CA12
++Eigenvalue 0.03758 0.03106 0.02102 0.01254 0.009277
++Proportion Explained 0.01777 0.01468 0.00994 0.00593 0.004390
++Cumulative Proportion 0.96506 0.97975 0.98968 0.99561 1.000000
++
++Accumulated constrained eigenvalues
++Importance of components:
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
++Eigenvalue 0.4483 0.3001 0.1499 0.10733 0.05668 0.04335 0.03345
++Proportion Explained 0.3935 0.2635 0.1316 0.09422 0.04976 0.03806 0.02937
++Cumulative Proportion 0.3935 0.6570 0.7886 0.88282 0.93258 0.97063 1.00000
++
++Scaling 2 for species and site scores
++* Species are scaled proportional to eigenvalues
++* Sites are unscaled: weighted dispersion equal on all dimensions
++
++
++Species scores
++
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++Achimill 0.8150 0.4375 -0.11236 0.35595 -0.114763 -0.01972
++Agrostol -0.7488 -0.4783 0.03561 0.17039 0.187389 0.23471
++Airaprae -0.8186 1.7469 1.04506 -0.28593 0.191836 0.73077
++Alopgeni -0.3442 -1.0216 0.37620 0.02296 -0.004041 0.04789
++Anthodor 0.3367 0.7694 -0.07602 -0.05421 0.136354 0.42463
++Bellpere 0.6535 0.2200 0.03438 0.60436 -0.090469 0.28138
++....
++Bracruta -0.1309 0.2009 -0.03708 -0.17421 -0.109657 0.04381
++Callcusp -1.5181 0.3834 -0.23255 0.15246 0.104239 -0.11424
++
++
++Site scores (weighted averages of species scores)
++
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++1 1.2468 -0.4017 0.91955 0.7292 1.5785 -1.0196
++2 0.8622 -0.1641 0.25789 1.7240 -0.7592 -0.6479
++3 0.3165 -0.9785 0.82952 0.7451 0.6556 0.3256
++4 0.2405 -0.8699 1.07861 1.4103 1.1164 2.4714
++5 1.1362 0.2621 -1.10847 -0.9417 0.5630 1.1495
++6 1.0575 0.4041 -1.65035 -1.8483 1.0287 -0.1690
++....
++19 -0.7913 2.7451 2.93017 -1.3851 -0.3932 1.7277
++20 -2.0770 1.0113 -0.02581 -0.8949 1.6406 -1.7917
++
++
++Site constraints (linear combinations of constraining variables)
++
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++1 0.7245 -0.3695 1.25652 -0.3678 0.9827 -0.60590
++2 0.9033 0.4250 0.03901 1.0557 -1.0860 -1.61234
++3 0.4493 -0.6694 0.67765 0.8695 0.9609 1.52307
++4 0.4550 -0.6532 0.72768 0.8529 0.9795 1.50218
++5 0.9671 -0.2010 -1.93972 -0.5807 0.2582 0.31905
++6 1.0805 0.1235 -0.93911 -0.9126 0.6307 -0.09863
++....
++19 -1.4581 1.6074 1.16812 -0.5305 0.3178 -0.40336
++20 -1.4468 1.6399 1.26818 -0.5637 0.3551 -0.44513
++
++
++Biplot scores for constraining variables
++
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++A1 -0.5543 -0.1643 -0.68092 0.10702 -0.17968 0.30434
++Moisture.L -0.9439 -0.1658 0.07947 -0.02251 0.03105 -0.02454
++Moisture.Q -0.1865 0.3564 -0.45393 -0.17252 0.28388 -0.63130
++Moisture.C -0.2065 0.1728 0.10624 0.68205 0.50145 0.35869
++ManagementHF 0.3621 -0.1143 -0.42227 -0.67858 0.17177 -0.12184
++ManagementNM -0.5835 0.7261 -0.01207 -0.09715 -0.11392 0.26924
++ManagementSF -0.1512 -0.6968 0.38542 0.24769 0.29474 0.23829
++
++
++Centroids for factor constraints
++
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++Moisture1 0.9119 0.35388 -0.40013 -0.26218 0.02084 -0.4708
++Moisture2 0.5015 -0.06706 0.60222 1.12478 0.33942 1.2024
++Moisture4 -0.1522 -1.35873 0.76544 -1.37289 -1.80794 0.3849
++Moisture5 -1.3394 0.11972 -0.20942 0.04843 0.39751 -0.3902
++ManagementBF 0.8376 0.41614 0.13885 1.40679 -0.97766 -0.9604
++ManagementHF 0.5426 -0.17426 -0.62822 -1.00848 0.25622 -0.1834
++ManagementNM -1.1010 1.36665 -0.02097 -0.18131 -0.21523 0.5084
++ManagementSF -0.2320 -1.06831 0.59183 0.38035 0.45250 0.3659
++
++> ## Scaling can be numeric or more user-friendly names
++> ## e.g. Hill's scaling for (C)CA
++> scrs <- scores(mod, scaling = "sites", hill = TRUE)
++> ## or correlation-based scores in PCA/RDA
++> scrs <- scores(rda(dune ~ A1 + Moisture + Management, dune.env),
+++ scaling = "sites", correlation = TRUE)
++>
++>
++>
++> cleanEx()
++> nameEx("prc")
++> ### * prc
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: prc
++> ### Title: Principal Response Curves for Treatments with Repeated
++> ### Observations
++> ### Aliases: prc summary.prc plot.prc
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## Chlorpyrifos experiment and experimental design: Pesticide
++> ## treatment in ditches (replicated) and followed over from 4 weeks
++> ## before to 24 weeks after exposure
++> data(pyrifos)
++> week <- gl(11, 12, labels=c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24))
++> dose <- factor(rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6), 11))
++> ditch <- gl(12, 1, length=132)
++> # PRC
++> mod <- prc(pyrifos, dose, week)
++> mod # RDA
++Call: prc(response = pyrifos, treatment = dose, time = week)
++
++ Inertia Proportion Rank
++Total 288.9920 1.0000
++Conditional 63.3493 0.2192 10
++Constrained 96.6837 0.3346 44
++Unconstrained 128.9589 0.4462 77
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10 RDA11
++25.282 8.297 6.044 4.766 4.148 3.857 3.587 3.334 3.087 2.551 2.466
++ RDA12 RDA13 RDA14 RDA15 RDA16 RDA17 RDA18 RDA19 RDA20 RDA21 RDA22
++ 2.209 2.129 1.941 1.799 1.622 1.579 1.440 1.398 1.284 1.211 1.133
++ RDA23 RDA24 RDA25 RDA26 RDA27 RDA28 RDA29 RDA30 RDA31 RDA32 RDA33
++ 1.001 0.923 0.862 0.788 0.750 0.712 0.685 0.611 0.584 0.537 0.516
++ RDA34 RDA35 RDA36 RDA37 RDA38 RDA39 RDA40 RDA41 RDA42 RDA43 RDA44
++ 0.442 0.417 0.404 0.368 0.340 0.339 0.306 0.279 0.271 0.205 0.179
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
++17.156 9.189 7.585 6.064 5.730 4.843 4.518 4.105
++(Showed only 8 of all 77 unconstrained eigenvalues)
++
++> summary(mod) # PRC
++
++Call:
++prc(response = pyrifos, treatment = dose, time = week)
++Species scores:
++ Simve Daplo Cerpu Alogu Aloco Alore Aloaf
Copsp
++-1.461934 -0.796510 -0.295171 -0.152301 -0.096273 -0.171335 -0.231967
-0.635966
++ Ostsp Slyla Acrha Aloex Chysp Alona Plead
Oxyte
++-1.257492 0.302872 -0.057396 -0.124049 -0.051689 -0.034638 -0.075268
-0.013815
++ Grate Copdi NauLa CilHa Strvi amosp Ascmo
Synsp
++-0.052667 -0.777088 -2.636100 -0.486881 -1.669474 0.738371 -0.037926
0.014409
++ Squro Squmu Polar Kerqu Anufi Mytve Mytvi
Mytmu
++-0.143790 0.246185 -0.251255 -0.269397 -0.235362 -0.040448 -0.049452
-0.057589
++ Lepsp Leppa Colob Colbi Colun Lecsp Lecqu
Lecco
++-0.542922 -0.046123 0.393234 0.075905 0.450495 -0.257170 0.048327
0.158007
++ Leclu Lecfl Tripo Cepsp Monlo Monae Scalo
Trilo
++-0.027077 0.221912 -0.117056 0.440303 0.287108 0.048918 0.041981
0.021257
++ Tripo.1 Tricy Trisp Tepat Rotne Notla Filsp
Lopox
++-0.134025 -0.182409 -0.042651 -0.004198 -0.078168 0.062163 0.091561
0.016854
++ hydrspec bothrosp olchaeta erpoocto glsicomp alglhete hebdstag
sphidae
++ 0.026485 -0.216815 0.633674 0.490029 0.078527 0.039728 -0.490678
-0.796015
++ ansuvote armicris bathcont binitent gyraalbu hippcomp lymnstag
lymnaes7
++-0.076512 -0.913681 -0.039855 1.060788 -0.017975 -0.219974 0.143403
-0.073502
++ physfont plbacorn popyanti radiovat radipere valvcris valvpisc
hycarina
++ 0.014349 -0.046098 -0.691904 0.010777 0.340163 -0.005753 0.145523
-0.567803
++ gammpule aselaqua proameri collembo caenhora caenluct caenrobu
cloedipt
++-0.830166 -0.858606 -0.063401 -0.016264 -3.136867 -1.292300 -0.068624
-2.574625
++ cloesimi aeshniae libellae conagrae corident coripanz coripunc
cymabons
++-0.675580 -0.115677 0.044524 -0.886795 -0.007484 -0.065501 0.096124
-0.025213
++ hesplinn hespsahl notoglau notomacu notoobli notoviri pacoconc
pleaminu
++ 0.037779 -0.018078 -0.301948 -0.027226 -0.044790 -0.117398 -0.009087
-0.038705
++ sigadist sigafall sigastri sigarasp colyfusc donacis6 gyrimari
haliconf
++-0.041594 0.009987 -0.032743 0.150817 -0.019847 0.042751 -0.005753
-0.243054
++ haliflav haligruf haliobli herubrev hya_herm hyglpusi hyhyovat
hypoplan
++-0.024222 -0.244814 -0.071501 0.069877 0.175327 0.006404 -0.013159
-0.008145
++ hyporusp hytuinae hytuvers laphminu noteclav rhantusp sialluta
ablalong
++-0.126197 -1.259664 -0.963901 -0.344204 -0.004303 -0.036774 -0.603320
-0.008145
++ ablaphmo cltanerv malopisp mopetenu prdiussp pstavari chironsp
crchirsp
++-1.627590 -0.041132 -0.025919 -0.004740 -0.301790 -0.045054 -1.027839
-0.009087
++ crclglat ditendsp mitegchl pachgarc pachgvit popegnub popedisp
acriluce
++-0.015746 -0.045402 -0.125429 0.006628 0.016265 -0.121971 0.037879
0.004324
++ chclpige conescut cricotsp liesspec psclbarb psclgsli psclobvi
psclplat
++ 0.004756 -0.446524 -0.066095 -0.058403 0.015576 -0.327165 0.197081
0.028310
++ psclpsil pscladsp cladotsp laa_spec patanysp tatarssp zaa_spec
anopmacu
++-0.003991 -0.003086 -0.293623 -0.018548 -0.079841 -0.364072 -0.027165
0.089046
++ cepogoae chaoobsc cucidae4 tabanusp agdasphr athrater cyrncren
holodubi
++-1.389767 -1.328261 -0.018078 0.006309 -0.147828 -0.036774 -0.038705
-0.051532
++ holopici leceriae lilurhom monaangu mystazur mystloni oecefurv
oecelacu
++-0.332631 -0.162413 -0.004929 -0.350402 -0.018078 -1.630725 -0.291847
-0.140893
++ triabico paponysp
++-0.048357 -0.053182
++
++Coefficients for dose + week:dose interaction
++which are contrasts to dose 0
++rows are dose, columns are week
++ -4 -1 0.1 1 2 4 8 12 15 19
++0.1 0.1327 0.2527 0.1875 0.07479 0.3863 0.2509 0.1485 0.2824 0.2064 0.3977
++0.9 0.1490 0.3558 0.3560 0.87706 0.9151 0.7918 0.2488 0.6523 0.4428 0.3229
++6 0.3055 0.2266 0.8346 2.12626 1.9923 2.1165 1.0317 0.8638 0.5660 0.6054
++44 0.2570 0.3601 1.3438 2.31842 2.3862 2.6894 2.3745 1.8536 1.4377 1.0607
++ 24
++0.1 0.1441
++0.9 0.2839
++6 0.3352
++44 0.5704
++> logabu <- colSums(pyrifos)
++> plot(mod, select = logabu > 100)
++> ## Ditches are randomized, we have a time series, and are only
++> ## interested in the first axis
++> ctrl <- how(plots = Plots(strata = ditch,type = "free"),
+++ within = Within(type = "series"), nperm = 99)
++> anova(mod, permutations = ctrl, first=TRUE)
++Permutation test for rda under reduced model
++Plots: ditch, plot permutation: free
++Permutation: series
++Number of permutations: 99
++
++Model: prc(response = pyrifos, treatment = dose, time = week)
++ Df Variance F Pr(>F)
++RDA1 1 25.282 15.096 0.01 **
++Residual 77 128.959
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("predict.cca")
++> ### * predict.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: predict.cca
++> ### Title: Prediction Tools for [Constrained] Ordination (CCA, RDA, DCA,
++> ### CA, PCA)
++> ### Aliases: fitted.cca fitted.rda fitted.capscale fitted.dbrda
++> ### residuals.cca predict.cca predict.rda predict.decorana coef.cca
++> ### coef.rda calibrate.cca calibrate
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env)
++> # Definition of the concepts 'fitted' and 'residuals'
++> mod
++Call: cca(formula = dune ~ A1 + Management + Condition(Moisture), data
++= dune.env)
++
++ Inertia Proportion Rank
++Total 2.1153 1.0000
++Conditional 0.6283 0.2970 3
++Constrained 0.5109 0.2415 4
++Unconstrained 0.9761 0.4615 12
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3 CCA4
++0.24932 0.12090 0.08160 0.05904
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9
CA10
++0.30637 0.13191 0.11516 0.10947 0.07724 0.07575 0.04871 0.03758 0.03106
0.02102
++ CA11 CA12
++0.01254 0.00928
++
++> cca(fitted(mod))
++Call: cca(X = fitted(mod))
++
++ Inertia Rank
++Total 0.5109
++Unconstrained 0.5109 4
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4
++0.24932 0.12090 0.08160 0.05904
++
++> cca(residuals(mod))
++Call: cca(X = residuals(mod))
++
++ Inertia Rank
++Total 0.9761
++Unconstrained 0.9761 12
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9
CA10
++0.30637 0.13191 0.11516 0.10947 0.07724 0.07575 0.04871 0.03758 0.03106
0.02102
++ CA11 CA12
++0.01254 0.00928
++
++> # Remove rare species (freq==1) from 'cca' and find their scores
++> # 'passively'.
++> freq <- specnumber(dune, MARGIN=2)
++> freq
++Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu
++ 7 10 2 8 6 6 5 1
++Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo
++ 1 2 5 6 1 3 5 4
++Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe
++ 12 7 14 13 6 5 7 3
++Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp
++ 18 3 16 3 15 3
++> mod <- cca(dune[, freq>1] ~ A1 + Management + Condition(Moisture), dune.env)
++> predict(mod, type="sp", newdata=dune[, freq==1], scaling="species")
++ CCA1 CCA2 CCA3 CCA4
++Chenalbu 1.5737337 -0.7842538 0.5503660 0.35108333
++Cirsarve 0.5945146 -0.3714228 -0.2862647 0.88373727
++Empenigr -1.8771953 -0.9904299 -0.2446222 0.04858656
++> # New sites
++> predict(mod, type="lc", new=data.frame(A1 = 3, Management="NM",
Moisture="2"), scal=2)
++ CCA1 CCA2 CCA3 CCA4
++1 -2.38829 -1.230652 -0.2363485 -0.3338258
++> # Calibration and residual plot
++> mod <- cca(dune ~ A1 + Moisture, dune.env)
++> pred <- calibrate(mod)
++> pred
++ A1 Moisture.L Moisture.Q Moisture.C
++1 2.2630533 -0.62633470 -0.20456759 0.220761764
++2 4.0510042 -0.47341146 -0.36986691 0.474939409
++3 4.2752294 -0.07214500 -0.60797514 0.303213289
++4 4.5398659 0.03192745 -1.12417368 0.932223234
++5 5.0409406 -0.84235946 0.43000738 -0.291599200
++6 5.1962100 -0.91316862 1.11354235 -0.804453944
++7 4.2452549 -0.76452556 0.60464291 -0.484842066
++8 5.0208369 0.43886340 0.08169514 0.132995916
++9 4.2663219 0.10720486 -0.34067849 -0.675151598
++10 4.0411356 -0.65472729 0.02832164 0.558402684
++11 2.8280051 -0.45762457 0.63079135 -0.089977975
++12 5.1204137 0.36328912 -0.69118581 -0.665622948
++13 4.9034218 0.47069541 -0.54378271 -0.118643453
++14 11.6455841 0.60920550 0.78341426 0.532852308
++15 10.7829689 0.69208513 0.82190786 0.237311062
++16 7.9892176 0.96421599 0.46793089 0.373647014
++17 0.9218684 -0.15822891 0.14593271 1.189161582
++18 3.1680733 -0.41737900 1.03352732 -0.236938282
++19 -1.2003506 0.57033354 0.72777285 0.509955590
++20 4.7876770 1.00324330 1.49898460 0.009202396
++> with(dune.env, plot(A1, pred[,"A1"] - A1, ylab="Prediction Error"))
++> abline(h=0)
++>
++>
++>
++> cleanEx()
++> nameEx("procrustes")
++> ### * procrustes
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: procrustes
++> ### Title: Procrustes Rotation of Two Configurations and PROTEST
++> ### Aliases: procrustes summary.procrustes plot.procrustes
++> ### points.procrustes text.procrustes lines.procrustes
++> ### residuals.procrustes fitted.procrustes predict.procrustes protest
++> ### Keywords: multivariate htest
++>
++> ### ** Examples
++>
++> data(varespec)
++> vare.dist <- vegdist(wisconsin(varespec))
++> mds.null <- monoMDS(vare.dist, y = cmdscale(vare.dist))
++> mds.alt <- monoMDS(vare.dist)
++> vare.proc <- procrustes(mds.alt, mds.null)
++> vare.proc
++
++Call:
++procrustes(X = mds.alt, Y = mds.null)
++
++Procrustes sum of squares:
++13.14
++
++> summary(vare.proc)
++
++Call:
++procrustes(X = mds.alt, Y = mds.null)
++
++Number of objects: 24 Number of dimensions: 2
++
++Procrustes sum of squares:
++ 13.1368
++Procrustes root mean squared error:
++ 0.7398422
++Quantiles of Procrustes errors:
++ Min 1Q Median 3Q Max
++0.02033237 0.27686842 0.34124239 0.64358542 2.23460567
++
++Rotation matrix:
++ [,1] [,2]
++[1,] 0.9933117 -0.1154641
++[2,] 0.1154641 0.9933117
++
++Translation of averages:
++ [,1] [,2]
++[1,] 3.038945e-17 1.218602e-17
++
++Scaling of target:
++[1] 0.6727804
++
++> plot(vare.proc)
++> plot(vare.proc, kind=2)
++> residuals(vare.proc)
++ 18 15 24 27 23 19 22
++0.28003467 0.13180094 1.72990690 0.63953155 0.37177564 0.11807007 0.38558576
++ 16 28 13 14 20 25 7
++0.28008711 1.22839229 0.65574702 0.26736966 0.02033237 0.35725398 0.28654874
++ 5 6 3 4 2 9 12
++0.69324047 0.20264726 0.29744269 0.50070811 2.23460567 0.31901411 0.21727135
++ 10 11 21
++0.32523080 0.56506437 0.78179902
++>
++>
++>
++> cleanEx()
++> nameEx("pyrifos")
++> ### * pyrifos
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: pyrifos
++> ### Title: Response of Aquatic Invertebrates to Insecticide Treatment
++> ### Aliases: pyrifos
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(pyrifos)
++> ditch <- gl(12, 1, length=132)
++> week <- gl(11, 12, labels=c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24))
++> dose <- factor(rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6), 11))
++>
++>
++>
++> cleanEx()
++> nameEx("radfit")
++> ### * radfit
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: radfit
++> ### Title: Rank - Abundance or Dominance / Diversity Models
++> ### Aliases: radfit radfit.default radfit.data.frame AIC.radfit
++> ### AIC.radfit.frame as.rad coef.radfit coef.radfit.frame deviance.radfit
++> ### deviance.radfit.frame 'logLik, radfit' 'logLik, radfit.frame'
++> ### fitted.radfit fitted.radfit.frame lines.radline lines.radfit
++> ### plot.radfit.frame plot.radfit plot.radline plot.rad radlattice
++> ### points.radline points.radfit summary.radfit.frame rad.preempt
++> ### rad.lognormal rad.zipf rad.zipfbrot rad.null predict.radline
++> ### predict.radfit predict.radfit.frame
++> ### Keywords: univar distribution
++>
++> ### ** Examples
++>
++> data(BCI)
++> mod <- rad.lognormal(BCI[5,])
++> mod
++
++RAD model: Log-Normal
++Family: poisson
++No. of species: 101
++Total abundance: 505
++
++ log.mu log.sigma Deviance AIC BIC
++ 0.951926 1.165929 17.077549 317.656487 322.886728
++> plot(mod)
++> mod <- radfit(BCI[1,])
++> ## Standard plot overlaid for all models
++> ## Pre-emption model is a line
++> plot(mod)
++> ## log for both axes: Zipf model is a line
++> plot(mod, log = "xy")
++> ## Lattice graphics separately for each model
++> radlattice(mod)
++> # Take a subset of BCI to save time and nerves
++> mod <- radfit(BCI[3:5,])
++> mod
++
++Deviance for RAD models:
++
++ 3 4 5
++Null 86.1127 49.8111 80.855
++Preemption 58.9295 39.7817 76.311
++Lognormal 29.2719 16.6588 17.078
++Zipf 50.1262 47.9108 30.936
++Mandelbrot 5.7342 5.5665 10.573
++> plot(mod, pch=".")
++>
++>
++>
++> cleanEx()
++> nameEx("rankindex")
++> ### * rankindex
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: rankindex
++> ### Title: Compares Dissimilarity Indices for Gradient Detection
++> ### Aliases: rankindex
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> ## The variables are automatically scaled
++> rankindex(varechem, varespec)
++ euc man gow bra kul
++0.2396330 0.2735087 0.2288358 0.2837910 0.2839834
++> rankindex(varechem, wisconsin(varespec))
++ euc man gow bra kul
++0.4200990 0.4215642 0.3708606 0.4215642 0.4215642
++> ## Using non vegdist indices as functions
++> funs <- list(Manhattan=function(x) dist(x, "manhattan"),
+++ Gower=function(x) cluster:::daisy(x, "gower"),
+++ Ochiai=function(x) designdist(x, "1-J/sqrt(A*B)"))
++> rankindex(scale(varechem), varespec, funs)
++Manhattan Gower Ochiai
++0.2735087 0.2288358 0.1696862
++>
++>
++>
++> cleanEx()
++> nameEx("rarefy")
++> ### * rarefy
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: rarefy
++> ### Title: Rarefaction Species Richness
++> ### Aliases: rarefy rrarefy drarefy rarecurve rareslope
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++> data(BCI)
++> S <- specnumber(BCI) # observed number of species
++> (raremax <- min(rowSums(BCI)))
++[1] 340
++> Srare <- rarefy(BCI, raremax)
++> plot(S, Srare, xlab = "Observed No. of Species", ylab = "Rarefied No. of
Species")
++> abline(0, 1)
++> rarecurve(BCI, step = 20, sample = raremax, col = "blue", cex = 0.6)
++>
++>
++>
++> cleanEx()
++> nameEx("raupcrick")
++> ### * raupcrick
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: raupcrick
++> ### Title: Raup-Crick Dissimilarity with Unequal Sampling Densities of
++> ### Species
++> ### Aliases: raupcrick
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## data set with variable species richness
++> data(sipoo)
++> ## default raupcrick
++> dr1 <- raupcrick(sipoo)
++> ## use null model "r0" of oecosimu
++> dr0 <- raupcrick(sipoo, null = "r0")
++> ## vegdist(..., method = "raup") corresponds to 'null = "r0"'
++> d <- vegdist(sipoo, "raup")
++> op <- par(mfrow=c(2,1), mar=c(4,4,1,1)+.1)
++> plot(dr1 ~ d, xlab = "Raup-Crick with Null R1", ylab="vegdist")
++> plot(dr0 ~ d, xlab = "Raup-Crick with Null R0", ylab="vegdist")
++> par(op)
++>
++> ## The calculation is essentially as in the following oecosimu() call,
++> ## except that designdist() is replaced with faster code
++> ## Not run: ##D
++> ##D oecosimu(sipoo, function(x) designdist(x, "J", "binary"), method = "r1")
++> ## End(Not run)
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++> nameEx("read.cep")
++> ### * read.cep
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: read.cep
++> ### Title: Reads a CEP (Canoco) data file
++> ### Aliases: read.cep
++> ### Keywords: IO file
++>
++> ### ** Examples
++>
++> ## Provided that you have the file `dune.spe'
++> ## Not run:
++> ##D theclassic <- read.cep("dune.spe", force=T)
++> ## End(Not run)
++>
++>
++>
++> cleanEx()
++> nameEx("renyi")
++> ### * renyi
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: renyi
++> ### Title: Renyi and Hill Diversities and Corresponding Accumulation Curves
++> ### Aliases: renyi plot.renyi renyiaccum plot.renyiaccum persp.renyiaccum
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++> data(BCI)
++> i <- sample(nrow(BCI), 12)
++> mod <- renyi(BCI[i,])
++> plot(mod)
++> mod <- renyiaccum(BCI[i,])
++> plot(mod, as.table=TRUE, col = c(1, 2, 2))
++> persp(mod)
++>
++>
++>
++> cleanEx()
++> nameEx("reorder.hclust")
++> ### * reorder.hclust
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: reorder.hclust
++> ### Title: Reorder a Hierarchical Clustering Tree
++> ### Aliases: reorder.hclust rev.hclust scores.hclust
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## reorder by water content of soil
++> data(mite, mite.env)
++> hc <- hclust(vegdist(wisconsin(sqrt(mite))))
++> ohc <- with(mite.env, reorder(hc, WatrCont))
++> plot(hc)
++> plot(ohc)
++>
++> ## label leaves by the observed value, and each branching point
++> ## (internal node) by the cluster mean
++> with(mite.env, plot(ohc, labels=round(WatrCont), cex=0.7))
++> ordilabel(scores(ohc), label=round(ohc$value), cex=0.7)
++>
++> ## Slightly different from reordered 'dendrogram' which ignores group
++> ## sizes in assessing means.
++> den <- as.dendrogram(hc)
++> den <- with(mite.env, reorder(den, WatrCont, agglo.FUN = mean))
++> plot(den)
++>
++>
++>
++> cleanEx()
++> nameEx("scores")
++> ### * scores
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: scores
++> ### Title: Get Species or Site Scores from an Ordination
++> ### Aliases: scores scores.default scores.lda
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> vare.pca <- prcomp(varespec)
++> scores(vare.pca, choices=c(1,2))
++ PC1 PC2
++18 -10.7847878 18.7094315
++15 -27.8036826 -11.7414745
++24 -25.6919559 -14.5399684
++27 -31.7820166 -31.2216800
++23 -19.6315869 -2.5541193
++19 -0.2413294 -11.4974077
++22 -26.6771373 -12.3140897
++16 -21.9230366 0.4449159
++28 -39.6083051 -41.8877392
++13 -4.0664328 20.4191153
++14 -18.4416245 5.4406988
++20 -17.3999191 2.3653380
++25 -25.1673547 -13.2508067
++7 -11.4065430 41.7356300
++5 -8.4243752 45.3805255
++6 -2.0759474 36.9311222
++3 39.8617580 8.0590041
++4 13.1065901 12.8377217
++2 57.6827011 -4.8983565
++9 63.3138332 -22.4481549
++12 44.1073111 -10.1653935
++10 64.9418975 -16.7633564
++11 11.5313633 3.9720890
++21 -3.4194194 -3.0130455
++>
++>
++>
++> cleanEx()
++> nameEx("screeplot.cca")
++> ### * screeplot.cca
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: screeplot.cca
++> ### Title: Screeplots for Ordination Results and Broken Stick Distributions
++> ### Aliases: screeplot.cca screeplot.princomp screeplot.prcomp
++> ### screeplot.decorana bstick bstick.default bstick.cca bstick.prcomp
++> ### bstick.princomp bstick.decorana
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> vare.pca <- rda(varespec, scale = TRUE)
++> bstick(vare.pca)
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7
PC8
++7.1438620 5.2308185 4.2742968 3.6366156 3.1583548 2.7757461 2.4569055
2.1836136
++ PC9 PC10 PC11 PC12 PC13 PC14 PC15
PC16
++1.9444831 1.7319228 1.5406184 1.3667054 1.2072851 1.0601279 0.9234819
0.7959457
++ PC17 PC18 PC19 PC20 PC21 PC22 PC23
++0.6763805 0.5638485 0.4575683 0.3568818 0.2612296 0.1701323 0.0831758
++> screeplot(vare.pca, bstick = TRUE, type = "lines")
++>
++>
++>
++> cleanEx()
++> nameEx("simper")
++> ### * simper
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: simper
++> ### Title: Similarity Percentages
++> ### Aliases: simper summary.simper
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> (sim <- with(dune.env, simper(dune, Management)))
++cumulative contributions of most influential species:
++
++$SF_BF
++ Agrostol Alopgeni Lolipere Trifrepe Poatriv Scorautu Bromhord
++0.09824271 0.18254830 0.25956958 0.33367870 0.40734444 0.47729205 0.53120026
++ Achimill Planlanc Elymrepe Bracruta
++0.57946526 0.62522255 0.67016196 0.71098133
++
++$SF_HF
++ Agrostol Alopgeni Lolipere Planlanc Rumeacet Elymrepe Poatriv
++0.08350879 0.16534834 0.23934930 0.30843624 0.37716139 0.43334492 0.48351753
++ Bracruta Eleopalu Poaprat Anthodor Sagiproc Trifprat
++0.52804045 0.57205850 0.61423981 0.65549838 0.69628951 0.73696831
++
++$SF_NM
++ Poatriv Alopgeni Agrostol Lolipere Eleopalu Poaprat Bracruta
Elymrepe
++0.1013601 0.1935731 0.2667383 0.3377578 0.3999419 0.4526707 0.5044725
0.5505643
++ Scorautu Trifrepe Sagiproc Salirepe
++0.5926117 0.6320111 0.6712478 0.7091528
++
++$BF_HF
++ Rumeacet Poatriv Planlanc Bromhord Lolipere Elymrepe Trifrepe
++0.08163219 0.15193797 0.21918333 0.27967181 0.33969561 0.39843338 0.45298204
++ Anthodor Achimill Bracruta Alopgeni Trifprat Juncarti
++0.50276849 0.55222648 0.60021994 0.64584333 0.69126471 0.73366621
++
++$BF_NM
++ Lolipere Poatriv Poaprat Trifrepe Bromhord Bracruta Eleopalu
Agrostol
++0.1242718 0.1992126 0.2711756 0.3414609 0.3958520 0.4448077 0.4910724
0.5369083
++ Achimill Scorautu Anthodor Planlanc
++0.5823926 0.6253645 0.6638182 0.7012577
++
++$HF_NM
++ Poatriv Lolipere Rumeacet Poaprat Planlanc Bracruta Eleopalu
++0.09913221 0.17468460 0.23917190 0.29701331 0.35469313 0.40365488 0.44804851
++ Agrostol Trifrepe Elymrepe Anthodor Juncarti Trifprat Salirepe
++0.49226546 0.53434466 0.57564661 0.61543243 0.65341300 0.68921695 0.72432408
++
++> summary(sim)
++
++Contrast: SF_BF
++
++ average sd ratio ava avb cumsum
++Agrostol 0.061374 0.034193 1.7949 4.6667 0.0000 0.09824
++Alopgeni 0.052667 0.036476 1.4439 4.3333 0.6667 0.18255
++Lolipere 0.048116 0.039445 1.2198 3.0000 6.0000 0.25957
++Trifrepe 0.046297 0.025525 1.8138 1.3333 4.6667 0.33368
++Poatriv 0.046020 0.033801 1.3615 4.6667 3.6667 0.40734
++Scorautu 0.043697 0.024922 1.7534 1.3333 4.3333 0.47729
++Bromhord 0.033677 0.025860 1.3023 0.5000 2.6667 0.53120
++Achimill 0.030152 0.020821 1.4482 0.1667 2.3333 0.57947
++Planlanc 0.028585 0.021549 1.3265 0.0000 2.0000 0.62522
++Elymrepe 0.028074 0.029778 0.9428 2.0000 1.3333 0.67016
++Bracruta 0.025501 0.023902 1.0669 2.0000 2.0000 0.71098
++Poaprat 0.025129 0.023967 1.0485 2.5000 4.0000 0.75121
++Sagiproc 0.024326 0.022149 1.0983 1.8333 0.6667 0.79014
++Bellpere 0.019859 0.017088 1.1622 0.6667 1.6667 0.82193
++Eleopalu 0.018611 0.042958 0.4333 1.3333 0.0000 0.85172
++Anthodor 0.017543 0.025804 0.6798 0.0000 1.3333 0.87981
++Juncbufo 0.016031 0.023708 0.6762 1.1667 0.0000 0.90547
++Vicilath 0.014671 0.013306 1.1026 0.0000 1.0000 0.92895
++Hyporadi 0.010286 0.015198 0.6768 0.0000 0.6667 0.94542
++Ranuflam 0.009306 0.013595 0.6845 0.6667 0.0000 0.96031
++Juncarti 0.006979 0.016109 0.4333 0.5000 0.0000 0.97148
++Callcusp 0.006979 0.016109 0.4333 0.5000 0.0000 0.98266
++Rumeacet 0.004526 0.010444 0.4333 0.3333 0.0000 0.98990
++Cirsarve 0.003983 0.009185 0.4336 0.3333 0.0000 0.99628
++Chenalbu 0.002326 0.005370 0.4333 0.1667 0.0000 1.00000
++Airaprae 0.000000 0.000000 NaN 0.0000 0.0000 1.00000
++Comapalu 0.000000 0.000000 NaN 0.0000 0.0000 1.00000
++Empenigr 0.000000 0.000000 NaN 0.0000 0.0000 1.00000
++Salirepe 0.000000 0.000000 NaN 0.0000 0.0000 1.00000
++Trifprat 0.000000 0.000000 NaN 0.0000 0.0000 1.00000
++
++Contrast: SF_HF
++
++ average sd ratio ava avb cumsum
++Agrostol 0.047380 0.031273 1.5151 4.6667 1.4 0.08351
++Alopgeni 0.046433 0.032897 1.4115 4.3333 1.6 0.16535
++Lolipere 0.041986 0.027007 1.5546 3.0000 4.0 0.23935
++Planlanc 0.039198 0.033208 1.1804 0.0000 3.0 0.30844
++Rumeacet 0.038992 0.027369 1.4247 0.3333 3.2 0.37716
++Elymrepe 0.031877 0.029550 1.0787 2.0000 2.0 0.43334
++Poatriv 0.028466 0.021522 1.3227 4.6667 4.8 0.48352
++Bracruta 0.025261 0.021044 1.2004 2.0000 2.8 0.52804
++Eleopalu 0.024974 0.038877 0.6424 1.3333 0.8 0.57206
++Poaprat 0.023932 0.019180 1.2478 2.5000 3.4 0.61424
++Anthodor 0.023409 0.021430 1.0923 0.0000 1.8 0.65550
++Sagiproc 0.023144 0.020479 1.1301 1.8333 0.8 0.69629
++Trifprat 0.023080 0.023432 0.9850 0.0000 1.8 0.73697
++Juncarti 0.022850 0.025677 0.8899 0.5000 1.6 0.77724
++Trifrepe 0.022383 0.019487 1.1486 1.3333 2.8 0.81669
++Juncbufo 0.021643 0.022237 0.9733 1.1667 1.2 0.85484
++Scorautu 0.020509 0.016422 1.2489 1.3333 2.8 0.89099
++Achimill 0.015183 0.011393 1.3326 0.1667 1.2 0.91775
++Bromhord 0.013375 0.014504 0.9222 0.5000 0.8 0.94132
++Ranuflam 0.010661 0.013387 0.7964 0.6667 0.4 0.96011
++Bellpere 0.009991 0.012571 0.7948 0.6667 0.4 0.97772
++Callcusp 0.006623 0.015076 0.4393 0.5000 0.0 0.98939
++Cirsarve 0.003809 0.008669 0.4394 0.3333 0.0 0.99611
++Chenalbu 0.002208 0.005025 0.4393 0.1667 0.0 1.00000
++Airaprae 0.000000 0.000000 NaN 0.0000 0.0 1.00000
++Comapalu 0.000000 0.000000 NaN 0.0000 0.0 1.00000
++Empenigr 0.000000 0.000000 NaN 0.0000 0.0 1.00000
++Hyporadi 0.000000 0.000000 NaN 0.0000 0.0 1.00000
++Salirepe 0.000000 0.000000 NaN 0.0000 0.0 1.00000
++Vicilath 0.000000 0.000000 NaN 0.0000 0.0 1.00000
++
++Contrast: SF_NM
++
++ average sd ratio ava avb cumsum
++Poatriv 0.078284 0.040947 1.9118 4.6667 0.0000 0.1014
++Alopgeni 0.071219 0.046958 1.5167 4.3333 0.0000 0.1936
++Agrostol 0.056508 0.044176 1.2792 4.6667 2.1667 0.2667
++Lolipere 0.054851 0.059914 0.9155 3.0000 0.3333 0.3378
++Eleopalu 0.048027 0.047168 1.0182 1.3333 2.1667 0.3999
++Poaprat 0.040724 0.031790 1.2810 2.5000 0.6667 0.4527
++Bracruta 0.040008 0.034398 1.1631 2.0000 2.8333 0.5045
++Elymrepe 0.035598 0.038515 0.9243 2.0000 0.0000 0.5506
++Scorautu 0.032475 0.034813 0.9328 1.3333 3.1667 0.5926
++Trifrepe 0.030430 0.031634 0.9619 1.3333 1.8333 0.6320
++Sagiproc 0.030304 0.030477 0.9943 1.8333 0.5000 0.6712
++Salirepe 0.029275 0.032014 0.9144 0.0000 1.8333 0.7092
++Anthodor 0.024541 0.036694 0.6688 0.0000 1.3333 0.7409
++Callcusp 0.022763 0.029443 0.7731 0.5000 1.1667 0.7704
++Ranuflam 0.022566 0.022819 0.9889 0.6667 1.3333 0.7996
++Juncarti 0.022543 0.028598 0.7883 0.5000 1.1667 0.8288
++Hyporadi 0.020108 0.031291 0.6426 0.0000 1.1667 0.8548
++Juncbufo 0.019860 0.029034 0.6840 1.1667 0.0000 0.8806
++Planlanc 0.015420 0.022769 0.6772 0.0000 0.8333 0.9005
++Airaprae 0.014883 0.021881 0.6802 0.0000 0.8333 0.9198
++Bellpere 0.012317 0.015921 0.7737 0.6667 0.3333 0.9357
++Comapalu 0.011883 0.017407 0.6826 0.0000 0.6667 0.9511
++Achimill 0.009294 0.014931 0.6224 0.1667 0.3333 0.9632
++Bromhord 0.007172 0.016333 0.4391 0.5000 0.0000 0.9724
++Rumeacet 0.005590 0.012751 0.4384 0.3333 0.0000 0.9797
++Empenigr 0.005225 0.012001 0.4354 0.0000 0.3333 0.9864
++Cirsarve 0.004782 0.010889 0.4391 0.3333 0.0000 0.9926
++Chenalbu 0.002893 0.006602 0.4382 0.1667 0.0000 0.9964
++Vicilath 0.002792 0.006425 0.4345 0.0000 0.1667 1.0000
++Trifprat 0.000000 0.000000 NaN 0.0000 0.0000 1.0000
++
++Contrast: BF_HF
++
++ average sd ratio ava avb cumsum
++Rumeacet 0.038666 0.02606 1.4838 0.0000 3.2 0.08163
++Poatriv 0.033301 0.02579 1.2911 3.6667 4.8 0.15194
++Planlanc 0.031852 0.01830 1.7401 2.0000 3.0 0.21918
++Bromhord 0.028651 0.01799 1.5926 2.6667 0.8 0.27967
++Lolipere 0.028431 0.02215 1.2834 6.0000 4.0 0.33970
++Elymrepe 0.027822 0.02959 0.9404 1.3333 2.0 0.39843
++Trifrepe 0.025838 0.01656 1.5603 4.6667 2.8 0.45298
++Anthodor 0.023582 0.02042 1.1547 1.3333 1.8 0.50277
++Achimill 0.023426 0.01474 1.5893 2.3333 1.2 0.55223
++Bracruta 0.022733 0.01802 1.2617 2.0000 2.8 0.60022
++Alopgeni 0.021610 0.02308 0.9363 0.6667 1.6 0.64584
++Trifprat 0.021514 0.02207 0.9747 0.0000 1.8 0.69126
++Juncarti 0.020084 0.02555 0.7860 0.0000 1.6 0.73367
++Scorautu 0.019318 0.01356 1.4241 4.3333 2.8 0.77445
++Bellpere 0.018290 0.01486 1.2305 1.6667 0.4 0.81306
++Agrostol 0.017605 0.02284 0.7708 0.0000 1.4 0.85023
++Juncbufo 0.015000 0.02066 0.7260 0.0000 1.2 0.88190
++Vicilath 0.012848 0.01140 1.1274 1.0000 0.0 0.90903
++Sagiproc 0.011685 0.01297 0.9008 0.6667 0.8 0.93369
++Eleopalu 0.010169 0.02111 0.4817 0.0000 0.8 0.95516
++Hyporadi 0.008950 0.01312 0.6824 0.6667 0.0 0.97406
++Poaprat 0.007203 0.01010 0.7133 4.0000 3.4 0.98927
++Ranuflam 0.005084 0.01055 0.4817 0.0000 0.4 1.00000
++Airaprae 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++Chenalbu 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++Cirsarve 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++Comapalu 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++Empenigr 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++Salirepe 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++Callcusp 0.000000 0.00000 NaN 0.0000 0.0 1.00000
++
++Contrast: BF_NM
++
++ average sd ratio ava avb cumsum
++Lolipere 0.090681 0.02644 3.4292 6.0000 0.3333 0.1243
++Poatriv 0.054684 0.04465 1.2248 3.6667 0.0000 0.1992
++Poaprat 0.052511 0.01813 2.8966 4.0000 0.6667 0.2712
++Trifrepe 0.051287 0.02756 1.8611 4.6667 1.8333 0.3415
++Bromhord 0.039689 0.02920 1.3590 2.6667 0.0000 0.3959
++Bracruta 0.035723 0.02869 1.2452 2.0000 2.8333 0.4448
++Eleopalu 0.033759 0.03573 0.9449 0.0000 2.1667 0.4911
++Agrostol 0.033446 0.03473 0.9630 0.0000 2.1667 0.5369
++Achimill 0.033190 0.02338 1.4198 2.3333 0.3333 0.5824
++Scorautu 0.031356 0.02026 1.5480 4.3333 3.1667 0.6254
++Anthodor 0.028060 0.03295 0.8517 1.3333 1.3333 0.6638
++Planlanc 0.027319 0.02193 1.2458 2.0000 0.8333 0.7013
++Salirepe 0.026770 0.02927 0.9145 0.0000 1.8333 0.7379
++Bellpere 0.023529 0.01909 1.2322 1.6667 0.3333 0.7702
++Hyporadi 0.021721 0.02450 0.8864 0.6667 1.1667 0.8000
++Ranuflam 0.020314 0.02275 0.8928 0.0000 1.3333 0.8278
++Elymrepe 0.019993 0.02926 0.6833 1.3333 0.0000 0.8552
++Callcusp 0.017833 0.02681 0.6653 0.0000 1.1667 0.8796
++Juncarti 0.017694 0.02600 0.6806 0.0000 1.1667 0.9039
++Vicilath 0.015773 0.01447 1.0902 1.0000 0.1667 0.9255
++Sagiproc 0.015432 0.01857 0.8310 0.6667 0.5000 0.9466
++Airaprae 0.013410 0.01969 0.6809 0.0000 0.8333 0.9650
++Comapalu 0.010739 0.01571 0.6835 0.0000 0.6667 0.9797
++Alopgeni 0.009997 0.01463 0.6833 0.6667 0.0000 0.9934
++Empenigr 0.004787 0.01105 0.4332 0.0000 0.3333 1.0000
++Chenalbu 0.000000 0.00000 NaN 0.0000 0.0000 1.0000
++Cirsarve 0.000000 0.00000 NaN 0.0000 0.0000 1.0000
++Juncbufo 0.000000 0.00000 NaN 0.0000 0.0000 1.0000
++Rumeacet 0.000000 0.00000 NaN 0.0000 0.0000 1.0000
++Trifprat 0.000000 0.00000 NaN 0.0000 0.0000 1.0000
++
++Contrast: HF_NM
++
++ average sd ratio ava avb cumsum
++Poatriv 0.071553 0.013681 5.2302 4.8 0.0000 0.09913
++Lolipere 0.054533 0.029625 1.8408 4.0 0.3333 0.17468
++Rumeacet 0.046546 0.030806 1.5109 3.2 0.0000 0.23917
++Poaprat 0.041750 0.018852 2.2146 3.4 0.6667 0.29701
++Planlanc 0.041633 0.029560 1.4084 3.0 0.8333 0.35469
++Bracruta 0.035340 0.020104 1.7579 2.8 2.8333 0.40365
++Eleopalu 0.032043 0.032315 0.9916 0.8 2.1667 0.44805
++Agrostol 0.031915 0.028887 1.1048 1.4 2.1667 0.49227
++Trifrepe 0.030372 0.022871 1.3280 2.8 1.8333 0.53434
++Elymrepe 0.029811 0.038676 0.7708 2.0 0.0000 0.57565
++Anthodor 0.028717 0.024799 1.1580 1.8 1.3333 0.61543
++Juncarti 0.027414 0.028537 0.9607 1.6 1.1667 0.65341
++Trifprat 0.025843 0.025972 0.9951 1.8 0.0000 0.68922
++Salirepe 0.025340 0.027291 0.9285 0.0 1.8333 0.72432
++Alopgeni 0.024459 0.032399 0.7549 1.6 0.0000 0.75821
++Scorautu 0.020703 0.014125 1.4658 2.8 3.1667 0.78689
++Ranuflam 0.019285 0.019939 0.9672 0.4 1.3333 0.81361
++Juncbufo 0.018181 0.024648 0.7376 1.2 0.0000 0.83880
++Hyporadi 0.017141 0.026548 0.6457 0.0 1.1667 0.86255
++Callcusp 0.016833 0.024901 0.6760 0.0 1.1667 0.88587
++Achimill 0.016555 0.014900 1.1111 1.2 0.3333 0.90881
++Sagiproc 0.015282 0.016535 0.9243 0.8 0.5000 0.92998
++Airaprae 0.012605 0.018243 0.6910 0.0 0.8333 0.94744
++Bromhord 0.012094 0.015169 0.7973 0.8 0.0000 0.96420
++Comapalu 0.010105 0.014556 0.6942 0.0 0.6667 0.97820
++Bellpere 0.008801 0.013732 0.6409 0.4 0.3333 0.99039
++Empenigr 0.004536 0.010325 0.4393 0.0 0.3333 0.99668
++Vicilath 0.002399 0.005461 0.4393 0.0 0.1667 1.00000
++Chenalbu 0.000000 0.000000 NaN 0.0 0.0000 1.00000
++Cirsarve 0.000000 0.000000 NaN 0.0 0.0000 1.00000
++Permutation: free
++Number of permutations: 0
++>
++>
++>
++> cleanEx()
++> nameEx("simulate.rda")
++> ### * simulate.rda
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: simulate.rda
++> ### Title: Simulate Responses with Gaussian Error or Permuted Residuals for
++> ### Constrained Ordination
++> ### Aliases: simulate.rda simulate.cca simulate.capscale
++> ### Keywords: models datagen multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- rda(dune ~ Moisture + Management, dune.env)
++> ## One simulation
++> update(mod, simulate(mod) ~ .)
++Call: rda(formula = simulate(mod) ~ Moisture + Management, data =
++dune.env)
++
++ Inertia Proportion Rank
++Total 82.8232 1.0000
++Constrained 52.6966 0.6363 6
++Unconstrained 30.1266 0.3637 13
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
++22.011 15.693 6.034 4.165 3.265 1.529
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
++6.524 4.747 3.703 3.119 2.963 2.279 1.782 1.440 1.251 0.858 0.613 0.477 0.369
++
++> ## An impression of confidence regions of site scores
++> plot(mod, display="sites")
++> for (i in 1:5) lines(procrustes(mod, update(mod, simulate(mod) ~ .)),
col="blue")
++> ## Simulate a set of null communities with permutation of residuals
++> simulate(mod, indx = shuffleSet(nrow(dune), 99))
++An object of class “simulate.rda”
++‘simulate index’ method (abundance, non-sequential)
++20 x 30 matrix
++Number of permuted matrices = 99
++
++>
++>
++>
++> cleanEx()
++> nameEx("sipoo")
++> ### * sipoo
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: sipoo
++> ### Title: Birds in the Archipelago of Sipoo (Sibbo)
++> ### Aliases: sipoo
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(sipoo)
++> ## Areas of the islands in hectares
++> sipoo.area <- c(1.1, 2.1, 2.2, 3.1, 3.5, 5.8, 6, 6.1, 6.5, 11.4, 13,
+++ 14.5, 16.1 ,17.5, 28.7, 40.5, 104.5, 233)
++>
++>
++>
++> cleanEx()
++> nameEx("spantree")
++> ### * spantree
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: spantree
++> ### Title: Minimum Spanning Tree
++> ### Aliases: spantree cophenetic.spantree as.hclust.spantree plot.spantree
++> ### lines.spantree spandepth
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune)
++> dis <- vegdist(dune)
++> tr <- spantree(dis)
++> ## Add tree to a metric scaling
++> plot(tr, cmdscale(dis), type = "t")
++> ## Find a configuration to display the tree neatly
++> plot(tr, type = "t")
++Initial stress : 0.03111
++stress after 10 iters: 0.01302, magic = 0.500
++stress after 20 iters: 0.01139, magic = 0.500
++stress after 30 iters: 0.01118, magic = 0.500
++stress after 40 iters: 0.01114, magic = 0.500
++> ## Depths of nodes
++> depths <- spandepth(tr)
++> plot(tr, type = "t", label = depths)
++Initial stress : 0.03111
++stress after 10 iters: 0.01302, magic = 0.500
++stress after 20 iters: 0.01139, magic = 0.500
++stress after 30 iters: 0.01118, magic = 0.500
++stress after 40 iters: 0.01114, magic = 0.500
++> ## Plot as a dendrogram
++> cl <- as.hclust(tr)
++> plot(cl)
++> ## cut hclust tree to classes and show in colours in spantree
++> plot(tr, col = cutree(cl, 5), pch=16)
++Initial stress : 0.03111
++stress after 10 iters: 0.01302, magic = 0.500
++stress after 20 iters: 0.01139, magic = 0.500
++stress after 30 iters: 0.01118, magic = 0.500
++stress after 40 iters: 0.01114, magic = 0.500
++>
++>
++>
++> cleanEx()
++> nameEx("specaccum")
++> ### * specaccum
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: specaccum
++> ### Title: Species Accumulation Curves
++> ### Aliases: specaccum print.specaccum summary.specaccum plot.specaccum
++> ### lines.specaccum boxplot.specaccum fitspecaccum plot.fitspecaccum
++> ### lines.fitspecaccum predict.specaccum predict.fitspecaccum
++> ### AIC.fitspecaccum deviance.fitspecaccum specslope
++> ### Keywords: univar models
++>
++> ### ** Examples
++>
++> data(BCI)
++> sp1 <- specaccum(BCI)
++Warning in cor(x > 0) : the standard deviation is zero
++> sp2 <- specaccum(BCI, "random")
++> sp2
++Species Accumulation Curve
++Accumulation method: random, with 100 permutations
++Call: specaccum(comm = BCI, method = "random")
++
++
++Sites 1.00000 2.00000 3.00000 4.00000 5.00000 6.00000 7.00000
++Richness 91.34000 121.01000 138.72000 150.54000 159.54000 166.50000 171.82000
++sd 7.21001 6.92892 6.74811 6.50939 6.12565 5.93483 5.60371
++
++Sites 8.00000 9.00000 10.00000 11.00000 12.00000 13.00000 14.00000
++Richness 176.18000 179.79000 182.73000 185.95000 188.59000 191.08000 193.10000
++sd 5.18229 5.12923 5.17542 4.71913 4.52177 4.42144 4.37278
++
++Sites 15.00000 16.00000 17.0000 18.00000 19.00000 20.00000 21.00000
++Richness 194.92000 196.87000 198.2700 199.67000 201.00000 202.47000 203.95000
++sd 4.52754 4.42138 4.2873 4.03283 4.00505 3.96819 3.95269
++
++Sites 22.00000 23.0000 24.00000 25.00000 26.00000 27.00000 28.0000
++Richness 205.18000 206.2100 207.23000 208.38000 209.39000 210.54000 211.4300
++sd 3.77493 3.6826 3.70055 3.32356 3.32391 3.31638 3.1567
++
++Sites 29.0000 30.00000 31.00000 32.00000 33.00000 34.00000 35.00000
++Richness 212.2900 213.23000 214.10000 214.88000 215.62000 216.37000 217.00000
++sd 3.0987 2.95712 2.76157 2.57525 2.52974 2.58846 2.48633
++
++Sites 36.00000 37.00000 38.00000 39.00000 40.00000 41.00000 42.00000
++Richness 217.55000 218.16000 218.77000 219.34000 219.97000 220.60000 221.21000
++sd 2.37144 2.36438 2.21042 2.04109 2.07172 1.96433 1.94518
++
++Sites 43.00000 44.00000 45.00 46.00000 47.00000 48.00000 49.00000
50
++Richness 221.85000 222.36000 222.89 223.32000 223.71000 224.17000 224.65000
225
++sd 1.77169 1.58605 1.49 1.47628 1.20851 0.97499 0.60927 0
++> summary(sp2)
++ 1 sites 2 sites 3 sites 4 sites
++ Min. : 77.00 Min. :105.0 Min. :123.0 Min. :135.0
++ 1st Qu.: 86.00 1st Qu.:116.0 1st Qu.:133.8 1st Qu.:145.8
++ Median : 91.00 Median :120.5 Median :140.0 Median :150.5
++ Mean : 91.34 Mean :121.0 Mean :138.7 Mean :150.5
++ 3rd Qu.: 97.00 3rd Qu.:127.0 3rd Qu.:144.0 3rd Qu.:155.0
++ Max. :109.00 Max. :136.0 Max. :158.0 Max. :162.0
++ 5 sites 6 sites 7 sites 8 sites
++ Min. :144.0 Min. :152.0 Min. :156.0 Min. :160.0
++ 1st Qu.:155.0 1st Qu.:162.0 1st Qu.:168.0 1st Qu.:173.0
++ Median :159.5 Median :167.0 Median :173.0 Median :177.0
++ Mean :159.5 Mean :166.5 Mean :171.8 Mean :176.2
++ 3rd Qu.:164.0 3rd Qu.:171.0 3rd Qu.:175.2 3rd Qu.:179.0
++ Max. :174.0 Max. :180.0 Max. :183.0 Max. :187.0
++ 9 sites 10 sites 11 sites 12 sites
++ Min. :161.0 Min. :164.0 Min. :174.0 Min. :178.0
++ 1st Qu.:177.0 1st Qu.:179.0 1st Qu.:182.8 1st Qu.:186.0
++ Median :180.0 Median :184.0 Median :187.0 Median :189.0
++ Mean :179.8 Mean :182.7 Mean :185.9 Mean :188.6
++ 3rd Qu.:183.0 3rd Qu.:186.0 3rd Qu.:188.0 3rd Qu.:191.0
++ Max. :194.0 Max. :196.0 Max. :198.0 Max. :199.0
++ 13 sites 14 sites 15 sites 16 sites
++ Min. :179.0 Min. :181.0 Min. :185.0 Min. :186.0
++ 1st Qu.:188.0 1st Qu.:191.0 1st Qu.:192.0 1st Qu.:194.0
++ Median :191.0 Median :193.0 Median :195.0 Median :197.0
++ Mean :191.1 Mean :193.1 Mean :194.9 Mean :196.9
++ 3rd Qu.:194.0 3rd Qu.:196.0 3rd Qu.:198.0 3rd Qu.:200.0
++ Max. :201.0 Max. :203.0 Max. :205.0 Max. :207.0
++ 17 sites 18 sites 19 sites 20 sites 21 sites
++ Min. :187.0 Min. :189.0 Min. :189 Min. :189.0 Min. :189.0
++ 1st Qu.:195.8 1st Qu.:197.0 1st Qu.:199 1st Qu.:200.0 1st Qu.:202.0
++ Median :198.0 Median :200.0 Median :201 Median :202.5 Median :204.0
++ Mean :198.3 Mean :199.7 Mean :201 Mean :202.5 Mean :203.9
++ 3rd Qu.:201.0 3rd Qu.:202.2 3rd Qu.:204 3rd Qu.:205.0 3rd Qu.:207.0
++ Max. :208.0 Max. :209.0 Max. :211 Max. :212.0 Max. :213.0
++ 22 sites 23 sites 24 sites 25 sites
++ Min. :194.0 Min. :195.0 Min. :197.0 Min. :198.0
++ 1st Qu.:203.0 1st Qu.:204.0 1st Qu.:205.0 1st Qu.:206.0
++ Median :205.0 Median :206.5 Median :207.0 Median :209.0
++ Mean :205.2 Mean :206.2 Mean :207.2 Mean :208.4
++ 3rd Qu.:207.2 3rd Qu.:209.0 3rd Qu.:210.0 3rd Qu.:211.0
++ Max. :213.0 Max. :215.0 Max. :216.0 Max. :216.0
++ 26 sites 27 sites 28 sites 29 sites
++ Min. :199.0 Min. :201.0 Min. :202.0 Min. :203.0
++ 1st Qu.:207.0 1st Qu.:208.8 1st Qu.:210.0 1st Qu.:211.0
++ Median :209.0 Median :211.0 Median :211.0 Median :212.0
++ Mean :209.4 Mean :210.5 Mean :211.4 Mean :212.3
++ 3rd Qu.:212.0 3rd Qu.:213.0 3rd Qu.:214.0 3rd Qu.:214.0
++ Max. :218.0 Max. :218.0 Max. :218.0 Max. :219.0
++ 30 sites 31 sites 32 sites 33 sites
++ Min. :203.0 Min. :204.0 Min. :206.0 Min. :206.0
++ 1st Qu.:211.0 1st Qu.:212.0 1st Qu.:213.0 1st Qu.:214.0
++ Median :213.0 Median :214.0 Median :215.0 Median :216.0
++ Mean :213.2 Mean :214.1 Mean :214.9 Mean :215.6
++ 3rd Qu.:215.0 3rd Qu.:216.0 3rd Qu.:217.0 3rd Qu.:218.0
++ Max. :220.0 Max. :220.0 Max. :220.0 Max. :220.0
++ 34 sites 35 sites 36 sites 37 sites 38 sites
++ Min. :208.0 Min. :209 Min. :209.0 Min. :212.0 Min. :214.0
++ 1st Qu.:214.8 1st Qu.:216 1st Qu.:216.0 1st Qu.:217.0 1st Qu.:217.0
++ Median :216.0 Median :217 Median :218.0 Median :218.0 Median :219.0
++ Mean :216.4 Mean :217 Mean :217.6 Mean :218.2 Mean :218.8
++ 3rd Qu.:218.0 3rd Qu.:219 3rd Qu.:219.0 3rd Qu.:220.0 3rd Qu.:220.0
++ Max. :222.0 Max. :222 Max. :223.0 Max. :223.0 Max. :224.0
++ 39 sites 40 sites 41 sites 42 sites 43 sites
++ Min. :214.0 Min. :214 Min. :216.0 Min. :216.0 Min. :217.0
++ 1st Qu.:218.0 1st Qu.:219 1st Qu.:219.0 1st Qu.:220.0 1st Qu.:221.0
++ Median :220.0 Median :220 Median :221.0 Median :221.0 Median :222.0
++ Mean :219.3 Mean :220 Mean :220.6 Mean :221.2 Mean :221.8
++ 3rd Qu.:221.0 3rd Qu.:222 3rd Qu.:222.0 3rd Qu.:223.0 3rd Qu.:223.0
++ Max. :224.0 Max. :224 Max. :224.0 Max. :225.0 Max. :225.0
++ 44 sites 45 sites 46 sites 47 sites
++ Min. :217.0 Min. :219.0 Min. :219.0 Min. :220.0
++ 1st Qu.:221.8 1st Qu.:222.0 1st Qu.:223.0 1st Qu.:223.0
++ Median :223.0 Median :223.0 Median :224.0 Median :224.0
++ Mean :222.4 Mean :222.9 Mean :223.3 Mean :223.7
++ 3rd Qu.:223.0 3rd Qu.:224.0 3rd Qu.:224.0 3rd Qu.:225.0
++ Max. :225.0 Max. :225.0 Max. :225.0 Max. :225.0
++ 48 sites 49 sites 50 sites
++ Min. :221.0 Min. :222.0 Min. :225
++ 1st Qu.:224.0 1st Qu.:224.0 1st Qu.:225
++ Median :224.0 Median :225.0 Median :225
++ Mean :224.2 Mean :224.7 Mean :225
++ 3rd Qu.:225.0 3rd Qu.:225.0 3rd Qu.:225
++ Max. :225.0 Max. :225.0 Max. :225
++> plot(sp1, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue")
++> boxplot(sp2, col="yellow", add=TRUE, pch="+")
++> ## Fit Lomolino model to the exact accumulation
++> mod1 <- fitspecaccum(sp1, "lomolino")
++> coef(mod1)
++ Asym xmid slope
++258.440682 2.442061 1.858694
++> fitted(mod1)
++ [1] 94.34749 121.23271 137.45031 148.83053 157.45735 164.31866 169.95946
++ [8] 174.71115 178.78954 182.34254 185.47566 188.26658 190.77402 193.04337
++[15] 195.11033 197.00350 198.74606 200.35705 201.85227 203.24499 204.54643
++[22] 205.76612 206.91229 207.99203 209.01150 209.97609 210.89054 211.75903
++[29] 212.58527 213.37256 214.12386 214.84180 215.52877 216.18692 216.81820
++[36] 217.42437 218.00703 218.56767 219.10762 219.62811 220.13027 220.61514
++[43] 221.08369 221.53679 221.97528 222.39991 222.81138 223.21037 223.59747
++[50] 223.97327
++> plot(sp1)
++> ## Add Lomolino model using argument 'add'
++> plot(mod1, add = TRUE, col=2, lwd=2)
++> ## Fit Arrhenius models to all random accumulations
++> mods <- fitspecaccum(sp2, "arrh")
++> plot(mods, col="hotpink")
++> boxplot(sp2, col = "yellow", border = "blue", lty=1, cex=0.3, add= TRUE)
++> ## Use nls() methods to the list of models
++> sapply(mods$models, AIC)
++ [1] 324.2489 358.8631 357.1474 348.5675 320.8596 362.4988 339.6392 302.6209
++ [9] 325.8906 335.5387 358.2781 347.9665 305.9077 338.6679 331.4992 319.3814
++ [17] 342.8604 308.0547 303.6049 307.0464 324.5011 335.4867 362.7110 342.5750
++ [25] 278.4388 346.3987 314.5455 305.6342 358.2744 307.9340 342.8888 301.1880
++ [33] 341.8359 348.4535 328.1716 341.5565 291.4052 336.6322 349.8087 350.1049
++ [41] 349.5025 352.0225 308.1040 322.4882 322.5936 361.7398 353.0302 350.8482
++ [49] 325.5460 360.2195 345.9105 342.7948 351.8214 267.6483 332.0333 320.1204
++ [57] 313.6519 344.1718 346.6968 311.0463 334.1548 368.1168 299.7756 322.6268
++ [65] 364.3129 353.3865 327.4970 343.1699 350.5206 316.0220 302.6937 286.5891
++ [73] 329.5312 323.6312 355.3245 322.2671 339.8070 367.4518 336.2615 323.6332
++ [81] 330.1162 309.3435 352.7868 321.6770 310.2606 326.1316 342.7621 333.2059
++ [89] 321.3756 368.4900 363.6466 326.1864 292.2932 296.4067 361.3046 356.2217
++ [97] 350.8804 359.9127 344.3568 332.4569
++>
++>
++>
++> cleanEx()
++> nameEx("specpool")
++> ### * specpool
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: specpool
++> ### Title: Extrapolated Species Richness in a Species Pool
++> ### Aliases: specpool specpool2vect poolaccum summary.poolaccum
++> ### plot.poolaccum estimateR estimateR.default estimateR.matrix
++> ### estimateR.data.frame estaccumR
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> attach(dune.env)
++> pool <- specpool(dune, Management)
++> pool
++ Species chao chao.se jack1 jack1.se jack2 boot boot.se n
++BF 16 17.19048 1.5895675 19.33333 2.211083 19.83333 17.74074 1.646379 3
++HF 21 21.51429 0.9511693 23.40000 1.876166 22.05000 22.56864 1.821518 5
++NM 21 22.87500 2.1582871 26.00000 3.291403 25.73333 23.77696 2.300982 6
++SF 21 29.88889 8.6447967 27.66667 3.496029 31.40000 23.99496 1.850288 6
++> op <- par(mfrow=c(1,2))
++> boxplot(specnumber(dune) ~ Management, col="hotpink", border="cyan3",
+++ notch=TRUE)
++Warning in bxp(list(stats = c(9, 9.5, 10, 11, 12, 11, 12, 13, 13, 14, 7, :
++ some notches went outside hinges ('box'): maybe set notch=FALSE
++> boxplot(specnumber(dune)/specpool2vect(pool) ~ Management, col="hotpink",
+++ border="cyan3", notch=TRUE)
++Warning in bxp(list(stats = c(0.46551724137931, 0.491379310344828,
0.517241379310345, :
++ some notches went outside hinges ('box'): maybe set notch=FALSE
++> par(op)
++> data(BCI)
++> ## Accumulation model
++> pool <- poolaccum(BCI)
++> summary(pool, display = "chao")
++$chao
++ N Chao 2.5% 97.5% Std.Dev
++ [1,] 3 161.4109 144.6126 178.9871 9.801654
++ [2,] 4 175.3013 156.0120 200.3193 11.771507
++ [3,] 5 184.5183 163.6632 208.8254 11.966732
++ [4,] 6 191.5108 172.6320 217.6871 11.433620
++ [5,] 7 196.1425 177.1485 216.1394 10.300755
++ [6,] 8 199.7655 180.8137 221.8030 11.050654
++ [7,] 9 202.3226 187.6958 226.0312 10.284834
++ [8,] 10 205.1811 187.1814 234.5619 11.982118
++ [9,] 11 208.0594 191.3298 233.2606 11.810585
++[10,] 12 210.7932 192.0850 237.1459 11.336662
++[11,] 13 213.9566 195.3646 237.5129 11.330362
++[12,] 14 216.3246 194.9807 238.0314 11.325669
++[13,] 15 219.0071 196.3220 240.6890 12.073741
++[14,] 16 221.1155 200.1056 241.8803 11.792394
++[15,] 17 221.7125 201.6610 245.1075 11.332692
++[16,] 18 223.6046 203.9908 248.0839 11.722007
++[17,] 19 224.9023 205.7025 249.4671 11.228833
++[18,] 20 227.0483 208.6850 254.0646 13.048967
++[19,] 21 228.6833 208.6412 258.0540 12.722020
++[20,] 22 229.5879 209.7609 253.7754 11.841663
++[21,] 23 230.1766 213.1453 253.7301 10.746815
++[22,] 24 231.5115 213.9103 255.8942 11.014966
++[23,] 25 233.5683 214.6193 259.3002 11.749781
++[24,] 26 233.9382 217.4818 258.2308 11.369741
++[25,] 27 234.8832 218.6546 255.4724 10.158710
++[26,] 28 234.6476 220.7203 255.0633 9.429129
++[27,] 29 235.7344 219.8494 257.2886 10.211869
++[28,] 30 236.5167 222.7719 260.5121 9.947075
++[29,] 31 236.4638 222.9289 256.9346 8.826309
++[30,] 32 237.7034 221.8471 261.0706 9.617682
++[31,] 33 237.1767 223.4613 257.0897 9.058297
++[32,] 34 237.3228 221.7998 256.3574 9.567790
++[33,] 35 237.5354 221.4743 255.5337 9.339085
++[34,] 36 237.2440 222.2844 256.1171 8.837235
++[35,] 37 237.5675 222.4925 256.7364 9.388989
++[36,] 38 237.7059 222.9352 258.7308 9.451033
++[37,] 39 237.9622 224.8405 261.9372 9.479366
++[38,] 40 237.7631 224.9289 258.1333 8.637200
++[39,] 41 238.3850 226.1408 258.9579 8.581810
++[40,] 42 238.8268 225.7357 257.0081 9.731966
++[41,] 43 238.6941 226.0755 254.6363 8.313701
++[42,] 44 238.7041 228.3762 253.0612 7.491410
++[43,] 45 238.5832 229.5340 255.1341 6.571046
++[44,] 46 238.0307 229.2938 250.0349 5.519841
++[45,] 47 237.6838 230.0538 249.1268 4.855208
++[46,] 48 237.3636 232.3044 246.4903 3.952778
++[47,] 49 236.8121 234.3115 245.4082 2.380487
++[48,] 50 236.3732 236.3732 236.3732 0.000000
++
++attr(,"class")
++[1] "summary.poolaccum"
++> plot(pool)
++> ## Quantitative model
++> estimateR(BCI[1:5,])
++ 1 2 3 4 5
++S.obs 93.000000 84.000000 90.000000 94.000000 101.000000
++S.chao1 117.473684 117.214286 141.230769 111.550000 136.000000
++se.chao1 11.583785 15.918953 23.001405 8.919663 15.467344
++S.ACE 122.848959 117.317307 134.669844 118.729941 137.114088
++se.ACE 5.736054 5.571998 6.191618 5.367571 5.848474
++>
++>
++>
++> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
++> cleanEx()
++
++detaching ‘dune.env’
++
++> nameEx("stepacross")
++> ### * stepacross
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: stepacross
++> ### Title: Stepacross as Flexible Shortest Paths or Extended
++> ### Dissimilarities
++> ### Aliases: stepacross
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> # There are no data sets with high beta diversity in vegan, but this
++> # should give an idea.
++> data(dune)
++> dis <- vegdist(dune)
++> edis <- stepacross(dis)
++Too long or NA distances: 5 out of 190 (2.6%)
++Stepping across 190 dissimilarities...
++> plot(edis, dis, xlab = "Shortest path", ylab = "Original")
++> ## Manhattan distance have no fixed upper limit.
++> dis <- vegdist(dune, "manhattan")
++> is.na(dis) <- no.shared(dune)
++> dis <- stepacross(dis, toolong=0)
++Too long or NA distances: 5 out of 190 (2.6%)
++Stepping across 190 dissimilarities...
++>
++>
++>
++> cleanEx()
++> nameEx("stressplot.wcmdscale")
++> ### * stressplot.wcmdscale
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: stressplot.wcmdscale
++> ### Title: Display Ordination Distances Against Observed Distances in
++> ### Eigenvector Ordinations
++> ### Aliases: stressplot.wcmdscale stressplot.cca stressplot.rda
++> ### stressplot.capscale stressplot.dbrda stressplot.prcomp
++> ### stressplot.princomp
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(dune, dune.env)
++> mod <- rda(dune)
++> stressplot(mod)
++> mod <- rda(dune ~ Management, dune.env)
++> stressplot(mod, k=3)
++>
++>
++>
++> cleanEx()
++> nameEx("taxondive")
++> ### * taxondive
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: taxondive
++> ### Title: Indices of Taxonomic Diversity and Distinctness
++> ### Aliases: taxondive summary.taxondive plot.taxondive taxa2dist
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++> ## Preliminary: needs better data and some support functions
++> data(dune)
++> data(dune.taxon)
++> # Taxonomic distances from a classification table with variable step
lengths.
++> taxdis <- taxa2dist(dune.taxon, varstep=TRUE)
++> plot(hclust(taxdis), hang = -1)
++> # Indices
++> mod <- taxondive(dune, taxdis)
++> mod
++ Species Delta Delta* Lambda+ Delta+ S Delta+
++1 5.000 25.009 32.154 1592.777 51.545 257.73
++2 10.000 60.493 66.350 1426.590 66.687 666.87
++3 10.000 46.599 51.702 1395.603 70.748 707.47
++4 13.000 58.199 63.176 1258.772 73.403 954.24
++5 14.000 72.045 76.990 1018.677 77.602 1086.43
++6 11.000 76.415 83.121 858.170 80.843 889.27
++7 13.000 70.250 75.475 970.358 78.397 1019.17
++8 12.000 59.126 63.436 1079.722 74.787 897.44
++9 13.000 56.948 60.985 1220.760 71.160 925.08
++10 12.000 68.902 74.513 1028.527 77.762 933.14
++11 9.000 76.201 85.126 552.357 82.538 742.84
++12 9.000 69.555 77.792 760.886 82.614 743.52
++13 10.000 55.296 62.923 935.943 76.657 766.57
++14 7.000 77.609 89.250 341.499 88.182 617.27
++15 8.000 74.924 84.249 473.604 86.558 692.47
++16 8.000 57.843 66.539 1111.251 73.360 586.88
++17 7.000 64.823 72.408 1209.057 70.974 496.82
++18 9.000 76.731 85.773 596.607 81.401 732.61
++19 9.000 73.449 81.318 476.221 83.371 750.34
++20 8.000 78.076 87.063 520.623 87.922 703.38
++Expected 73.289 70.182 78.712
++> summary(mod)
++ Delta Delta* Delta+ sd(Delta+) z(Delta+) Pr(>|z|)
++1 25.0089 32.1543 51.5455 9.5637 -2.8405 0.004504 **
++2 60.4931 66.3497 66.6869 4.6664 -2.5769 0.009970 **
++3 46.5985 51.7024 70.7475 4.6664 -1.7067 0.087881 .
++4 58.1988 63.1763 73.4033 3.5073 -1.5135 0.130150
++5 72.0452 76.9903 77.6024 3.2187 -0.3446 0.730386
++6 76.4148 83.1205 80.8430 4.2170 0.5054 0.613259
++7 70.2500 75.4752 78.3974 3.5073 -0.0896 0.928626
++8 59.1259 63.4363 74.7865 3.8361 -1.0232 0.306220
++9 56.9481 60.9854 71.1597 3.5073 -2.1532 0.031303 *
++10 68.9021 74.5133 77.7617 3.8361 -0.2476 0.804432
++11 76.2014 85.1259 82.5379 5.2089 0.7346 0.462605
++12 69.5554 77.7922 82.6136 5.2089 0.7491 0.453792
++13 55.2961 62.9232 76.6566 4.6664 -0.4404 0.659657
++14 77.6087 89.2500 88.1818 6.7459 1.4039 0.160362
++15 74.9245 84.2485 86.5584 5.8818 1.3341 0.182178
++16 57.8435 66.5389 73.3604 5.8818 -0.9098 0.362936
++17 64.8225 72.4081 70.9740 6.7459 -1.1470 0.251377
++18 76.7314 85.7730 81.4015 5.2089 0.5164 0.605570
++19 73.4487 81.3182 83.3712 5.2089 0.8945 0.371029
++20 78.0762 87.0634 87.9221 5.8818 1.5659 0.117368
++Expected 73.2888 70.1816 78.7116
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> plot(mod)
++>
++>
++>
++> cleanEx()
++> nameEx("tolerance")
++> ### * tolerance
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: tolerance
++> ### Title: Species tolerances and sample heterogeneities
++> ### Aliases: tolerance tolerance.cca
++>
++> ### ** Examples
++>
++> data(dune)
++> data(dune.env)
++> mod <- cca(dune ~ ., data = dune.env)
++>
++> ## defaults to species tolerances
++> tolerance(mod)
++
++ Species Tolerances
++
++Scaling: 2
++
++ CCA1 CCA2
++Achimill 0.9196092 8.449914e-01
++Agrostol 0.9044440 8.718601e-01
++Airaprae 2.6654621 4.080607e-01
++Alopgeni 0.7709807 3.464157e-01
++Anthodor 1.0199113 7.575379e-01
++Bellpere 1.1104145 9.067794e-01
++Bromhord 1.0381512 5.515502e-01
++Chenalbu 0.6033850 6.883383e-15
++Cirsarve 1.3186022 1.010898e-09
++Comapalu 2.1945688 5.677653e-01
++Eleopalu 1.7007903 8.292822e-01
++Elymrepe 1.3717380 5.135604e-01
++Empenigr 2.8931006 4.260585e-09
++Hyporadi 2.2304480 5.791210e-01
++Juncarti 1.2178954 9.536362e-01
++Juncbufo 1.0888681 5.294370e-01
++Lolipere 1.0576496 7.874610e-01
++Planlanc 0.6397722 6.393173e-01
++Poaprat 1.1008934 9.166587e-01
++Poatriv 1.0732501 7.099165e-01
++Ranuflam 1.8373921 1.059420e+00
++Rumeacet 1.0088537 7.625654e-01
++Sagiproc 1.0449246 1.019154e+00
++Salirepe 2.4879997 8.654457e-02
++Scorautu 1.1072914 1.036536e+00
++Trifprat 0.5623270 2.811272e-01
++Trifrepe 0.8794567 8.738483e-01
++Vicilath 0.6164605 4.896853e-01
++Bracruta 1.1352368 1.051819e+00
++Callcusp 2.4779468 8.464129e-01
++
++>
++> ## sample heterogeneities for CCA axes 1:6
++> tolerance(mod, which = "sites", choices = 1:6)
++
++ Sample Heterogeneities
++
++Scaling: 2
++
++ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
++1 1.6160868 0.8611530 1.3611613 0.8547721 0.8880025 1.0054039
++2 0.7379270 0.4136311 0.4814279 0.2579402 0.5508780 0.3008441
++3 1.2043495 0.7279717 1.1259717 0.9726111 0.9957139 0.9500034
++4 1.1765582 0.6901907 1.1813605 0.9080998 1.0242807 0.8926357
++5 0.3568513 0.5614830 0.7904248 0.4439038 0.5395412 0.5563630
++6 0.5242810 0.6867342 1.2883496 0.8121662 0.9649406 0.8990979
++7 0.4780376 0.4993262 0.5981893 0.4002808 0.3971772 0.3037031
++8 0.7500360 0.5498552 0.6659454 0.6745113 0.6543351 0.6517439
++9 1.1531088 0.7399384 1.0907452 1.1959420 0.8945268 1.0529521
++10 0.3560047 0.5806978 0.6260266 0.7902086 0.5703606 0.5987258
++11 0.5680664 0.6016877 0.6052417 0.8408208 0.7677540 0.5231514
++12 1.3520612 1.1084494 1.3764339 1.7806761 1.4762045 1.6001441
++13 1.2059850 0.8859365 1.2589142 1.5498105 1.3904166 1.3897352
++14 1.7981550 0.6747926 0.9814690 1.1843091 0.6378377 0.7056578
++15 0.7836875 0.5294221 0.4075052 0.6010561 0.4589683 0.4276478
++16 0.6799624 0.6058313 0.4878699 0.6155630 0.6700358 0.5629790
++17 2.2689099 1.5142792 2.3067682 2.1185580 2.0873993 2.3439179
++18 1.6837720 1.4067575 1.6687447 1.8211091 1.8556056 1.6626793
++19 1.9282709 0.9816332 1.2377752 1.2116335 1.5379732 1.5270539
++20 2.5707156 1.4458326 1.5654328 1.5825992 1.6868279 1.6688951
++
++>
++>
++>
++> cleanEx()
++> nameEx("treedive")
++> ### * treedive
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: treedive
++> ### Title: Functional Diversity and Community Distances from Species Trees
++> ### Aliases: treedive treeheight treedist
++> ### Keywords: univar
++>
++> ### ** Examples
++>
++>
++> ## There is no data set on species properties yet, and we demonstrate
++> ## the methods using phylogenetic trees
++> data(dune)
++> data(dune.phylodis)
++> cl <- hclust(dune.phylodis)
++> treedive(dune, cl)
++Forced matching of 'tree' labels and 'comm' names
++ 1 2 3 4 5 6 7
8
++ 384.0913 568.8791 1172.9455 1327.9317 1426.9067 1391.1628 1479.5062
1523.0792
++ 9 10 11 12 13 14 15
16
++1460.0423 1316.4832 1366.9960 1423.5582 895.1120 1457.2705 1505.9501
1187.5165
++ 17 18 19 20
++ 517.6920 1394.5162 1470.4671 1439.5571
++> ## Significance test using Null model communities.
++> ## The current choice fixes numbers of species and picks species
++> ## proportionally to their overall frequency
++> oecosimu(dune, treedive, "r1", tree = cl, verbose = FALSE)
++Warning in oecosimu(dune, treedive, "r1", tree = cl, verbose = FALSE) :
++ nullmodel transformed 'comm' to binary data
++oecosimu object
++
++Call: oecosimu(comm = dune, nestfun = treedive, method = "r1", tree =
++cl, verbose = FALSE)
++
++nullmodel method ‘r1’ with 99 simulations
++
++alternative hypothesis: statistic is less or greater than simulated values
++
++ statistic SES mean 2.5% 50% 97.5% Pr(sim.)
++1 384.09 -1.28273 781.41 382.13 664.87 1244.3 0.09 .
++2 568.88 -2.29048 1245.21 648.63 1342.75 1602.9 0.01 **
++3 1172.95 -0.28386 1251.47 668.89 1353.45 1594.1 0.57
++4 1327.93 -0.18989 1376.56 850.14 1462.61 1689.9 0.63
++5 1426.91 -0.24932 1484.65 892.50 1544.84 1755.4 0.55
++6 1391.16 0.40503 1285.89 721.15 1364.47 1596.7 0.83
++7 1479.51 0.26497 1416.16 789.44 1480.89 1712.1 0.99
++8 1523.08 0.56404 1366.20 742.42 1453.77 1730.5 0.63
++9 1460.04 0.11145 1433.00 879.43 1498.68 1747.1 0.77
++10 1316.48 -0.11917 1348.17 798.65 1428.77 1684.6 0.57
++11 1367.00 0.73635 1164.21 630.76 1249.70 1516.6 0.51
++12 1423.56 0.90574 1147.15 565.62 1260.84 1508.1 0.35
++13 895.11 -1.04265 1207.29 628.21 1328.30 1580.5 0.51
++14 1457.27 1.52076 1005.04 533.57 1133.14 1382.6 0.03 *
++15 1505.95 1.34359 1109.43 504.18 1218.43 1511.6 0.07 .
++16 1187.52 0.47862 1040.61 585.02 1175.42 1474.2 0.99
++17 517.69 -1.44887 960.72 521.92 1120.30 1374.2 0.03 *
++18 1394.52 0.88939 1138.12 652.91 1251.28 1505.1 0.39
++19 1470.47 1.03008 1179.52 554.33 1275.38 1476.5 0.09 .
++20 1439.56 1.22599 1085.02 553.27 1235.70 1441.9 0.09 .
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Phylogenetically ordered community table
++> dtree <- treedist(dune, cl)
++> tabasco(dune, hclust(dtree), cl)
++> ## Use tree distances in capscale
++> capscale(dtree ~ 1, comm=dune)
++Call: capscale(formula = dtree ~ 1, comm = dune)
++
++ Inertia Eigenvals Rank
++Total 2.1834 2.3439
++Unconstrained 2.1834 2.3439 10
++Imaginary -0.1605 9
++Inertia is squared Treedist distance
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10
++1.1971 0.4546 0.2967 0.1346 0.1067 0.0912 0.0391 0.0190 0.0045 0.0004
++
++>
++>
++>
++> cleanEx()
++> nameEx("tsallis")
++> ### * tsallis
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: tsallis
++> ### Title: Tsallis Diversity and Corresponding Accumulation Curves
++> ### Aliases: tsallis tsallisaccum persp.tsallisaccum
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(BCI)
++> i <- sample(nrow(BCI), 12)
++> x1 <- tsallis(BCI[i,])
++> x1
++ 0 0.2 0.4 0.6 0.8 1 1.2 1.4
1.6
++14 97 43.58438 21.14034 11.153293 6.418569 4.017494 2.713442 1.956269
1.488283
++19 108 47.27018 22.32602 11.498387 6.492802 4.013094 2.692466 1.936696
1.473828
++28 84 38.19298 18.72155 9.985457 5.818617 3.693896 2.532394 1.852192
1.427230
++43 85 38.38228 18.77974 10.035449 5.867927 3.736254 2.565004 1.875593
1.443295
++10 93 41.50369 20.10059 10.639881 6.164157 3.889803 2.648176 1.922207
1.470121
++41 101 45.01956 21.66615 11.353495 6.499317 4.052495 2.729799 1.964435
1.492566
++42 86 39.74400 19.79375 10.684037 6.259100 3.966614 2.699614 1.954275
1.489483
++29 85 38.51838 18.81994 10.009321 5.819419 3.688721 2.526664 1.847408
1.423613
++27 98 43.84660 21.16041 11.110930 6.371476 3.980281 2.687154 1.938584
1.476662
++3 89 39.79074 19.34183 10.294475 6.003596 3.814060 2.612130 1.905004
1.461939
++9 89 39.96271 19.39982 10.272025 5.952153 3.761331 2.568371 1.872010
1.438318
++7 81 37.47840 18.73414 10.172688 6.004908 3.836811 2.631757 1.918091
1.469869
++ 1.8 2
++14 1.182226 0.9718626
++19 1.172481 0.9655820
++28 1.145854 0.9499296
++43 1.156578 0.9569632
++10 1.172341 0.9663808
++41 1.184546 0.9731442
++42 1.183858 0.9731849
++29 1.143249 0.9481041
++27 1.174682 0.9669962
++3 1.168492 0.9646078
++9 1.152087 0.9534257
++7 1.173083 0.9672014
++> diversity(BCI[i,],"simpson") == x1[["2"]]
++ 14 19 28 43 10 41 42 29 27 3 9 7
++TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
++> plot(x1)
++> x2 <- tsallis(BCI[i,],norm=TRUE)
++> x2
++ 0 0.2 0.4 0.6 0.8 1 1.2 1.4
++14 1 0.9134203 0.8653450 0.8483542 0.8548076 0.8762317 0.9040571 0.9313075
++19 1 0.9079031 0.8537683 0.8315799 0.8347890 0.8554245 0.8846675 0.9147428
++28 1 0.8997942 0.8397547 0.8130733 0.8129096 0.8314621 0.8602754 0.8916927
++43 1 0.8955869 0.8360372 0.8125579 0.8165505 0.8387881 0.8699334 0.9021018
++10 1 0.9001004 0.8450759 0.8255446 0.8324355 0.8561634 0.8872568 0.9180261
++41 1 0.9130269 0.8644349 0.8473152 0.8541311 0.8762204 0.9047063 0.9323803
++42 1 0.9185764 0.8746393 0.8602787 0.8675785 0.8881987 0.9141183 0.9390681
++29 1 0.8987625 0.8378269 0.8104423 0.8098004 0.8281171 0.8569302 0.8885453
++27 1 0.9112896 0.8605499 0.8410587 0.8456725 0.8661975 0.8940909 0.9221774
++3 1 0.8943582 0.8362208 0.8155352 0.8226868 0.8476046 0.8803722 0.9129172
++9 1 0.8982235 0.8387276 0.8137568 0.8156375 0.8358867 0.8656242 0.8971059
++7 1 0.9094899 0.8600339 0.8427936 0.8492653 0.8706729 0.8985570 0.9261475
++ 1.6 1.8 2
++14 0.9538903 0.9705576 0.9818818
++19 0.9406572 0.9605041 0.9745226
++28 0.9203563 0.9436787 0.9612382
++43 0.9302296 0.9522490 0.9682216
++10 0.9438798 0.9632975 0.9767719
++41 0.9550896 0.9716606 0.9827793
++42 0.9595063 0.9744477 0.9845010
++29 0.9175446 0.9412749 0.9592583
++27 0.9460502 0.9641597 0.9768635
++3 0.9403687 0.9610574 0.9754460
++9 0.9251745 0.9475646 0.9641383
++7 0.9493990 0.9669335 0.9791421
++> plot(x2)
++> mod1 <- tsallisaccum(BCI[i,])
++> plot(mod1, as.table=TRUE, col = c(1, 2, 2))
++> persp(mod1)
++> mod2 <- tsallisaccum(BCI[i,], norm=TRUE)
++> persp(mod2,theta=100,phi=30)
++>
++>
++>
++> cleanEx()
++> nameEx("varechem")
++> ### * varechem
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: varespec
++> ### Title: Vegetation and environment in lichen pastures
++> ### Aliases: varechem varespec
++> ### Keywords: datasets
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++>
++>
++>
++> cleanEx()
++> nameEx("varpart")
++> ### * varpart
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: varpart
++> ### Title: Partition the Variation of Community Matrix by 2, 3, or 4
++> ### Explanatory Matrices
++> ### Aliases: varpart varpart2 varpart3 varpart4 showvarparts plot.varpart
++> ### plot.varpart234 simpleRDA2 simpleDBRDA
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(mite)
++> data(mite.env)
++> data(mite.pcnm)
++>
++> # Two explanatory matrices -- Hellinger-transform Y
++> # Formula shortcut "~ ." means: use all variables in 'data'.
++> mod <- varpart(mite, ~ ., mite.pcnm, data=mite.env, transfo="hel")
++> mod
++
++Partition of variance in RDA
++
++Call: varpart(Y = mite, X = ~., mite.pcnm, data = mite.env, transfo =
++"hel")
++Species transformation: hellinger
++Explanatory tables:
++X1: ~.
++X2: mite.pcnm
++
++No. of explanatory tables: 2
++Total variation (SS): 27.205
++ Variance: 0.39428
++No. of observations: 70
++
++Partition table:
++ Df R.squared Adj.R.squared Testable
++[a+b] = X1 11 0.52650 0.43670 TRUE
++[b+c] = X2 22 0.62300 0.44653 TRUE
++[a+b+c] = X1+X2 33 0.75893 0.53794 TRUE
++Individual fractions
++[a] = X1|X2 11 0.09141 TRUE
++[b] 0 0.34530 FALSE
++[c] = X2|X1 22 0.10124 TRUE
++[d] = Residuals 0.46206 FALSE
++---
++Use function ‘rda’ to test significance of fractions of interest
++>
++> ## Use fill colours
++> showvarparts(2, bg = c("hotpink","skyblue"))
++> plot(mod, bg = c("hotpink","skyblue"))
++> # Alternative way of to conduct this partitioning
++> # Change the data frame with factors into numeric model matrix
++> mm <- model.matrix(~ SubsDens + WatrCont + Substrate + Shrub + Topo,
mite.env)[,-1]
++> mod <- varpart(decostand(mite, "hel"), mm, mite.pcnm)
++> # Test fraction [a] using partial RDA:
++> aFrac <- rda(decostand(mite, "hel"), mm, mite.pcnm)
++> anova(aFrac, step=200, perm.max=200)
++Permutation test for rda under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: rda(X = decostand(mite, "hel"), Y = mm, Z = mite.pcnm)
++ Df Variance F Pr(>F)
++Model 11 0.053592 1.8453 0.001 ***
++Residual 36 0.095050
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> # RsquareAdj gives the same result as component [a] of varpart
++> RsquareAdj(aFrac)
++$r.squared
++[1] 0.1359251
++
++$adj.r.squared
++[1] 0.09140797
++
++>
++> # Partition Bray-Curtis dissimilarities
++> varpart(vegdist(mite), ~ ., mite.pcnm, data = mite.env)
++
++Partition of squared Bray distance in dbRDA
++
++Call: varpart(Y = vegdist(mite), X = ~., mite.pcnm, data = mite.env)
++
++Explanatory tables:
++X1: ~.
++X2: mite.pcnm
++
++No. of explanatory tables: 2
++Total variation (SS): 14.696
++No. of observations: 70
++
++Partition table:
++ Df R.squared Adj.R.squared Testable
++[a+b] = X1 11 0.50512 0.41127 TRUE
++[b+c] = X2 22 0.60144 0.41489 TRUE
++[a+b+c] = X1+X2 33 0.74631 0.51375 TRUE
++Individual fractions
++[a] = X1|X2 11 0.09887 TRUE
++[b] 0 0.31240 FALSE
++[c] = X2|X1 22 0.10249 TRUE
++[d] = Residuals 0.48625 FALSE
++---
++Use function ‘capscale’ to test significance of fractions of interest
++> # Three explanatory matrices
++> mod <- varpart(mite, ~ SubsDens + WatrCont, ~ Substrate + Shrub + Topo,
+++ mite.pcnm, data=mite.env, transfo="hel")
++> mod
++
++Partition of variance in RDA
++
++Call: varpart(Y = mite, X = ~SubsDens + WatrCont, ~Substrate + Shrub +
++Topo, mite.pcnm, data = mite.env, transfo = "hel")
++Species transformation: hellinger
++Explanatory tables:
++X1: ~SubsDens + WatrCont
++X2: ~Substrate + Shrub + Topo
++X3: mite.pcnm
++
++No. of explanatory tables: 3
++Total variation (SS): 27.205
++ Variance: 0.39428
++No. of observations: 70
++
++Partition table:
++ Df R.square Adj.R.square Testable
++[a+d+f+g] = X1 2 0.32677 0.30667 TRUE
++[b+d+e+g] = X2 9 0.40395 0.31454 TRUE
++[c+e+f+g] = X3 22 0.62300 0.44653 TRUE
++[a+b+d+e+f+g] = X1+X2 11 0.52650 0.43670 TRUE
++[a+c+d+e+f+g] = X1+X3 24 0.67372 0.49970 TRUE
++[b+c+d+e+f+g] = X2+X3 31 0.72400 0.49884 TRUE
++[a+b+c+d+e+f+g] = All 33 0.75893 0.53794 TRUE
++Individual fractions
++[a] = X1 | X2+X3 2 0.03910 TRUE
++[b] = X2 | X1+X3 9 0.03824 TRUE
++[c] = X3 | X1+X2 22 0.10124 TRUE
++[d] 0 0.01407 FALSE
++[e] 0 0.09179 FALSE
++[f] 0 0.08306 FALSE
++[g] 0 0.17045 FALSE
++[h] = Residuals 0.46206 FALSE
++Controlling 1 table X
++[a+d] = X1 | X3 2 0.05317 TRUE
++[a+f] = X1 | X2 2 0.12216 TRUE
++[b+d] = X2 | X3 9 0.05231 TRUE
++[b+e] = X2 | X1 9 0.13003 TRUE
++[c+e] = X3 | X1 22 0.19303 TRUE
++[c+f] = X3 | X2 22 0.18429 TRUE
++---
++Use function ‘rda’ to test significance of fractions of interest
++> showvarparts(3, bg=2:4)
++> plot(mod, bg=2:4)
++> # An alternative formulation of the previous model using
++> # matrices mm1 amd mm2 and Hellinger transformed species data
++> mm1 <- model.matrix(~ SubsDens + WatrCont, mite.env)[,-1]
++> mm2 <- model.matrix(~ Substrate + Shrub + Topo, mite.env)[, -1]
++> mite.hel <- decostand(mite, "hel")
++> mod <- varpart(mite.hel, mm1, mm2, mite.pcnm)
++> # Use RDA to test fraction [a]
++> # Matrix can be an argument in formula
++> rda.result <- rda(mite.hel ~ mm1 + Condition(mm2) +
+++ Condition(as.matrix(mite.pcnm)))
++> anova(rda.result, step=200, perm.max=200)
++Permutation test for rda under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = mite.hel ~ mm1 + Condition(mm2) +
Condition(as.matrix(mite.pcnm)))
++ Df Variance F Pr(>F)
++Model 2 0.013771 2.6079 0.001 ***
++Residual 36 0.095050
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++> # Four explanatory tables
++> mod <- varpart(mite, ~ SubsDens + WatrCont, ~Substrate + Shrub + Topo,
+++ mite.pcnm[,1:11], mite.pcnm[,12:22], data=mite.env, transfo="hel")
++> mod
++
++Partition of variance in RDA
++
++Call: varpart(Y = mite, X = ~SubsDens + WatrCont, ~Substrate + Shrub +
++Topo, mite.pcnm[, 1:11], mite.pcnm[, 12:22], data = mite.env, transfo =
++"hel")
++Species transformation: hellinger
++Explanatory tables:
++X1: ~SubsDens + WatrCont
++X2: ~Substrate + Shrub + Topo
++X3: mite.pcnm[, 1:11]
++X4: mite.pcnm[, 12:22]
++
++No. of explanatory tables: 4
++Total variation (SS): 27.205
++ Variance: 0.39428
++No. of observations: 70
++
++Partition table:
++ Df R.square Adj.R.square Testable
++[aeghklno] = X1 2 0.32677 0.30667 TRUE
++[befiklmo] = X2 9 0.40395 0.31454 TRUE
++[cfgjlmno] = X3 11 0.53231 0.44361 TRUE
++[dhijkmno] = X4 11 0.09069 -0.08176 TRUE
++[abefghiklmno] = X1+X2 11 0.52650 0.43670 TRUE
++[acefghjklmno] = X1+X3 13 0.59150 0.49667 TRUE
++[adeghijklmno] = X1+X4 13 0.40374 0.26533 TRUE
++[bcefgijklmno] = X2+X3 20 0.63650 0.48813 TRUE
++[bdefhijklmno] = X2+X4 20 0.53338 0.34292 TRUE
++[cdfghijklmno] = X3+X4 22 0.62300 0.44653 TRUE
++[abcefghijklmno] = X1+X2+X3 22 0.67947 0.52944 TRUE
++[abdefghijklmno] = X1+X2+X4 22 0.61553 0.43557 TRUE
++[acdefghijklmno] = X1+X3+X4 24 0.67372 0.49970 TRUE
++[bcdefghijklmno] = X2+X3+X4 31 0.72400 0.49884 TRUE
++[abcdefghijklmno] = All 33 0.75893 0.53794 TRUE
++Individual fractions
++[a] = X1 | X2+X3+X4 2 0.03910 TRUE
++[b] = X2 | X1+X3+X4 9 0.03824 TRUE
++[c] = X3 | X1+X2+X4 11 0.10237 TRUE
++[d] = X4 | X1+X2+X3 11 0.00850 TRUE
++[e] 0 0.01407 FALSE
++[f] 0 0.13200 FALSE
++[g] 0 0.05355 FALSE
++[h] 0 0.00220 FALSE
++[i] 0 -0.00547 FALSE
++[j] 0 -0.00963 FALSE
++[k] 0 -0.00231 FALSE
++[l] 0 0.24037 FALSE
++[m] 0 -0.03474 FALSE
++[n] 0 0.02730 FALSE
++[o] 0 -0.06761 FALSE
++[p] = Residuals 0 0.46206 FALSE
++Controlling 2 tables X
++[ae] = X1 | X3+X4 2 0.05317 TRUE
++[ag] = X1 | X2+X4 2 0.09265 TRUE
++[ah] = X1 | X2+X3 2 0.04131 TRUE
++[be] = X2 | X3+X4 9 0.05231 TRUE
++[bf] = X2 | X1+X4 9 0.17024 TRUE
++[bi] = X2 | X1+X3 9 0.03277 TRUE
++[cf] = X3 | X1+X4 11 0.23437 TRUE
++[cg] = X3 | X2+X4 11 0.15592 TRUE
++[cj] = X3 | X1+X2 11 0.09274 TRUE
++[dh] = X4 | X2+X3 11 0.01071 TRUE
++[di] = X4 | X1+X3 11 0.00303 TRUE
++[dj] = X4 | X1+X2 11 -0.00113 TRUE
++Controlling 1 table X
++[aghn] = X1 | X2 2 0.12216 TRUE
++[aehk] = X1 | X3 2 0.05306 TRUE
++[aegl] = X1 | X4 2 0.34709 TRUE
++[bfim] = X2 | X1 9 0.13003 TRUE
++[beik] = X2 | X3 9 0.04452 TRUE
++[befl] = X2 | X4 9 0.42468 TRUE
++[cfjm] = X3 | X1 11 0.19000 TRUE
++[cgjn] = X3 | X2 11 0.17359 TRUE
++[cfgl] = X3 | X4 11 0.52830 TRUE
++[dijm] = X4 | X1 11 -0.04134 TRUE
++[dhjn] = X4 | X2 11 0.02837 TRUE
++[dhik] = X4 | X3 11 0.00292 TRUE
++---
++Use function ‘rda’ to test significance of fractions of interest
++> plot(mod, bg=2:5)
++> # Show values for all partitions by putting 'cutoff' low enough:
++> plot(mod, cutoff = -Inf, cex = 0.7, bg=2:5)
++>
++>
++>
++> cleanEx()
++> nameEx("vegan-package")
++> ### * vegan-package
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: vegan-package
++> ### Title: Community Ecology Package: Ordination, Diversity and
++> ### Dissimilarities
++> ### Aliases: vegan-package vegan
++> ### Keywords: package multivariate univar models spatial nonparametric
++> ### htest regression
++>
++> ### ** Examples
++>
++> ### Example 1: Unconstrained ordination
++> ## NMDS
++> data(varespec)
++> data(varechem)
++> ord <- metaMDS(varespec)
++Square root transformation
++Wisconsin double standardization
++Run 0 stress 0.1843196
++Run 1 stress 0.2455912
++Run 2 stress 0.2169407
++Run 3 stress 0.2313231
++Run 4 stress 0.1974406
++Run 5 stress 0.1858402
++Run 6 stress 0.1948414
++Run 7 stress 0.2265717
++Run 8 stress 0.222507
++Run 9 stress 0.2023215
++Run 10 stress 0.2673177
++Run 11 stress 0.1976151
++Run 12 stress 0.1852397
++Run 13 stress 0.2341085
++Run 14 stress 0.1955867
++Run 15 stress 0.2137409
++Run 16 stress 0.2109638
++Run 17 stress 0.1825658
++... New best solution
++... Procrustes: rmse 0.04169825 max resid 0.1521436
++Run 18 stress 0.1843197
++Run 19 stress 0.2570119
++Run 20 stress 0.3760596
++*** No convergence -- monoMDS stopping criteria:
++ 20: stress ratio > sratmax
++> plot(ord, type = "t")
++> ## Fit environmental variables
++> ef <- envfit(ord, varechem)
++> ef
++
++***VECTORS
++
++ NMDS1 NMDS2 r2 Pr(>r)
++N -0.05719 -0.99836 0.2537 0.039 *
++P 0.61959 0.78492 0.1938 0.129
++K 0.76629 0.64249 0.1809 0.138
++Ca 0.68506 0.72849 0.4119 0.005 **
++Mg 0.63240 0.77464 0.4271 0.005 **
++S 0.19123 0.98155 0.1752 0.133
++Al -0.87169 0.49006 0.5269 0.001 ***
++Fe -0.93613 0.35164 0.4451 0.001 ***
++Mn 0.79873 -0.60169 0.5230 0.002 **
++Zn 0.61750 0.78657 0.1879 0.122
++Mo -0.90304 0.42955 0.0609 0.524
++Baresoil 0.92503 -0.37988 0.2508 0.043 *
++Humdepth 0.93291 -0.36011 0.5200 0.001 ***
++pH -0.64809 0.76156 0.2308 0.064 .
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++Permutation: free
++Number of permutations: 999
++
++
++> plot(ef, p.max = 0.05)
++> ### Example 2: Constrained ordination (RDA)
++> ## The example uses formula interface to define the model
++> data(dune)
++> data(dune.env)
++> ## No constraints: PCA
++> mod0 <- rda(dune ~ 1, dune.env)
++> mod0
++Call: rda(formula = dune ~ 1, data = dune.env)
++
++ Inertia Rank
++Total 84.12
++Unconstrained 84.12 19
++Inertia is variance
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
++24.795 18.147 7.629 7.153 5.695 4.333 3.199 2.782
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++> plot(mod0)
++> ## All environmental variables: Full model
++> mod1 <- rda(dune ~ ., dune.env)
++> mod1
++Call: rda(formula = dune ~ A1 + Moisture + Management + Use + Manure,
++data = dune.env)
++
++ Inertia Proportion Rank
++Total 84.1237 1.0000
++Constrained 63.2062 0.7513 12
++Unconstrained 20.9175 0.2487 7
++Inertia is variance
++Some constraints were aliased because they were collinear (redundant)
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10 RDA11
++22.396 16.208 7.039 4.038 3.760 2.609 2.167 1.803 1.404 0.917 0.582
++ RDA12
++ 0.284
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7
++6.627 4.309 3.549 2.546 2.340 0.934 0.612
++
++> plot(mod1)
++> ## Automatic selection of variables by permutation P-values
++> mod <- ordistep(mod0, scope=formula(mod1))
++
++Start: dune ~ 1
++
++ Df AIC F Pr(>F)
+++ Management 3 87.082 2.8400 0.005 **
+++ Moisture 3 87.707 2.5883 0.005 **
+++ Manure 4 89.232 1.9539 0.015 *
+++ A1 1 89.591 1.9217 0.045 *
+++ Use 2 91.032 1.1741 0.295
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: dune ~ Management
++
++ Df AIC F Pr(>F)
++- Management 3 89.62 2.84 0.005 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++ Df AIC F Pr(>F)
+++ Moisture 3 85.567 1.9764 0.010 **
+++ Manure 3 87.517 1.3902 0.045 *
+++ A1 1 87.424 1.2965 0.245
+++ Use 2 88.284 1.0510 0.400
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++Step: dune ~ Management + Moisture
++
++ Df AIC F Pr(>F)
++- Moisture 3 87.082 1.9764 0.015 *
++- Management 3 87.707 2.1769 0.005 **
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++
++ Df AIC F Pr(>F)
+++ Manure 3 85.762 1.1225 0.255
+++ A1 1 86.220 0.8359 0.625
+++ Use 2 86.842 0.8027 0.780
++
++> mod
++Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
++
++ Inertia Proportion Rank
++Total 84.1237 1.0000
++Constrained 46.4249 0.5519 6
++Unconstrained 37.6988 0.4481 13
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
++21.588 14.075 4.123 3.163 2.369 1.107
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
++8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
++
++> plot(mod)
++> ## Permutation test for all variables
++> anova(mod)
++Permutation test for rda under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
++ Df Variance F Pr(>F)
++Model 6 46.425 2.6682 0.001 ***
++Residual 13 37.699
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Permutation test of "type III" effects, or significance when a term
++> ## is added to the model after all other terms
++> anova(mod, by = "margin")
++Permutation test for rda under reduced model
++Marginal effects of terms
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
++ Df Variance F Pr(>F)
++Management 3 18.938 2.1769 0.003 **
++Moisture 3 17.194 1.9764 0.005 **
++Residual 13 37.699
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> ## Plot only sample plots, use different symbols and draw SD ellipses
++> ## for Managemenet classes
++> plot(mod, display = "sites", type = "n")
++> with(dune.env, points(mod, disp = "si", pch = as.numeric(Management)))
++> with(dune.env, legend("topleft", levels(Management), pch = 1:4,
+++ title = "Management"))
++> with(dune.env, ordiellipse(mod, Management, label = TRUE))
++> ## add fitted surface of diversity to the model
++> ordisurf(mod, diversity(dune), add = TRUE)
++
++Family: gaussian
++Link function: identity
++
++Formula:
++y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
++
++Estimated degrees of freedom:
++1.28 total = 2.28
++
++REML score: 3.00623
++> ### Example 3: analysis of dissimilarites a.k.a. non-parametric
++> ### permutational anova
++> adonis(dune ~ ., dune.env)
++
++Call:
++adonis(formula = dune ~ ., data = dune.env)
++
++Permutation: free
++Number of permutations: 999
++
++Terms added sequentially (first to last)
++
++ Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
++A1 1 0.7230 0.72295 5.2038 0.16817 0.001 ***
++Moisture 3 1.1871 0.39569 2.8482 0.27613 0.006 **
++Management 3 0.9036 0.30121 2.1681 0.21019 0.023 *
++Use 2 0.0921 0.04606 0.3315 0.02143 0.982
++Manure 3 0.4208 0.14026 1.0096 0.09787 0.481
++Residuals 7 0.9725 0.13893 0.22621
++Total 19 4.2990 1.00000
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++> adonis(dune ~ Management + Moisture, dune.env)
++
++Call:
++adonis(formula = dune ~ Management + Moisture, data = dune.env)
++
++Permutation: free
++Number of permutations: 999
++
++Terms added sequentially (first to last)
++
++ Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
++Management 3 1.4686 0.48953 3.7907 0.34161 0.001 ***
++Moisture 3 1.1516 0.38387 2.9726 0.26788 0.002 **
++Residuals 13 1.6788 0.12914 0.39051
++Total 19 4.2990 1.00000
++---
++Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
++>
++>
++>
++> cleanEx()
++> nameEx("vegdist")
++> ### * vegdist
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Encoding: UTF-8
++>
++> ### Name: vegdist
++> ### Title: Dissimilarity Indices for Community Ecologists
++> ### Aliases: vegdist
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> data(varespec)
++> vare.dist <- vegdist(varespec)
++> # Orlóci's Chord distance: range 0 .. sqrt(2)
++> vare.dist <- vegdist(decostand(varespec, "norm"), "euclidean")
++> # Anderson et al. (2006) version of Gower
++> vare.dist <- vegdist(decostand(varespec, "log"), "altGower")
++Warning: non-integer data: divided by smallest positive value
++> # Range standardization with "altGower" (that excludes double-zeros)
++> vare.dist <- vegdist(decostand(varespec, "range"), "altGower")
++>
++>
++>
++> cleanEx()
++> nameEx("vegemite")
++> ### * vegemite
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: vegemite
++> ### Title: Display Compact Ordered Community Tables
++> ### Aliases: vegemite tabasco coverscale
++> ### Keywords: print manip hplot
++>
++> ### ** Examples
++>
++> data(varespec)
++> ## Print only more common species
++> freq <- apply(varespec > 0, 2, sum)
++> vegemite(varespec, scale="Hult", sp.ind = freq > 10)
++
++ 1122212121122 1112
++ 854739268340575634292011
++ Callvulg 111...11.311...1111.111.
++ Empenigr 211332121112211111212213
++ Vaccviti 323332212113221211233234
++ Pinusylv 111.111111111.11.1111111
++ Dicrfusc 12.111441121211111111111
++ Dicrpoly .11.11.11..1.11....1.111
++ Pleuschr 144533435123411111111131
++ Polyjuni 111.11.111112111.111.111
++ Pohlnuta 111111111111.1..11.11111
++ Ptilcili 1111111111..1..11.11..12
++ Cladarbu 321122121332143423111121
++ Cladrang 321221131312145443313241
++ Cladstel 11111311.211.11254555542
++ Cladunci 112111111131111111111111
++ Cladcocc 11..11111111111111.1.11.
++ Cladcorn 111111111111111111111111
++ Cladgrac 111111111111111111111.11
++ Cladfimb 11.1111111111111111111.1
++ Cladcris 111111111111111111111111
++ Cladchlo ..1.111..1...1.11.11.1.1
++ Cetreric 111...11.111111111.1.1..
++ Cetrisla .11....1111.1....1.11111
++ Stersp 111.11.1111.112111...11.
++ Claddefo 1111111111111111111111.1
++ sites species
++ 24 24
++scale: Hult
++> ## Order by correspondence analysis, use Hill scaling and layout:
++> dca <- decorana(varespec)
++> vegemite(varespec, dca, "Hill", zero="-")
++
++ 1 1 1 11122211122222
++ 203942561738913046572458
++ Flavniva -1114-11-1-11-1---------
++ Cladstel 5555551451425411111211--
++ Cladphyl -1-1----1-------1-------
++ Cladcerv 1---1-----------------1-
++ Cladsp ---11--1--11---1-1-11-1-
++ Cladamau --1---1----1------------
++ Cladchlo 1111---1-11-111-----11--
++ Cladrang 535254555555223414332321
++ Diphcomp --11-----112----------1-
++ Stersp -11-1-4111111-1-111--111
++ Pinusylv 11-111111-111111111-1111
++ Polypili ------111--11-11--1-----
++ Cetrisla -1-111--1-1--1--111--111
++ Cladcocc -1111-1111111-11111-1-11
++ Cladarbu 113142453555313343413231
++ Vacculig --1-1----3-1---1---12-11
++ Pohlnuta -11111--11111111111111-1
++ Cladfimb 11111111-111111111111-11
++ Callvulg 111-21-12-51---1221-21--
++ Icmaeric ------1--1------11------
++ Empenigr 342214131314344333143131
++ Vaccviti 342514244334455432444443
++ Cladgrac 1-1111111111111111111111
++ Cetreric -1111-11-111---1111-111-
++ Cladcorn 111111111111111111111111
++ Cladcris 111111111111111111111111
++ Peltapht --------1-11--1----1--1-
++ Ptilcili 1-11---11-11141--1111111
++ Barbhatc ----------1-121----1----
++ Claddefo 11111111-111111111111111
++ Cladbotr ----------1-1111---1-1-1
++ Betupube -------------1------1-1-
++ Dicrpoly -1-1--1-11--1211-11--1-1
++ Cladunci 111111122111111251212311
++ Polycomm ------------11--1--1--1-
++ Polyjuni 11-11-1111111121111--131
++ Rhodtome ----------1--2-----21--1
++ Dicrfusc 111111111121112145425-41
++ Pleuschr 111213114132524434555555
++ Vaccmyrt ---1------1-24--12133--4
++ Descflex ----1----1--11-----21-11
++ Nepharct --1------1-1---1------2-
++ Dicrsp -----1----1--1-1-11-1541
++ Hylosple ---------------1---3--13
++ sites species
++ 24 44
++scale: Hill
++> ## Show one class from cluster analysis, but retain the ordering above
++> clus <- hclust(vegdist(varespec))
++> cl <- cutree(clus, 3)
++> sel <- vegemite(varespec, use=dca, select = cl == 3, scale="Br")
++
++ 1 12
++ 20921
++ Flavniva .++..
++ Cladstel 55542
++ Cladphyl .++..
++ Cladcerv r....
++ Cladsp ..+..
++ Cladchlo r++.+
++ Cladrang 22121
++ Diphcomp ..+..
++ Stersp .+...
++ Pinusylv r++1+
++ Cetrisla .++++
++ Cladcocc .++..
++ Cladarbu +1+1+
++ Pohlnuta .++++
++ Cladfimb r++++
++ Callvulg r+.+.
++ Empenigr 22122
++ Vaccviti 22223
++ Cladgrac r.+++
++ Cetreric .++..
++ Cladcorn r+++r
++ Cladcris r++++
++ Ptilcili r.+.2
++ Barbhatc ....1
++ Claddefo r++++
++ Cladbotr ....+
++ Betupube ....+
++ Dicrpoly .++.1
++ Cladunci +++1+
++ Polycomm ....+
++ Polyjuni r++.+
++ Rhodtome ....1
++ Dicrfusc r++++
++ Pleuschr ++121
++ Vaccmyrt ..+.2
++ Descflex ....+
++ Dicrsp ...++
++ sites species
++ 5 37
++scale: Braun.Blanquet
++> ## Re-create previous
++> vegemite(varespec, sp=sel$sp, site=sel$site, scale="Hult")
++
++ 1 12
++ 20921
++ Flavniva .11..
++ Cladstel 55552
++ Cladphyl .11..
++ Cladcerv 1....
++ Cladsp ..1..
++ Cladchlo 111.1
++ Cladrang 32131
++ Diphcomp ..1..
++ Stersp .1...
++ Pinusylv 11111
++ Cetrisla .1111
++ Cladcocc .11..
++ Cladarbu 11111
++ Pohlnuta .1111
++ Cladfimb 11111
++ Callvulg 11.1.
++ Empenigr 22123
++ Vaccviti 22334
++ Cladgrac 1.111
++ Cetreric .11..
++ Cladcorn 11111
++ Cladcris 11111
++ Ptilcili 1.1.2
++ Barbhatc ....1
++ Claddefo 11111
++ Cladbotr ....1
++ Betupube ....1
++ Dicrpoly .11.1
++ Cladunci 11111
++ Polycomm ....1
++ Polyjuni 111.1
++ Rhodtome ....1
++ Dicrfusc 11111
++ Pleuschr 11111
++ Vaccmyrt ..1.3
++ Descflex ....1
++ Dicrsp ...11
++ sites species
++ 5 37
++scale: Hult
++> ## Re-order clusters by ordination
++> clus <- as.dendrogram(clus)
++> clus <- reorder(clus, scores(dca, choices=1, display="sites"), agglo.FUN =
mean)
++> vegemite(varespec, clus, scale = "Hult")
++
++ 1 111 1211221212222
++ 431567380922149306254578
++ Flavniva 21.111.111....11........
++ Cladamau .1.1...1................
++ Stersp 111211111....111.1.111.1
++ Polypili ..111..1......111..1....
++ Diphcomp .1...111.1...........1..
++ Cladphyl ..1.....11...1..........
++ Cladrang 344544332133111223121121
++ Cladcerv 1.........1..........1..
++ Cladstel 454121215555213111111.1.
++ Cladarbu 322344331111132222121111
++ Vacculig 11...2.1........1.1..111
++ Callvulg 111.1.311.11.1..11111...
++ Icmaeric ...1.1.......1...1......
++ Cladsp 1...1.11.1......111..11.
++ Cladcocc 1111111111...1111111.1.1
++ Pinusylv 1.111.1111111111111111.1
++ Cladchlo .1..111.111.1.11..1.1...
++ Cetrisla 1.1...1.11.111...1.111.1
++ Cladfimb 11.11111111111111111.111
++ Peltapht ..1...11.......1.....11.
++ Cetreric 11.1111111...1..111111..
++ Cladgrac 11111111.111111111111111
++ Pohlnuta 111..11111.1111111111.11
++ Ptilcili .11.1.11.11.2.11.1111111
++ Barbhatc ......1.....1.11......1.
++ Cladcorn 111111111111111111111111
++ Vaccviti 113122132323412331223232
++ Cladcris 111111111111111111111111
++ Empenigr 111111122122312322111231
++ Cladbotr ......1.....1.111...1.11
++ Betupube ............1.....1..1..
++ Cladunci 111111111111131111112111
++ Claddefo 11.111111111111111111111
++ Dicrpoly ..11.1..11..1.1111.11..1
++ Polycomm ............111......11.
++ Rhodtome ......1.....1.....1...11
++ Polyjuni 1.111111111.111111.112.1
++ Dicrfusc 11111111111112111442.211
++ Pleuschr 113111111111123333444455
++ Vaccmyrt ......1..1..311..111..13
++ Nepharct .1...1.1........1....1..
++ Dicrsp ......1....11...111133.1
++ Descflex 1....1......1.1...1..111
++ Hylosple ................1....122
++ sites species
++ 24 44
++scale: Hult
++>
++> ## Abundance values have such a wide range that they must be rescaled
++> ## or all abundances will not be shown in tabasco
++> tabasco(decostand(varespec, "log"), dca)
++Warning: non-integer data: divided by smallest positive value
++>
++> ## Classification trees for species
++> data(dune, dune.taxon)
++> taxontree <- hclust(taxa2dist(dune.taxon))
++> plotree <- hclust(vegdist(dune), "average")
++> ## Automatic reordering of clusters
++> tabasco(dune, plotree, sp.ind = taxontree)
++> ## No reordering of taxonomy
++> tabasco(dune, plotree, sp.ind = taxontree, Colv = FALSE)
++> ## Species cluster: most dissimilarity indices do a bad job when
++> ## comparing rare and common species, but Raup-Crick makes sense
++> sptree <- hclust(vegdist(t(dune), "raup"), "average")
++> tabasco(dune, plotree, sptree)
++>
++>
++>
++> cleanEx()
++> nameEx("wascores")
++> ### * wascores
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: wascores
++> ### Title: Weighted Averages Scores for Species
++> ### Aliases: wascores eigengrad
++> ### Keywords: multivariate univar
++>
++> ### ** Examples
++>
++> data(varespec)
++> data(varechem)
++> vare.dist <- vegdist(wisconsin(varespec))
++> vare.mds <- monoMDS(vare.dist)
++> vare.points <- postMDS(vare.mds$points, vare.dist)
++> vare.wa <- wascores(vare.points, varespec)
++> plot(scores(vare.points), pch="+", asp=1)
++> text(vare.wa, rownames(vare.wa), cex=0.8, col="blue")
++> ## Omit rare species (frequency <= 4)
++> freq <- apply(varespec>0, 2, sum)
++> plot(scores(vare.points), pch="+", asp=1)
++> text(vare.wa[freq > 4,], rownames(vare.wa)[freq > 4],cex=0.8,col="blue")
++> ## Works for environmental variables, too.
++> wascores(varechem, varespec)
++ N P K Ca Mg S Al
++Callvulg 25.12401 41.66188 246.92383 572.3431 99.40828 49.10552 245.15751
++Empenigr 21.61371 44.17350 158.92517 580.8403 89.20628 35.75327 107.05670
++Rhodtome 22.34553 40.35530 162.31108 643.8819 95.07712 30.84303 28.83159
++Vaccmyrt 24.96352 49.80649 189.70177 656.4179 96.75529 34.55190 32.75705
++Vaccviti 21.14028 45.86984 162.12372 613.2126 94.30084 37.26975 116.14656
++Pinusylv 18.37299 44.24818 163.60195 670.9387 93.52214 37.02238 150.45523
++Descflex 22.24089 54.74804 212.20357 771.0159 114.15179 38.00000 24.64143
++Betupube 21.51034 28.86552 112.28276 513.5586 75.15172 23.55172 33.12414
++Vacculig 28.00729 33.48758 114.90230 372.9653 70.89954 29.32378 202.89008
++Diphcomp 22.34228 39.71049 127.44259 446.1565 80.16173 32.32963 122.31574
++Dicrsp 21.33007 60.03758 185.04563 828.0544 148.67509 46.75427 90.42294
++Dicrfusc 23.45681 39.14575 162.91954 578.2309 77.81648 33.48086 60.66890
++Dicrpoly 20.65446 43.87409 150.51485 665.5845 115.22112 36.17079 90.16733
++Hylosple 26.10599 67.88980 245.78681 779.6520 111.96685 42.27433 24.92738
++Pleuschr 22.60476 54.22534 199.96241 712.6278 109.23425 40.01132 70.43900
++Polypili 23.17377 43.75902 144.82623 724.9738 85.42623 30.58525 145.73115
++Polyjuni 22.89480 47.98022 154.81906 643.3864 87.27819 33.63863 53.24888
++Polycomm 21.73521 41.17042 154.91549 631.8704 101.84789 32.17324 46.80986
++Pohlnuta 19.99885 48.59198 169.68855 678.3813 104.31641 39.94427 132.05458
++Ptilcili 21.27880 33.44211 127.08522 564.5652 85.96417 27.11720 56.60692
++Barbhatc 21.17461 27.93323 113.13542 497.9138 77.50564 23.72288 42.09749
++Cladarbu 23.56127 38.04952 142.03073 454.9019 74.00779 33.81002 173.12698
++Cladrang 24.28421 38.60534 135.31177 463.2750 70.54209 32.53349 183.79979
++Cladstel 19.28049 46.71060 158.00576 540.4904 80.19153 40.29106 225.89526
++Cladunci 21.41240 45.49844 163.40402 621.9100 98.35538 40.00734 119.59481
++Cladcocc 21.72473 42.80681 156.32330 557.9007 80.95448 36.25161 149.82616
++Cladcorn 22.11640 47.06656 160.36881 623.5185 95.17781 36.75273 104.71463
++Cladgrac 22.51887 44.06576 156.50214 583.1558 94.10292 36.93930 134.13424
++Cladfimb 21.77980 41.82652 153.29444 512.4646 78.28232 35.62323 128.96061
++Cladcris 20.88795 44.12262 171.04016 574.5672 92.52169 38.24003 116.03507
++Cladchlo 19.51207 45.39655 150.93190 571.0233 95.77586 39.50862 156.81983
++Cladbotr 22.97660 38.89574 167.20000 590.8021 99.57234 34.79362 87.75957
++Cladamau 25.07143 35.84286 105.07857 395.2214 68.18571 27.11429 95.91429
++Cladsp 19.21923 47.37308 168.49231 526.7654 79.54423 45.15385 215.33846
++Cetreric 21.00944 47.76972 165.07972 579.6322 99.14944 42.25472 163.46000
++Cetrisla 18.36552 42.73695 151.78374 626.2813 89.77833 35.33498 132.68227
++Flavniva 18.56110 61.18194 207.67705 502.9203 60.91755 50.22532 396.82405
++Nepharct 23.33099 49.10019 146.84715 618.1601 64.27319 29.95760 31.72300
++Stersp 28.19743 32.84800 94.33459 389.5143 53.25377 24.22175 95.39326
++Peltapht 21.08553 54.45395 193.38816 886.5487 119.35132 37.92500 106.16447
++Icmaeric 28.88636 27.00000 87.86818 307.0500 40.48182 22.17273 89.94091
++Cladcerv 20.25000 56.79000 192.36000 519.2300 62.10000 45.18000 314.92000
++Claddefo 22.19198 45.22981 167.73069 583.7983 92.01320 38.51369 100.46139
++Cladphyl 15.73750 54.56875 180.39375 775.4500 99.65625 43.35000 208.55000
++ Fe Mn Zn Mo Baresoil Humdepth pH
++Callvulg 75.457843 52.38247 8.281074 0.4734635 27.241036 2.187819 2.845108
++Empenigr 38.146102 53.49357 7.159938 0.3289657 27.324317 2.367439 2.888078
++Rhodtome 5.560906 70.48260 7.444100 0.2251490 37.325030 2.689154 2.895352
++Vaccmyrt 5.589213 75.17221 7.838533 0.2666732 31.404171 2.798935 2.855216
++Vaccviti 37.586067 51.81515 7.617213 0.3680289 26.307701 2.307879 2.923128
++Pinusylv 39.121898 35.22311 7.733333 0.3485401 17.762968 1.996350 3.049148
++Descflex 6.066429 110.87232 9.526607 0.2316071 22.740179 2.834821 2.822857
++Betupube 5.417241 37.53448 5.637931 0.2068966 51.496552 2.527586 2.979310
++Vacculig 93.963929 37.73062 4.593824 0.3780552 21.410710 2.041196 3.006965
++Diphcomp 73.281173 46.88025 4.593827 0.3725309 31.836574 2.103704 2.856790
++Dicrsp 22.504617 65.35020 13.060765 0.5610370 23.182889 2.232247 2.954272
++Dicrfusc 13.922252 61.40958 6.922859 0.3218816 26.918674 2.484399 2.806431
++Dicrpoly 20.973927 33.28779 9.110561 0.3892739 37.304043 2.228713 3.015842
++Hylosple 4.729157 115.14606 9.885976 0.2851996 20.956264 2.925000 2.807594
++Pleuschr 19.113811 77.12277 9.007860 0.3405945 24.584979 2.596881 2.858446
++Polypili 51.993443 36.74754 8.045902 0.2204918 17.368852 1.493443 3.227869
++Polyjuni 12.885704 82.56274 7.945126 0.2760289 28.116303 2.615523 2.874729
++Polycomm 7.895775 68.22535 7.843662 0.2591549 38.687324 2.926761 2.860563
++Pohlnuta 33.089313 42.02290 8.452290 0.3935115 24.709351 2.147328 2.985496
++Ptilcili 14.036188 33.93547 5.906924 0.2303712 48.941884 2.502498 2.973376
++Barbhatc 8.199687 31.11379 5.550784 0.2084639 54.331975 2.514734 2.986834
++Cladarbu 65.470394 38.28429 6.387500 0.4464046 22.592997 2.048540 2.937879
++Cladrang 76.612752 34.86010 6.616452 0.3903501 17.270158 1.799128 3.022346
++Cladstel 84.639467 33.27903 7.287216 0.4054879 9.854042 1.851973 3.052167
++Cladunci 27.463504 40.10322 9.108102 0.5120114 28.312376 2.362687 2.858564
++Cladcocc 46.653763 43.53584 7.269176 0.3698925 19.972222 2.025090 2.974194
++Cladcorn 32.916238 53.38441 7.518489 0.3639871 26.620868 2.399518 2.890997
++Cladgrac 45.758366 44.63911 7.702724 0.4244163 25.546654 2.230350 2.933658
++Cladfimb 41.710354 47.83687 6.815152 0.3887626 24.763889 2.239394 2.902778
++Cladcris 34.729585 44.75984 6.933735 0.3746988 29.711352 2.400000 2.841633
++Cladchlo 42.089655 35.13276 7.908621 0.3814655 22.319655 2.075000 3.022414
++Cladbotr 23.374468 46.54255 7.263830 0.3106383 45.725532 2.580851 2.904255
++Cladamau 68.971429 41.98571 4.928571 0.3214286 27.592857 1.857143 2.914286
++Cladsp 47.913462 49.61923 8.421154 0.6173077 16.864423 2.213462 2.921154
++Cetreric 42.614167 36.96694 9.516389 0.5687500 21.452639 2.058056 2.923889
++Cetrisla 29.617734 33.24138 7.518227 0.3192118 26.417980 2.027586 3.065025
++Flavniva 94.339916 37.03232 9.116371 0.9987764 19.692312 1.799241 2.923629
++Nepharct 12.910837 115.15684 7.743536 0.2180608 23.135932 2.541065 2.918251
++Stersp 30.226998 31.97061 7.635502 0.2828767 15.844007 1.477740 3.038756
++Peltapht 37.598684 56.64079 7.652632 0.2046053 28.321053 2.286842 3.026316
++Icmaeric 24.236364 23.95909 6.618182 0.2863636 18.727273 1.568182 2.968182
++Cladcerv 111.090000 52.04000 8.530000 0.6800000 15.393000 1.870000 2.900000
++Claddefo 25.116325 48.81105 7.599609 0.4096285 33.814545 2.468133 2.823656
++Cladphyl 50.475000 35.28125 8.568750 0.2812500 7.728125 1.575000 3.231250
++> ## And the strengths of these variables are:
++> eigengrad(varechem, varespec)
++ N P K Ca Mg S Al
++0.13000842 0.18880078 0.16246365 0.15722067 0.16359171 0.13391967 0.29817406
++ Fe Mn Zn Mo Baresoil Humdepth pH
++0.20766831 0.27254480 0.16783834 0.09542514 0.20931501 0.25051326 0.14583161
++>
++>
++>
++> cleanEx()
++> nameEx("wcmdscale")
++> ### * wcmdscale
++>
++> flush(stderr()); flush(stdout())
++>
++> ### Name: wcmdscale
++> ### Title: Weighted Classical (Metric) Multidimensional Scaling
++> ### Aliases: wcmdscale scores.wcmdscale plot.wcmdscale
++> ### Keywords: multivariate
++>
++> ### ** Examples
++>
++> ## Correspondence analysis as a weighted principal coordinates
++> ## analysis of Euclidean distances of Chi-square transformed data
++> data(dune)
++> rs <- rowSums(dune)/sum(dune)
++> d <- dist(decostand(dune, "chi"))
++> ord <- wcmdscale(d, w = rs, eig = TRUE)
++> ## Ordinary CA
++> ca <- cca(dune)
++> ## Eigevalues are numerically similar
++> ca$CA$eig - ord$eig
++ CA1 CA2 CA3 CA4 CA5
++ 2.220446e-16 -1.276756e-15 -1.054712e-15 1.942890e-16 1.387779e-16
++ CA6 CA7 CA8 CA9 CA10
++ 1.387779e-16 -1.387779e-17 -1.387779e-16 2.359224e-16 2.775558e-17
++ CA11 CA12 CA13 CA14 CA15
++ 1.387779e-16 0.000000e+00 3.469447e-17 2.775558e-17 1.561251e-17
++ CA16 CA17 CA18 CA19
++ 0.000000e+00 1.734723e-18 1.127570e-17 1.196959e-16
++> ## Configurations are similar when site scores are scaled by
++> ## eigenvalues in CA
++> procrustes(ord, ca, choices=1:19, scaling = "sites")
++
++Call:
++procrustes(X = ord, Y = ca, choices = 1:19, scaling = "sites")
++
++Procrustes sum of squares:
++-4.263e-14
++
++> plot(procrustes(ord, ca, choices=1:2, scaling="sites"))
++> ## Reconstruction of non-Euclidean distances with negative eigenvalues
++> d <- vegdist(dune)
++> ord <- wcmdscale(d, eig = TRUE)
++> ## Only positive eigenvalues:
++> cor(d, dist(ord$points))
++[1] 0.9975185
++> ## Correction with negative eigenvalues:
++> cor(d, sqrt(dist(ord$points)^2 - dist(ord$negaxes)^2))
++[1] 1
++>
++>
++>
++> ### * <FOOTER>
++> ###
++> options(digits = 7L)
++> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos =
'CheckExEnv'),"\n")
++Time elapsed: 129.459 0.488 130.001 0 0
++> grDevices::dev.off()
++null device
++ 1
++> ###
++> ### Local variables: ***
++> ### mode: outline-minor ***
++> ### outline-regexp: "\\(> \\)?### [*]+" ***
++> ### End: ***
++> quit('no')
+diff --git a/tests/oecosimu-tests.R b/tests/oecosimu-tests.R
+new file mode 100644
+index 0000000..0e8a82b
+--- /dev/null
++++ b/tests/oecosimu-tests.R
+@@ -0,0 +1,166 @@
++### oecosimu-tests: unit tests for vegan functions
++
++### This file contains unit tests for oecosimu, permatfull and related
++### functions. This file is run in R CMD check and its results are
++### compared against previously saved results in
++### oecosimu-tests.Rout.save. If you change tests, you must generate
++### new oecosimu-tests.Rout.save in this directory.
++
++### The current plan is that tests/ are not included in the CRAN
++### release, but only in the development version of vegan in R-Forge.
++
++### The tests here are not intended for human reading. The tests need
++### not be ecological or biologically meaningful, but they are only
++### intended for testing strange arguments, protect against
++### regressions and test correctness of results.
++
++### The tests are in a single file and you should clean work space
++### after the unit test. You should set random number seed (if needed)
++### for comparison against vegan-tests.Rout.save, and you should
++### remove the seed after the unit test. If possible, avoid very long
++### lasting tests.
++
++### <-- oecosimu/permat specifics -->
++
++### The necessary condition is that fill, row and column sums are honoured.
Secondarily we want to keep the simulation results identical when chaning the
underlying routines in oecosimu (except experimental method = "backtrack"). The
permatfull/swap code can be regarded as more experimental, and we can perhaps
there change the simulation results, but not lightly.
++
++###<--- BEGIN TESTS --->
++suppressPackageStartupMessages(require(vegan))
++set.seed(4711)
++
++### Prior to vegan 2.2-0 these were commsimulator() tests for binary
++### null model, but now commsimulator() is deprecated and calls were
++### replaced with corresponding simulate(nullmodel())
++
++## expect fill, rowSums, colSums
++expect <- data.frame("r00" = c(TRUE, FALSE, FALSE),
++ "r0" = c(TRUE, TRUE, FALSE),
++ "r1" = c(TRUE, TRUE, FALSE),
++ "r2" = c(TRUE, TRUE, FALSE),
++ "c0" = c(TRUE, FALSE, TRUE),
++ "swap" = c(TRUE, TRUE, TRUE),
++ "tswap" = c(TRUE, TRUE, TRUE),
++ "quasiswap" = c(TRUE, TRUE, TRUE))
++margintest <- function(x, fill, rs, cs) {
++ c(sum(x) == fill,
++ all(rowSums(x) == rs),
++ all(colSums(x) == cs))
++}
++
++## data must be binary! 'sipoo' is (and not transformed)
++data(sipoo)
++fill <- sum(sipoo)
++rs <- rowSums(sipoo)
++cs <- colSums(sipoo)
++
++for(method in names(expect)) {
++ m <- simulate(nullmodel(sipoo, method = method), thin = 100)
++ cat("--> method:", method, "\n")
++ cat("** margintest:")
++ print(all(margintest(m, fill, rs, cs) == expect[, method]))
++ vegemite(drop(m))
++}
++## clean
++rm(list = ls())
++
++### end commsimulator
++
++### set up permatfull/swap tests
++
++data(mite)
++data(mite.env)
++x <- as.matrix(subset(mite, mite.env$Topo == "Blanket"))
++x <- x[, colSums(x) > 0]
++gsum <- sum(x)
++rsum <- rowSums(x)
++csum <- colSums(x)
++fill <- sum(x>0)
++rfrq <- rowSums(x>0)
++cfrq <- colSums(x>0)
++
++margintest <-
++ function(x, gsum, rsum, csum, fill, rfrq, cfrq)
++{
++ cat("grand sum: ")
++ print(all.equal(sum(x), gsum, check.attributes = FALSE))
++ cat("row sums: ")
++ print(all.equal(rowSums(x), rsum, check.attributes = FALSE))
++ cat("col sums: ")
++ print(all.equal(colSums(x), csum, check.attributes = FALSE))
++ cat("fill: ")
++ print(all.equal(sum(x>0), fill, check.attributes = FALSE))
++ cat("row freqs: ")
++ print(all.equal(rowSums(x>0), rfrq, check.attributes = FALSE))
++ cat("col freqs: ")
++ print(all.equal(colSums(x>0), cfrq, check.attributes = FALSE))
++}
++
++### permatfull
++
++set.seed(4711)
++margin <- c("none", "rows", "columns", "both")
++shuffle <- c("ind", "samp", "both")
++
++for(mar in margin) {
++ for(what in shuffle) {
++ cat("\n--> margin", mar, " shuffle", what, "<--\n")
++ set.seed(4711)
++ m <- permatfull(x, fixedmar = mar, shuffle = what, mtype = "count",
++ times=1)$perm[[1]]
++ margintest(m, gsum, rsum, csum, fill, rfrq, cfrq)
++ print(m[,1:12])
++ }
++}
++
++### permatswap
++set.seed(4711)
++methods <- c("swap", "quasiswap", "swsh", "abuswap")
++margins <- c("rows", "columns", "both")
++shuffle <- c("samp", "both")
++
++incompatible <- function(method, margin)
++{
++ (method == "swap" && margin != "both") ||
++ (method == "abuswap" && margin == "both") ||
++ (method == "quasiswap" && margin != "both") ||
++ (method == "swsh" && margin == "both")
++}
++
++for(method in methods) {
++ for(margin in margins) {
++ for(what in shuffle) {
++ if (incompatible(method = method, margin = margin))
++ next
++ cat("\n*** ", method, " ***\n")
++ cat("--> margin", margin, " shuffle", what, "<--\n")
++ set.seed(4711)
++ m <- permatswap(x, method = method, fixedmar = margin, shuffle =
what,
++ mtype = "count", thin=100, times=1)$perm[[1]]
++ margintest(m, gsum, rsum, csum,fill, rfrq, cfrq)
++ print(m[,1:12])
++ }
++ }
++}
++### end permatswap
++
++nm=nullmodel(m,"abuswap_c")
++sm<-simulate(nm,nsim=100,thin=100,burnin=100)
++
++### clean
++rm(list = ls())
++
++## end permatfull/swap
++
++## The following vegan functions depend on *oecosimu*: adipart
++## hiersimu multipart raupcrick. The following functions directly
++## depend on *commsimulator*: permatfull1 permatswap1. All these have
++## derived and/or method functions. These should not be broken.
++
++## Do not break raupcrick:
++set.seed(4711)
++data(sipoo)
++as.numeric(raupcrick(sipoo, nsimul = 99))
++rm(list = ls())
++## end raupcrick
++
++
+diff --git a/tests/oecosimu-tests.Rout.save b/tests/oecosimu-tests.Rout.save
+new file mode 100644
+index 0000000..1827843
+--- /dev/null
++++ b/tests/oecosimu-tests.Rout.save
+@@ -0,0 +1,1982 @@
++
++R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
++Copyright (C) 2015 The R Foundation for Statistical Computing
++Platform: x86_64-apple-darwin13.4.0 (64-bit)
++
++R is free software and comes with ABSOLUTELY NO WARRANTY.
++You are welcome to redistribute it under certain conditions.
++Type 'license()' or 'licence()' for distribution details.
++
++R is a collaborative project with many contributors.
++Type 'contributors()' for more information and
++'citation()' on how to cite R or R packages in publications.
++
++Type 'demo()' for some demos, 'help()' for on-line help, or
++'help.start()' for an HTML browser interface to help.
++Type 'q()' to quit R.
++
++> ### oecosimu-tests: unit tests for vegan functions
++>
++> ### This file contains unit tests for oecosimu, permatfull and related
++> ### functions. This file is run in R CMD check and its results are
++> ### compared against previously saved results in
++> ### oecosimu-tests.Rout.save. If you change tests, you must generate
++> ### new oecosimu-tests.Rout.save in this directory.
++>
++> ### The current plan is that tests/ are not included in the CRAN
++> ### release, but only in the development version of vegan in R-Forge.
++>
++> ### The tests here are not intended for human reading. The tests need
++> ### not be ecological or biologically meaningful, but they are only
++> ### intended for testing strange arguments, protect against
++> ### regressions and test correctness of results.
++>
++> ### The tests are in a single file and you should clean work space
++> ### after the unit test. You should set random number seed (if needed)
++> ### for comparison against vegan-tests.Rout.save, and you should
++> ### remove the seed after the unit test. If possible, avoid very long
++> ### lasting tests.
++>
++> ### <-- oecosimu/permat specifics -->
++>
++> ### The necessary condition is that fill, row and column sums are honoured.
Secondarily we want to keep the simulation results identical when chaning the
underlying routines in oecosimu (except experimental method = "backtrack"). The
permatfull/swap code can be regarded as more experimental, and we can perhaps
there change the simulation results, but not lightly.
++>
++> ###<--- BEGIN TESTS --->
++> suppressPackageStartupMessages(require(vegan))
++> set.seed(4711)
++>
++> ### Prior to vegan 2.2-0 these were commsimulator() tests for binary
++> ### null model, but now commsimulator() is deprecated and calls were
++> ### replaced with corresponding simulate(nullmodel())
++>
++> ## expect fill, rowSums, colSums
++> expect <- data.frame("r00" = c(TRUE, FALSE, FALSE),
+++ "r0" = c(TRUE, TRUE, FALSE),
+++ "r1" = c(TRUE, TRUE, FALSE),
+++ "r2" = c(TRUE, TRUE, FALSE),
+++ "c0" = c(TRUE, FALSE, TRUE),
+++ "swap" = c(TRUE, TRUE, TRUE),
+++ "tswap" = c(TRUE, TRUE, TRUE),
+++ "quasiswap" = c(TRUE, TRUE, TRUE))
++> margintest <- function(x, fill, rs, cs) {
+++ c(sum(x) == fill,
+++ all(rowSums(x) == rs),
+++ all(colSums(x) == cs))
+++ }
++>
++> ## data must be binary! 'sipoo' is (and not transformed)
++> data(sipoo)
++> fill <- sum(sipoo)
++> rs <- rowSums(sipoo)
++> cs <- colSums(sipoo)
++>
++> for(method in names(expect)) {
+++ m <- simulate(nullmodel(sipoo, method = method), thin = 100)
+++ cat("--> method:", method, "\n")
+++ cat("** margintest:")
+++ print(all(margintest(m, fill, rs, cs) == expect[, method]))
+++ vegemite(drop(m))
+++ }
++--> method: r00
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali 1...11........1..1
++ Falcsubb ..1....1.1..1...1.
++ Tetrtetr 1.....1.......1..1
++ Scolrust ...11.1...1..1.11.
++ Colupalu 1..........1.1...1
++ Cucucano ...1.1.1.......1..
++ Apusapus .........1..1.....
++ Picucanu ..........1.11...1
++ Dryomart 1...1...........11
++ Jynxtorq ..11..1...1....1..
++ Deliurbi .....11.1.........
++ Hirurust .1.......1.11.1.1.
++ Motaalba .......1....11.1..
++ Anthtriv 1..1...111........
++ Trogtrog ..1.1...1.....1..1
++ Prunmodu .....1.1...1..1.11
++ Turdpila .....1.1......1..1
++ Turdphil .....1...1....1...
++ Turdmeru .1....1..1....1...
++ Eritrube 1....1..111.......
++ Oenaoena .11....1..........
++ Phoephoe ....1..1....11....
++ Acroarun ..........1.1....1
++ Acroscir 1...11.....1.....1
++ Hippicte 1....11...1.....1.
++ Sylvatri ..........1....1..
++ Sylvbori ..111111.....11.11
++ Sylvcurr .1.....1..........
++ Sylvcomm .1..........1.....
++ Phylsibi .1..1.....1..1..1.
++ Phyltrocs 1.111.............
++ Phyltrocd ....1.1....1.1...1
++ Reguregu ...1..111.....1..1
++ Muscstri 11.1........1.....
++ Ficehypo ..1.11.11......111
++ Parumajo .1....1.....1.....
++ Parucaer .................1
++ Paruater ...........1......
++ Parumont ..1....111...1.1..
++ Cardchlo ...1......111....1
++ Cardspin .1..11.1..........
++ Pyrrpyrr .........1...1...1
++ Loxicurv .1.......1.1....1.
++ Frincoel 11....1.....11....
++ Sturvulg .........1.11.1...
++ Orioorio ......1.....1.....
++ Corvcora .1...1.....1...1..
++ Garrglan 11................
++ sites species
++ 18 48
++--> method: r0
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali .........1..1..1.1
++ Falcsubb .1................
++ Tetrtetr .......1....1....1
++ Scolrust ............1.1..1
++ Colupalu ..1....11.1.....11
++ Cucucano .....1..1..1.1111.
++ Apusapus .1..1.......1..111
++ Picucanu ...........1..1.11
++ Dryomart .....11......1..11
++ Jynxtorq ............1.1..1
++ Deliurbi .....1...1.1.1...1
++ Hirurust ............1....1
++ Motaalba ..........11.1...1
++ Anthtriv ...1......1...1..1
++ Trogtrog .......1.1..11.111
++ Prunmodu .........11....1..
++ Turdpila .......1.........1
++ Turdphil ............1....1
++ Turdmeru ....1..1....1...11
++ Turdilia ..1.......1..1.11.
++ Oenaoena .....1..1.1.....11
++ Phoephoe .............11..1
++ Acroarun .............1....
++ Acroscir 111.......1..11...
++ Hippicte ...........1....1.
++ Sylvatri .......1..1...1..1
++ Sylvbori .........1.11..111
++ Sylvcurr ......111.....1.11
++ Sylvcomm ..1..............1
++ Phylsibi .......1...1....11
++ Phyltrocs .........11...1111
++ Phyltrocd 11...1......1.1...
++ Reguregu .......1.....1..1.
++ Muscstri ....1......1....11
++ Ficehypo .............11111
++ Parumajo 1.1......1..1...1.
++ Parucaer ...............111
++ Paruater 1...1...1.....111.
++ Parumont ..............1.1.
++ Cardchlo ...1..........1111
++ Cardspin ....1............1
++ Pyrrpyrr ......1..11....111
++ Loxicurv ..........1..1..1.
++ Frincoel .............1..11
++ Sturvulg .....1.1.......111
++ Orioorio ......1...11....1.
++ Corvcoro ........1......111
++ Corvcora ....1.....11.1111.
++ Garrglan ...............111
++ sites species
++ 18 49
++--> method: r1
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Falcsubb .............1....
++ Tetrtetr ....1.........1.1.
++ Scolrust .............1.1.1
++ Colupalu ................11
++ Apusapus ..........1.1...11
++ Picucanu ................1.
++ Dryomart ................11
++ Deliurbi .............11.11
++ Hirurust .................1
++ Motaalba .....111....1.1111
++ Anthtriv ..1.............11
++ Trogtrog ............1....1
++ Prunmodu .......1...1....11
++ Turdpila ...........1....1.
++ Turdphil .....1.......1.111
++ Turdmeru .....1.1.1.1.11111
++ Turdilia ....1...1.111...11
++ Eritrube ..........1.1..111
++ Oenaoena 1.1............1.1
++ Phoephoe .......1..1.......
++ Acroarun .................1
++ Acroscir .....1.1..11....1.
++ Hippicte ......1.1....1..1.
++ Sylvatri ............1....1
++ Sylvbori .1..........1.1111
++ Sylvcurr ....1.1...1....1.1
++ Sylvcomm .........1.....1.1
++ Phylsibi .....1...11...11.1
++ Phyltrocs .........11.1.1111
++ Phyltrocd .............11.11
++ Reguregu 11.11..1...11.1.11
++ Muscstri .............11111
++ Parumajo 11.....11.1111.111
++ Parucaer .............1..11
++ Paruater ........1.....1...
++ Parumont ..........1......1
++ Cardchlo .......1........1.
++ Cardspin .....1..1111.11111
++ Pyrrpyrr ................1.
++ Loxicurv ..1...1..1....11..
++ Frincoel .1111..111.1111111
++ Sturvulg .........11..1..11
++ Orioorio ..............1.1.
++ Corvcoro 1.1.1..1..11111111
++ Corvcora .................1
++ Garrglan .................1
++ sites species
++ 18 46
++--> method: r2
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali ..........1.....11
++ Falcsubb ...............1..
++ Tetrtetr .................1
++ Scolrust ................11
++ Cucucano ................1.
++ Apusapus ................11
++ Jynxtorq ..............1..1
++ Deliurbi .................1
++ Hirurust ............111..1
++ Motaalba 1.1..1....1.111111
++ Anthtriv .1.........11..111
++ Trogtrog ..........1..11.11
++ Prunmodu .........1..1...11
++ Turdpila ..........1...1.11
++ Turdphil 1...1..1....1..111
++ Turdmeru .11.1.11...1.11111
++ Turdilia .....1....1.....11
++ Eritrube .....11.11..1.1111
++ Oenaoena ..1.......11....1.
++ Acroscir .................1
++ Hippicte .......1..........
++ Sylvbori .......1.......111
++ Sylvcurr .1.1.....1.1111111
++ Sylvcomm .............1..11
++ Phylsibi ..............1111
++ Phyltrocs 11..1..11111111111
++ Phyltrocd ...........1...111
++ Reguregu ..1.11111111111111
++ Muscstri ..........1.....11
++ Ficehypo ................1.
++ Parumajo .......11111111111
++ Parucaer ...............1.1
++ Parumont ................11
++ Cardchlo .................1
++ Cardspin ....1..1.111.11111
++ Loxicurv .............1...1
++ Frincoel 1..111.1111.111111
++ Sturvulg .......1.....11.11
++ Corvcoro ..1..11.1.11111111
++ Garrglan ................1.
++ sites species
++ 18 40
++--> method: c0
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali ............1.....
++ Falcsubb ..1...............
++ Tetrtetr ........1...1.....
++ Scolrust ...1....1.........
++ Colupalu .1................
++ Cucucano ........1.........
++ Apusapus ....1.............
++ Picucanu ............1.....
++ Dryomart ...1..............
++ Jynxtorq 1.................
++ Deliurbi ............1.1...
++ Hirurust .1.......1..11....
++ Motaalba 1..1..11.11.11..11
++ Anthtriv ....111....1......
++ Trogtrog .........1..11....
++ Prunmodu ..11..1...........
++ Turdpila .....1.....1......
++ Turdphil ..11....1....1...1
++ Turdmeru 11..1...1.1111111.
++ Turdilia .1.1..111..1......
++ Eritrube .1111.1.....1...1.
++ Oenaoena 1....11.....1.....
++ Phoephoe ......1...........
++ Acroarun ....1.............
++ Acroscir 1.......1.........
++ Hippicte ..1........1......
++ Sylvatri .1................
++ Sylvbori ......11...1.....1
++ Sylvcurr 1...11.11...1111..
++ Sylvcomm ...........11...1.
++ Phylsibi ..1....1...1.....1
++ Phyltrocs .11.1.1.1111111..1
++ Phyltrocd 11.............1..
++ Reguregu .1..1.111111.111.1
++ Muscstri .1......1.1.......
++ Ficehypo ...1......1.......
++ Parumajo .1..11.1.111...11.
++ Parucaer ....1....1........
++ Paruater ............1.....
++ Parumont .....1......1.....
++ Cardchlo 1.1...............
++ Cardspin 1.1111111...11.11.
++ Pyrrpyrr .1................
++ Loxicurv 1.......1.........
++ Frincoel 111111111111111111
++ Sturvulg ..........1...1..1
++ Orioorio ....1.............
++ Corvcoro .111111111.111.1.1
++ Corvcora .....1............
++ Garrglan ..1...............
++ sites species
++ 18 50
++--> method: swap
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali ............1.....
++ Falcsubb .......1..........
++ Tetrtetr .............1...1
++ Scolrust ............1...1.
++ Colupalu .................1
++ Cucucano ................1.
++ Apusapus .................1
++ Picucanu ...........1......
++ Dryomart ...............1..
++ Jynxtorq .............1....
++ Deliurbi ................11
++ Hirurust .........1.....111
++ Motaalba 1.1....1.11.1111.1
++ Anthtriv ..........1...1.11
++ Trogtrog .......1........11
++ Prunmodu ..........1.....11
++ Turdpila ................11
++ Turdphil .............11111
++ Turdmeru 11....111..11.1111
++ Turdilia ....11....1....111
++ Eritrube ........1.1.1.1111
++ Oenaoena ....1........1..11
++ Phoephoe .............1....
++ Acroarun .................1
++ Acroscir ..............1.1.
++ Hippicte ................11
++ Sylvatri .................1
++ Sylvbori ....11..........11
++ Sylvcurr ..1.1..1.1...11111
++ Sylvcomm .............1..11
++ Phylsibi .............1111.
++ Phyltrocs 1....111.1111.1111
++ Phyltrocd .....1.........1.1
++ Reguregu .1...1..1111111111
++ Muscstri ..............1.11
++ Ficehypo ......1..........1
++ Parumajo .......11.111.1111
++ Parucaer ............1...1.
++ Paruater .............1....
++ Parumont ..........11......
++ Cardchlo ..........1.....1.
++ Cardspin ..1....11111111111
++ Pyrrpyrr ................1.
++ Loxicurv ...............1.1
++ Frincoel 111111111111111111
++ Sturvulg ..............1.11
++ Orioorio .................1
++ Corvcoro .1111..1.111111111
++ Corvcora .................1
++ Garrglan ...........1......
++ sites species
++ 18 50
++--> method: tswap
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali .................1
++ Falcsubb 1.................
++ Tetrtetr ................11
++ Scolrust ......1..........1
++ Colupalu .................1
++ Cucucano ................1.
++ Apusapus .............1....
++ Picucanu .................1
++ Dryomart ..............1...
++ Jynxtorq .............1....
++ Deliurbi ................11
++ Hirurust .......1...1.1..1.
++ Motaalba 111.1..1..111...11
++ Anthtriv ............1.11.1
++ Trogtrog ...........1....11
++ Prunmodu ...............111
++ Turdpila ................11
++ Turdphil ..........1..1.111
++ Turdmeru .......11111111111
++ Turdilia ............111111
++ Eritrube ........1..11.1111
++ Oenaoena ..1....1.....1..1.
++ Phoephoe ..........1.......
++ Acroarun .................1
++ Acroscir ..........1.....1.
++ Hippicte ................11
++ Sylvatri .................1
++ Sylvbori ............1..111
++ Sylvcurr .....1.11.1.1.1111
++ Sylvcomm .............11..1
++ Phylsibi ..............1111
++ Phyltrocs ....111..111111111
++ Phyltrocd ..............1.11
++ Reguregu .11.11....11111111
++ Muscstri ..............1.11
++ Ficehypo ...............11.
++ Parumajo .1...1.1111...1.11
++ Parucaer .......1..1.......
++ Paruater .................1
++ Parumont ...............1.1
++ Cardchlo ...............11.
++ Cardspin 1.1.1..1..11111111
++ Pyrrpyrr ................1.
++ Loxicurv .........11.......
++ Frincoel 111111111111111111
++ Sturvulg .........1...1..1.
++ Orioorio .........1........
++ Corvcoro ...1111111.1111111
++ Corvcora .................1
++ Garrglan .................1
++ sites species
++ 18 50
++--> method: quasiswap
++** margintest:[1] TRUE
++
++ SLLS FS AGH KMK
++ v.e.TSl.Fsra Taua
++ aHdHokafapanRrisu
++ rohorakarlnsauvtn
++ tgogvtarhalkgtoai
++ hhlheashonnhslkhsO
++ oomodLkolddokaaesn
++ llelsealmeelanrvra
++ mmnmhdrmnttmrdiois
++ Pandhali ...........1......
++ Falcsubb .................1
++ Tetrtetr ................11
++ Scolrust .......1........1.
++ Colupalu .................1
++ Cucucano ................1.
++ Apusapus .................1
++ Picucanu ............1.....
++ Dryomart ................1.
++ Jynxtorq .................1
++ Deliurbi ..........1....1..
++ Hirurust .........1.1..1..1
++ Motaalba .1.....1111..11111
++ Anthtriv .........1....1.11
++ Trogtrog ............11...1
++ Prunmodu ..........11.....1
++ Turdpila ................11
++ Turdphil ........1....11.11
++ Turdmeru ..1.1.11..1.111111
++ Turdilia ...1.....1..11..11
++ Eritrube .1.....1..1.1..111
++ Oenaoena .......1....1...11
++ Phoephoe .................1
++ Acroarun .................1
++ Acroscir ..............1.1.
++ Hippicte ................11
++ Sylvatri .................1
++ Sylvbori ..........1....111
++ Sylvcurr .....1.1..111.1111
++ Sylvcomm .............11.1.
++ Phylsibi ..........1.1.11..
++ Phyltrocs 1.1.11.1..1.111111
++ Phyltrocd ...........1....11
++ Reguregu 1...11.1..11111111
++ Muscstri ...............111
++ Ficehypo .............1..1.
++ Parumajo ..1.....11.1.11111
++ Parucaer 1..............1..
++ Paruater ...............1..
++ Parumont ..........1......1
++ Cardchlo ................11
++ Cardspin ..1.11..11.1111111
++ Pyrrpyrr ................1.
++ Loxicurv ................11
++ Frincoel 111111111111111111
++ Sturvulg ......1........1.1
++ Orioorio .............1....
++ Corvcoro .1..11111111.11111
++ Corvcora ................1.
++ Garrglan ..............1...
++ sites species
++ 18 50
++> ## clean
++> rm(list = ls())
++>
++> ### end commsimulator
++>
++> ### set up permatfull/swap tests
++>
++> data(mite)
++> data(mite.env)
++> x <- as.matrix(subset(mite, mite.env$Topo == "Blanket"))
++> x <- x[, colSums(x) > 0]
++> gsum <- sum(x)
++> rsum <- rowSums(x)
++> csum <- colSums(x)
++> fill <- sum(x>0)
++> rfrq <- rowSums(x>0)
++> cfrq <- colSums(x>0)
++>
++> margintest <-
+++ function(x, gsum, rsum, csum, fill, rfrq, cfrq)
+++ {
+++ cat("grand sum: ")
+++ print(all.equal(sum(x), gsum, check.attributes = FALSE))
+++ cat("row sums: ")
+++ print(all.equal(rowSums(x), rsum, check.attributes = FALSE))
+++ cat("col sums: ")
+++ print(all.equal(colSums(x), csum, check.attributes = FALSE))
+++ cat("fill: ")
+++ print(all.equal(sum(x>0), fill, check.attributes = FALSE))
+++ cat("row freqs: ")
+++ print(all.equal(rowSums(x>0), rfrq, check.attributes = FALSE))
+++ cat("col freqs: ")
+++ print(all.equal(colSums(x>0), cfrq, check.attributes = FALSE))
+++ }
++>
++> ### permatfull
++>
++> set.seed(4711)
++> margin <- c("none", "rows", "columns", "both")
++> shuffle <- c("ind", "samp", "both")
++>
++> for(mar in margin) {
+++ for(what in shuffle) {
+++ cat("\n--> margin", mar, " shuffle", what, "<--\n")
+++ set.seed(4711)
+++ m <- permatfull(x, fixedmar = mar, shuffle = what, mtype = "count",
+++ times=1)$perm[[1]]
+++ margintest(m, gsum, rsum, csum, fill, rfrq, cfrq)
+++ print(m[,1:12])
+++ }
+++ }
++
++--> margin none shuffle ind <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.4053168"
++col sums: [1] "Mean relative difference: 1.1797"
++fill: [1] "Mean relative difference: 0.5960133"
++row freqs: [1] "Mean relative difference: 0.5960133"
++col freqs: [1] "Mean relative difference: 0.6122867"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 8 6 5 2 5 2 3 2 3 4 1 6
++9 4 3 4 11 2 5 3 4 3 2 6 3
++11 7 3 5 2 2 4 3 3 6 5 2 2
++14 2 7 1 3 5 1 4 5 7 6 8 6
++15 6 1 4 6 1 2 6 2 3 4 1 3
++19 4 6 2 5 5 8 4 7 3 5 3 3
++25 3 4 8 4 3 2 5 1 2 4 5 4
++30 2 5 4 3 1 4 2 8 5 2 2 1
++32 2 2 5 3 5 4 5 4 4 4 4 5
++33 1 4 3 2 2 7 4 1 3 3 2 1
++34 1 3 1 4 4 2 4 7 2 5 5 2
++35 4 5 3 8 10 6 2 6 5 1 6 3
++36 5 2 3 5 5 4 2 5 2 2 3 5
++37 2 5 5 5 5 1 6 5 3 3 1 2
++38 2 5 5 4 2 3 2 8 2 4 5 3
++39 2 8 7 2 2 4 5 5 6 2 1 5
++40 4 4 3 4 3 3 4 0 2 1 4 5
++41 5 7 5 3 4 3 2 2 6 4 2 6
++42 5 7 1 6 3 5 2 2 0 5 4 5
++43 8 7 2 4 1 1 4 2 2 1 7 3
++44 2 6 3 4 7 4 1 8 1 2 4 9
++45 6 4 3 1 5 3 3 3 3 1 6 5
++46 1 3 2 9 2 2 0 6 3 4 4 2
++47 5 7 5 4 3 8 2 4 1 5 5 5
++48 4 2 3 8 3 5 3 7 5 1 4 3
++49 6 4 1 3 5 4 3 3 3 3 6 2
++50 4 2 0 4 4 3 1 3 6 2 2 2
++51 1 1 3 2 4 1 3 5 2 5 6 5
++52 4 3 5 3 5 5 2 6 3 2 4 5
++53 3 4 9 5 2 0 2 5 3 3 2 2
++54 7 0 6 3 4 6 4 6 4 6 2 5
++55 0 5 2 8 5 3 4 6 2 4 3 2
++56 3 1 5 5 1 2 5 2 6 3 2 4
++57 3 5 5 2 1 4 4 5 4 3 5 2
++58 4 7 5 6 3 3 2 1 2 3 1 7
++59 2 3 1 0 5 5 2 6 4 5 6 6
++60 3 1 4 5 4 8 6 3 2 6 3 5
++61 4 4 2 4 4 6 6 6 6 4 6 4
++62 6 4 4 0 5 1 3 4 3 7 0 5
++63 4 5 2 3 2 3 4 4 2 3 3 2
++64 5 3 3 4 5 3 4 2 3 3 3 4
++66 2 6 3 4 2 4 1 5 2 1 5 5
++67 4 1 2 4 3 3 4 4 1 5 2 1
++68 3 5 6 3 6 4 4 4 1 6 1 1
++
++--> margin none shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.6473947"
++col sums: [1] "Mean relative difference: 1.243225"
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.3908451"
++col freqs: [1] "Mean relative difference: 0.6542373"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 1 0 3 0 0 0 0 0 73 0 0
++9 0 7 0 0 0 1 1 1 1 0 0 0
++11 0 0 0 0 0 0 0 0 1 0 2 0
++14 0 0 0 30 0 0 1 1 0 0 1 0
++15 2 3 4 0 0 1 1 0 0 0 8 0
++19 3 0 0 0 0 0 55 0 0 1 0 0
++25 3 0 0 20 0 0 0 8 7 0 0 56
++30 0 0 0 0 3 17 0 0 0 0 0 0
++32 0 0 7 17 1 0 1 2 2 0 3 0
++33 16 0 3 0 3 0 2 7 0 0 0 0
++34 2 0 0 8 0 1 0 0 2 2 0 0
++35 0 1 0 0 26 0 0 0 0 0 0 0
++36 0 1 20 6 0 2 0 0 34 1 0 2
++37 0 0 8 0 16 2 0 0 0 5 3 14
++38 0 0 0 1 0 17 0 0 0 0 0 3
++39 0 2 0 0 0 0 2 0 0 3 6 0
++40 0 0 0 0 24 0 0 0 1 12 7 2
++41 3 28 2 0 6 0 0 0 7 0 0 0
++42 0 0 9 1 0 0 57 1 3 0 0 0
++43 0 0 0 0 6 5 0 2 0 1 0 4
++44 0 1 0 0 0 0 0 0 17 0 15 0
++45 0 0 1 18 0 0 6 2 0 1 0 2
++46 0 20 0 1 0 0 10 2 10 5 23 0
++47 0 0 0 9 0 0 0 39 0 0 1 1
++48 0 3 2 14 0 0 21 1 0 0 2 0
++49 0 0 12 5 0 0 5 1 0 4 5 0
++50 0 0 1 5 0 8 0 0 0 0 0 0
++51 0 0 5 0 40 16 1 0 0 0 0 0
++52 0 0 0 0 0 0 0 0 0 2 18 0
++53 0 0 0 0 0 4 0 0 0 0 3 0
++54 2 20 0 0 4 0 12 0 0 0 0 0
++55 5 0 0 11 0 0 3 8 1 0 0 0
++56 0 0 0 0 0 0 0 0 0 0 0 2
++57 0 0 1 0 3 1 0 0 0 0 0 37
++58 1 9 0 0 0 4 0 0 0 0 0 3
++59 0 0 4 5 0 0 18 0 2 0 2 2
++60 5 0 1 0 5 2 0 1 2 3 0 0
++61 2 2 1 0 54 1 1 1 0 0 0 2
++62 0 0 20 2 0 5 26 0 3 15 8 0
++63 0 1 0 0 0 0 0 2 0 0 0 0
++64 0 22 44 0 4 1 9 5 2 0 1 6
++66 0 0 0 0 0 2 3 0 0 7 0 1
++67 5 6 0 0 1 0 0 0 5 0 0 0
++68 12 0 0 0 0 0 1 8 1 0 7 2
++
++--> margin none shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.4389986"
++col sums: [1] "Mean relative difference: 1.20342"
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.3670669"
++col freqs: [1] "Mean relative difference: 0.5723684"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 7 9 7 6 12 0 10 0 0 0 0
++9 10 0 12 0 11 0 8 0 7 0 7 0
++11 6 14 9 0 6 0 12 0 0 7 0 11
++14 0 13 7 0 0 0 0 0 0 0 0 10
++15 15 0 0 0 9 0 11 0 16 11 0 0
++19 0 8 9 8 0 0 11 12 9 0 12 0
++25 0 12 7 10 0 7 12 0 0 10 0 12
++30 7 0 0 8 0 10 0 8 9 0 0 0
++32 8 0 0 0 0 0 11 0 0 0 0 0
++33 11 6 0 8 0 9 0 0 0 0 0 0
++34 0 15 9 7 0 13 5 7 0 11 0 0
++35 11 0 0 0 0 9 0 0 5 0 0 0
++36 0 0 10 9 9 0 0 9 0 7 4 0
++37 9 0 0 13 0 8 0 0 0 0 0 0
++38 10 7 10 9 10 0 10 0 14 11 6 0
++39 8 0 0 9 0 0 0 0 0 10 0 8
++40 0 8 0 0 0 0 12 0 0 0 11 0
++41 0 0 11 0 13 0 5 10 13 0 0 13
++42 0 7 7 0 0 11 16 11 11 14 0 7
++43 9 11 0 0 6 0 0 0 0 0 11 13
++44 9 0 0 7 14 8 0 7 10 0 7 0
++45 0 0 0 0 0 0 7 0 11 0 0 0
++46 9 9 0 0 0 9 7 0 0 0 0 0
++47 0 7 0 7 10 0 7 10 11 0 0 0
++48 9 7 0 9 12 0 12 8 7 10 10 0
++49 0 0 8 0 0 6 0 0 0 0 0 0
++50 0 0 9 11 8 15 0 0 15 9 0 7
++51 0 0 0 0 0 10 0 0 0 6 0 17
++52 0 8 0 0 3 0 0 10 8 0 0 10
++53 0 0 0 0 0 0 12 7 0 0 10 8
++54 9 6 10 0 6 6 0 0 7 14 0 0
++55 7 0 0 0 0 9 0 0 12 0 0 0
++56 0 0 8 0 0 17 10 8 0 0 0 0
++57 0 13 0 0 17 11 16 0 0 8 8 11
++58 0 4 12 0 6 0 0 0 0 15 12 8
++59 0 7 0 0 0 0 9 0 0 10 0 12
++60 0 10 0 0 0 7 0 0 0 0 0 0
++61 0 0 16 7 0 10 0 6 0 0 0 0
++62 11 0 4 0 5 9 11 8 0 12 0 9
++63 0 0 0 0 6 0 0 0 9 11 0 0
++64 0 0 2 7 0 0 0 0 10 3 0 0
++66 0 0 0 0 5 0 11 0 0 0 0 8
++67 0 0 0 11 0 0 9 0 6 0 8 0
++68 13 0 0 0 0 0 9 0 0 0 0 10
++
++--> margin rows shuffle ind <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 1.1797"
++fill: [1] "Mean relative difference: 0.5715292"
++row freqs: [1] "Mean relative difference: 0.5715292"
++col freqs: [1] "Mean relative difference: 0.6076233"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 7 4 6 2 6 4 2 2 2 1 1 4
++9 4 2 2 4 5 4 5 1 4 3 5 3
++11 5 6 4 2 6 6 1 8 2 9 11 5
++14 9 12 6 9 4 6 6 7 5 10 7 4
++15 4 4 3 2 2 2 3 7 4 3 3 1
++19 0 2 4 4 4 3 4 2 2 4 1 5
++25 5 1 3 4 0 1 3 5 4 4 5 2
++30 3 0 3 2 1 7 4 3 2 1 4 0
++32 4 3 3 1 1 4 1 4 3 1 1 3
++33 4 4 2 4 3 5 2 7 1 8 4 1
++34 3 7 3 8 4 9 5 6 4 7 5 5
++35 3 1 4 3 5 2 2 3 4 2 5 4
++36 2 3 4 2 1 4 6 1 2 1 4 6
++37 2 9 1 4 6 2 4 2 5 6 3 2
++38 4 8 1 4 5 7 3 3 9 5 3 9
++39 1 4 4 1 4 1 3 1 6 5 3 2
++40 3 3 5 9 1 4 7 5 7 6 1 2
++41 4 3 3 3 3 2 2 0 3 1 4 2
++42 4 4 6 3 1 5 3 2 6 4 3 3
++43 3 3 6 2 8 4 6 4 4 5 1 4
++44 2 2 0 1 2 2 3 0 2 1 2 3
++45 6 7 3 8 3 2 2 4 5 8 1 4
++46 5 4 7 2 2 5 3 7 6 7 3 4
++47 4 4 3 3 1 5 2 3 4 6 5 4
++48 5 4 4 4 3 1 3 4 0 4 2 4
++49 2 6 1 1 0 1 6 5 3 2 1 2
++50 3 4 6 4 6 1 6 6 4 5 5 4
++51 0 2 2 7 3 3 4 5 7 4 3 5
++52 2 3 8 1 7 2 4 3 3 5 2 3
++53 5 7 3 5 3 5 3 6 7 4 4 5
++54 1 0 3 1 0 2 3 1 1 1 1 0
++55 0 1 2 2 3 5 0 1 2 0 2 3
++56 2 4 2 2 2 2 2 3 7 1 3 5
++57 0 1 0 0 0 0 1 0 0 1 0 0
++58 5 3 3 4 2 5 9 3 4 3 3 2
++59 7 2 2 2 4 3 0 1 2 1 3 3
++60 5 7 3 4 6 2 3 3 7 1 6 2
++61 2 0 1 1 1 1 1 1 1 2 1 4
++62 0 0 0 0 0 0 1 0 0 1 0 1
++63 3 2 0 3 3 2 2 1 1 0 4 1
++64 2 3 6 3 2 3 2 1 2 1 0 1
++66 2 1 6 2 4 2 2 4 5 1 1 2
++67 15 24 21 18 36 22 25 21 18 21 19 28
++68 6 4 4 2 0 4 6 1 1 1 4 6
++
++--> margin rows shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 1.173485"
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] "Mean relative difference: 0.6414474"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 1 0 2 1 3 1 1 0 8 4 0
++9 1 3 0 0 1 0 2 19 20 0 9 0
++11 0 7 42 2 36 1 1 1 3 0 35 17
++14 8 12 0 24 3 0 2 20 0 41 1 5
++15 7 0 0 0 0 1 8 0 0 0 1 0
++19 14 0 1 0 0 2 0 2 10 0 0 0
++25 32 0 0 0 0 0 0 2 2 0 0 2
++30 0 18 0 0 20 1 0 7 1 0 2 0
++32 0 0 5 0 9 2 4 0 2 5 0 0
++33 2 0 0 23 0 0 1 0 1 38 1 1
++34 0 2 22 39 2 1 1 0 1 0 9 0
++35 0 0 1 0 4 2 1 0 2 0 3 21
++36 2 12 0 0 0 3 0 1 0 0 3 0
++37 0 0 0 0 1 3 0 22 2 0 0 2
++38 0 0 0 0 15 4 1 0 0 1 8 0
++39 0 0 0 0 0 0 40 3 0 0 0 0
++40 26 65 0 0 0 0 1 5 0 0 0 0
++41 0 5 0 0 0 2 0 0 0 0 0 1
++42 0 1 0 0 0 0 0 7 6 0 0 0
++43 0 0 1 3 7 0 0 0 16 14 0 0
++44 0 0 28 0 0 0 0 0 0 0 0 0
++45 11 0 1 1 1 0 0 4 1 52 6 6
++46 0 0 0 12 4 1 0 28 42 0 5 4
++47 0 1 0 1 24 0 20 0 0 0 0 0
++48 10 0 0 3 0 53 0 29 0 0 0 4
++49 7 0 7 0 17 0 0 0 0 1 0 0
++50 8 0 14 3 2 0 0 0 0 0 0 0
++51 3 0 0 3 0 0 0 0 0 0 0 1
++52 46 0 0 0 0 3 0 0 0 0 1 0
++53 3 0 0 0 0 10 17 0 0 25 0 2
++54 0 0 0 0 0 0 1 0 0 0 0 0
++55 0 0 0 0 0 0 5 0 0 0 0 0
++56 0 54 3 0 0 17 0 0 0 0 0 0
++57 0 0 0 0 0 0 0 0 0 0 1 2
++58 1 1 0 2 0 0 1 0 12 0 57 0
++59 0 0 0 0 0 0 0 2 0 0 0 29
++60 0 1 0 0 4 0 28 1 1 16 55 0
++61 0 0 5 3 9 0 0 0 0 0 0 0
++62 0 0 0 0 3 2 0 0 1 0 1 0
++63 0 2 2 2 0 0 19 8 0 0 0 1
++64 0 0 1 0 0 0 0 4 0 3 0 0
++66 0 0 0 0 0 0 1 0 3 0 9 21
++67 0 4 0 11 0 7 0 0 0 723 0 0
++68 0 0 0 3 1 0 0 22 0 3 0 0
++
++--> margin rows shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 1.251856"
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] "Mean relative difference: 0.6503497"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 6 0 6 0 0 9 4 0 0 4 0 10
++9 0 6 8 3 9 0 0 0 0 0 9 6
++11 5 6 0 0 10 0 4 5 7 0 11 0
++14 9 0 12 8 13 0 0 13 13 13 15 0
++15 0 6 0 4 0 6 2 0 0 0 8 7
++19 4 0 0 6 2 8 3 0 0 0 3 4
++25 0 0 5 0 3 7 9 7 4 0 0 0
++30 4 3 0 3 0 0 5 6 5 5 3 8
++32 0 8 0 0 3 8 0 2 0 11 0 6
++33 11 0 0 0 0 0 0 0 8 12 0 7
++34 10 9 0 0 9 8 12 0 0 0 10 0
++35 6 5 0 9 0 2 0 0 4 0 0 0
++36 6 5 8 0 0 0 6 0 0 0 4 12
++37 0 0 0 7 8 0 0 12 10 0 13 0
++38 0 8 14 15 14 0 0 13 12 0 0 0
++39 9 0 0 4 8 0 8 0 9 0 0 13
++40 0 0 0 0 10 14 0 0 0 0 0 0
++41 0 3 9 0 0 0 0 0 0 9 7 0
++42 21 0 0 0 12 17 0 0 15 0 15 0
++43 0 0 0 0 0 13 17 0 12 0 0 13
++44 0 13 0 0 0 0 0 0 0 17 0 0
++45 0 0 6 10 9 8 0 8 6 16 0 9
++46 0 0 0 8 9 13 0 12 0 7 0 4
++47 13 5 13 9 0 0 9 0 0 0 6 7
++48 0 0 8 0 0 0 0 0 12 11 12 0
++49 0 7 0 10 0 0 0 8 14 0 7 8
++50 0 0 0 10 13 11 0 0 0 0 18 13
++51 0 5 0 7 0 6 0 0 5 7 0 11
++52 0 0 0 0 5 12 0 0 0 0 0 0
++53 9 0 5 0 7 0 0 8 0 0 0 12
++54 0 0 0 11 0 0 0 0 6 4 5 0
++55 0 0 6 3 0 0 7 8 0 0 9 0
++56 13 0 14 0 0 0 7 0 13 9 0 0
++57 0 0 0 0 0 0 0 1 0 0 0 0
++58 16 0 0 10 10 0 0 9 0 0 0 0
++59 0 0 18 0 13 0 0 0 0 0 0 13
++60 0 0 0 9 0 10 13 0 0 0 0 15
++61 0 0 3 8 0 0 0 0 5 0 0 0
++62 0 3 1 0 1 0 5 0 0 0 0 0
++63 0 0 0 8 10 0 0 0 7 0 0 7
++64 0 0 0 0 0 7 0 5 5 9 0 0
++66 0 6 0 0 9 0 5 0 7 7 0 0
++67 0 136 0 0 147 0 133 0 0 0 0 0
++68 7 0 0 0 0 5 7 0 0 9 0 5
++
++--> margin columns shuffle ind <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.4160623"
++col sums: [1] TRUE
++fill: [1] "Mean relative difference: 0.3566138"
++row freqs: [1] "Mean relative difference: 0.406423"
++col freqs: [1] "Mean relative difference: 0.3577495"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 12 0 4 0 0 0 1 0 0 1 0 2
++9 7 1 12 0 0 0 1 0 2 3 0 4
++11 10 0 12 1 0 0 2 0 1 4 0 2
++14 4 0 11 0 0 0 1 0 3 1 1 3
++15 10 2 12 3 0 1 3 0 0 5 2 1
++19 7 0 5 0 0 0 5 0 0 4 0 2
++25 5 1 10 0 0 0 3 0 2 7 1 1
++30 4 1 8 1 0 0 0 1 2 3 0 3
++32 5 1 16 0 0 0 2 0 2 3 2 1
++33 3 0 10 0 0 1 1 0 2 4 1 3
++34 3 1 12 0 0 0 2 0 2 5 1 2
++35 8 0 8 0 0 0 1 0 0 3 3 1
++36 9 1 5 0 0 0 2 0 1 6 2 1
++37 5 0 8 0 0 0 2 0 0 5 1 1
++38 4 1 9 2 0 0 1 0 2 3 1 1
++39 4 1 12 1 0 1 1 0 0 4 1 2
++40 7 2 11 0 0 0 2 0 2 2 2 0
++41 9 1 14 0 0 0 4 0 1 7 3 3
++42 8 2 8 0 0 0 2 0 1 4 1 1
++43 13 2 12 0 0 1 3 1 0 2 4 3
++44 4 2 5 0 2 0 1 0 1 4 1 0
++45 10 1 8 1 0 1 3 1 1 3 1 1
++46 3 0 8 0 0 0 3 0 1 2 0 1
++47 8 0 9 0 0 0 2 0 0 1 2 2
++48 7 1 7 0 0 0 1 1 0 5 2 1
++49 10 0 13 2 0 0 0 0 2 3 0 0
++50 8 0 9 0 0 0 3 0 2 6 3 4
++51 3 0 6 2 0 0 1 0 0 7 0 1
++52 7 0 5 0 0 0 3 1 0 5 4 1
++53 6 0 10 0 1 0 3 0 0 6 0 4
++54 11 0 13 0 0 0 1 0 1 4 1 1
++55 0 0 18 0 0 0 1 0 1 2 2 1
++56 6 1 13 1 0 0 2 0 0 5 0 2
++57 6 0 7 0 0 2 0 0 0 3 1 0
++58 8 1 11 1 0 0 2 1 1 6 0 3
++59 4 2 13 0 0 0 0 0 0 2 2 0
++60 6 0 12 0 0 0 1 0 0 3 3 1
++61 8 0 5 1 0 0 3 0 0 5 1 1
++62 10 0 10 0 0 1 2 0 3 6 0 3
++63 8 0 8 0 0 0 1 0 0 3 2 1
++64 9 1 10 0 0 1 3 0 2 3 0 3
++66 4 0 7 0 0 0 3 0 0 7 3 3
++67 8 1 12 0 0 0 4 0 1 8 1 3
++68 8 0 12 0 1 0 6 0 2 4 3 2
++
++--> margin columns shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.6283446"
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.3269565"
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 4 0 3 0 0 1 5 1 0 0 6 2
++9 0 0 4 0 0 0 3 0 3 0 0 0
++11 3 0 16 0 0 0 2 0 2 0 9 1
++14 20 0 2 0 0 0 0 0 0 2 0 0
++15 1 5 8 0 0 0 0 0 0 18 3 1
++19 6 0 14 0 0 0 0 0 2 0 0 0
++25 3 0 9 0 0 0 0 0 0 0 0 2
++30 12 0 8 0 2 0 2 0 0 0 0 1
++32 38 0 3 0 0 0 0 0 0 12 0 2
++33 36 0 5 0 0 0 0 0 0 11 2 3
++34 0 0 16 0 0 0 16 0 0 1 0 1
++35 11 0 12 0 0 0 5 0 7 0 0 0
++36 10 0 0 0 0 0 2 0 0 0 4 1
++37 3 0 3 0 0 2 2 0 0 0 20 0
++38 1 0 16 0 0 0 4 0 3 0 0 0
++39 41 0 37 0 0 0 0 0 3 24 0 0
++40 3 0 9 0 0 0 0 0 0 0 0 0
++41 4 0 2 0 0 0 0 0 4 0 5 5
++42 2 0 0 0 0 0 0 1 0 1 0 0
++43 8 0 10 0 1 1 15 0 0 0 0 2
++44 3 0 2 0 0 0 5 0 0 16 0 1
++45 0 0 5 9 0 0 0 0 0 0 0 0
++46 3 4 6 0 0 0 3 0 1 26 0 1
++47 0 0 10 0 0 0 0 0 0 3 0 4
++48 5 2 7 0 0 1 0 0 4 0 0 2
++49 1 0 14 0 0 1 0 0 0 0 0 1
++50 3 0 8 0 0 0 12 0 0 0 0 9
++51 1 4 2 0 0 0 1 0 0 1 0 0
++52 19 0 2 2 0 0 0 0 0 0 0 0
++53 3 1 6 0 1 2 0 0 1 1 0 3
++54 2 0 22 1 0 1 7 2 2 20 0 2
++55 5 0 35 0 0 0 0 0 2 0 0 1
++56 0 0 4 0 0 0 2 0 1 0 0 0
++57 6 0 5 0 0 0 0 0 0 0 0 0
++58 12 0 31 0 0 0 0 0 0 0 4 2
++59 1 7 0 2 0 0 0 0 0 36 0 0
++60 0 0 17 0 0 0 0 0 0 0 0 7
++61 0 0 12 0 0 0 0 1 3 0 5 4
++62 8 0 0 0 0 0 0 0 1 0 0 1
++63 4 0 18 2 0 0 1 1 2 0 0 10
++64 5 3 15 0 0 0 0 0 0 0 0 0
++66 6 1 12 0 0 0 0 0 0 1 0 1
++67 4 0 17 0 0 0 1 0 0 5 0 6
++68 2 0 3 0 0 0 0 0 0 1 0 0
++
++--> margin columns shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.4073882"
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.3514938"
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 6 0 15 0 0 0 6 0 4 5 0 2
++9 14 0 17 0 0 0 0 0 0 11 0 3
++11 8 0 10 4 0 0 0 0 2 0 0 2
++14 12 0 16 0 0 0 0 0 0 0 8 3
++15 12 4 6 0 0 0 4 0 0 15 0 0
++19 11 0 0 0 0 0 4 0 4 11 0 0
++25 0 0 11 0 0 0 0 0 0 15 0 2
++30 5 0 9 0 0 0 0 0 0 0 6 0
++32 8 0 10 0 0 0 0 0 2 0 0 3
++33 7 0 12 0 0 0 0 0 3 7 7 0
++34 11 0 9 0 0 1 0 0 0 0 0 0
++35 9 0 18 0 0 0 3 0 0 0 0 0
++36 12 0 11 2 1 0 0 0 0 0 0 3
++37 8 0 21 0 0 0 3 1 0 0 0 4
++38 8 0 9 0 0 0 0 0 2 10 6 0
++39 12 0 13 4 0 0 7 0 0 0 0 3
++40 5 0 12 0 0 0 6 0 4 0 0 0
++41 0 0 10 0 0 0 0 0 4 0 0 1
++42 9 0 9 0 0 0 0 0 0 0 0 0
++43 0 0 10 0 0 0 0 0 0 11 0 2
++44 7 3 0 0 0 0 3 0 0 12 0 0
++45 8 5 7 2 0 0 1 0 0 0 0 1
++46 7 1 12 0 1 0 4 0 3 14 0 0
++47 10 4 8 0 0 0 4 0 0 0 0 0
++48 5 0 14 0 0 0 0 0 1 0 0 2
++49 10 0 9 0 0 0 0 0 0 13 0 5
++50 0 0 3 0 2 0 7 0 0 8 0 4
++51 0 2 11 0 0 2 0 0 1 7 9 3
++52 0 0 11 0 0 1 6 1 0 6 6 2
++53 5 0 10 0 0 0 4 0 0 0 0 3
++54 4 0 7 0 0 0 0 0 0 13 0 0
++55 10 0 10 4 0 1 6 0 0 0 5 1
++56 10 3 10 0 0 2 0 0 0 0 4 4
++57 8 0 8 0 0 0 0 0 2 0 0 2
++58 4 0 0 0 0 1 4 1 0 10 0 5
++59 13 5 0 0 0 0 0 0 1 0 0 0
++60 11 0 8 0 0 0 0 0 0 0 0 0
++61 5 0 9 0 0 0 0 0 4 0 0 4
++62 6 0 7 0 0 1 0 1 0 0 0 3
++63 0 0 12 0 0 0 0 0 0 0 0 0
++64 4 0 15 0 0 0 8 0 0 0 0 3
++66 6 0 11 0 0 0 0 2 0 0 7 4
++67 8 0 8 0 0 0 8 0 2 0 0 0
++68 1 0 12 0 0 0 0 0 2 11 0 2
++
++--> margin both shuffle ind <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] "Mean relative difference: 0.3059361"
++row freqs: [1] "Mean relative difference: 0.3301435"
++col freqs: [1] "Mean relative difference: 0.3117783"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 2 0 5 0 0 0 3 0 1 4 1 1
++9 4 1 8 0 0 0 4 0 2 3 0 1
++11 10 2 15 1 0 0 3 0 1 4 5 4
++14 18 3 22 0 0 0 5 0 2 12 2 3
++15 2 1 10 0 0 0 2 1 0 5 0 1
++19 6 0 12 0 0 0 1 0 1 2 1 4
++25 4 1 11 1 0 0 3 0 0 3 0 3
++30 3 0 5 0 0 1 0 0 2 3 0 1
++32 3 0 5 0 0 0 1 0 0 4 1 0
++33 9 0 11 0 0 0 2 1 1 0 2 2
++34 12 0 14 0 0 1 2 0 1 9 1 1
++35 5 1 5 0 0 0 3 0 0 5 0 1
++36 8 0 13 0 0 0 4 0 1 4 2 0
++37 8 3 10 0 0 0 1 0 0 7 1 2
++38 9 0 14 1 0 0 3 0 3 5 2 6
++39 5 1 10 0 0 0 2 0 0 6 0 2
++40 7 1 10 3 0 0 0 0 0 8 2 2
++41 1 0 9 0 0 0 2 0 0 2 0 2
++42 6 0 15 0 0 0 3 0 0 3 3 2
++43 7 1 15 0 1 0 4 0 2 6 2 1
++44 5 0 4 0 0 0 0 0 1 3 0 0
++45 6 0 10 0 0 0 2 0 2 9 2 3
++46 12 0 16 1 0 0 2 1 0 1 3 1
++47 5 0 7 0 0 1 2 0 0 0 0 1
++48 3 0 11 1 0 0 1 0 1 5 1 2
++49 7 0 7 0 0 0 4 0 2 3 1 1
++50 8 2 7 0 0 0 0 1 2 6 1 1
++51 5 0 7 2 0 0 0 0 3 0 0 3
++52 7 2 2 0 1 0 1 0 0 1 1 2
++53 6 0 12 1 0 1 2 0 3 6 1 4
++54 2 0 7 0 0 0 0 0 0 1 0 1
++55 3 0 3 0 0 2 1 0 1 2 0 0
++56 5 0 9 0 0 0 3 0 0 3 2 0
++57 1 0 1 0 0 0 1 0 0 1 0 0
++58 10 0 7 0 0 0 2 0 2 6 1 2
++59 4 1 8 0 0 1 0 0 0 0 1 3
++60 3 1 14 0 0 0 2 0 1 5 2 1
++61 2 0 4 1 0 0 0 0 0 0 0 0
++62 0 0 0 0 0 0 0 0 0 0 0 0
++63 6 1 5 0 0 0 2 0 0 2 0 1
++64 6 0 7 0 0 0 2 0 0 1 4 1
++66 7 1 8 0 0 0 2 0 0 4 1 1
++67 49 4 45 3 2 2 11 1 4 23 9 8
++68 8 0 10 1 0 0 0 1 2 2 3 1
++
++--> margin both shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] "Mean relative difference: 0.3059361"
++row freqs: [1] "Mean relative difference: 0.3301435"
++col freqs: [1] "Mean relative difference: 0.3117783"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 2 0 5 0 0 0 3 0 1 4 1 1
++9 4 1 8 0 0 0 4 0 2 3 0 1
++11 10 2 15 1 0 0 3 0 1 4 5 4
++14 18 3 22 0 0 0 5 0 2 12 2 3
++15 2 1 10 0 0 0 2 1 0 5 0 1
++19 6 0 12 0 0 0 1 0 1 2 1 4
++25 4 1 11 1 0 0 3 0 0 3 0 3
++30 3 0 5 0 0 1 0 0 2 3 0 1
++32 3 0 5 0 0 0 1 0 0 4 1 0
++33 9 0 11 0 0 0 2 1 1 0 2 2
++34 12 0 14 0 0 1 2 0 1 9 1 1
++35 5 1 5 0 0 0 3 0 0 5 0 1
++36 8 0 13 0 0 0 4 0 1 4 2 0
++37 8 3 10 0 0 0 1 0 0 7 1 2
++38 9 0 14 1 0 0 3 0 3 5 2 6
++39 5 1 10 0 0 0 2 0 0 6 0 2
++40 7 1 10 3 0 0 0 0 0 8 2 2
++41 1 0 9 0 0 0 2 0 0 2 0 2
++42 6 0 15 0 0 0 3 0 0 3 3 2
++43 7 1 15 0 1 0 4 0 2 6 2 1
++44 5 0 4 0 0 0 0 0 1 3 0 0
++45 6 0 10 0 0 0 2 0 2 9 2 3
++46 12 0 16 1 0 0 2 1 0 1 3 1
++47 5 0 7 0 0 1 2 0 0 0 0 1
++48 3 0 11 1 0 0 1 0 1 5 1 2
++49 7 0 7 0 0 0 4 0 2 3 1 1
++50 8 2 7 0 0 0 0 1 2 6 1 1
++51 5 0 7 2 0 0 0 0 3 0 0 3
++52 7 2 2 0 1 0 1 0 0 1 1 2
++53 6 0 12 1 0 1 2 0 3 6 1 4
++54 2 0 7 0 0 0 0 0 0 1 0 1
++55 3 0 3 0 0 2 1 0 1 2 0 0
++56 5 0 9 0 0 0 3 0 0 3 2 0
++57 1 0 1 0 0 0 1 0 0 1 0 0
++58 10 0 7 0 0 0 2 0 2 6 1 2
++59 4 1 8 0 0 1 0 0 0 0 1 3
++60 3 1 14 0 0 0 2 0 1 5 2 1
++61 2 0 4 1 0 0 0 0 0 0 0 0
++62 0 0 0 0 0 0 0 0 0 0 0 0
++63 6 1 5 0 0 0 2 0 0 2 0 1
++64 6 0 7 0 0 0 2 0 0 1 4 1
++66 7 1 8 0 0 0 2 0 0 4 1 1
++67 49 4 45 3 2 2 11 1 4 23 9 8
++68 8 0 10 1 0 0 0 1 2 2 3 1
++
++--> margin both shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] "Mean relative difference: 0.3059361"
++row freqs: [1] "Mean relative difference: 0.3301435"
++col freqs: [1] "Mean relative difference: 0.3117783"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 2 0 5 0 0 0 3 0 1 4 1 1
++9 4 1 8 0 0 0 4 0 2 3 0 1
++11 10 2 15 1 0 0 3 0 1 4 5 4
++14 18 3 22 0 0 0 5 0 2 12 2 3
++15 2 1 10 0 0 0 2 1 0 5 0 1
++19 6 0 12 0 0 0 1 0 1 2 1 4
++25 4 1 11 1 0 0 3 0 0 3 0 3
++30 3 0 5 0 0 1 0 0 2 3 0 1
++32 3 0 5 0 0 0 1 0 0 4 1 0
++33 9 0 11 0 0 0 2 1 1 0 2 2
++34 12 0 14 0 0 1 2 0 1 9 1 1
++35 5 1 5 0 0 0 3 0 0 5 0 1
++36 8 0 13 0 0 0 4 0 1 4 2 0
++37 8 3 10 0 0 0 1 0 0 7 1 2
++38 9 0 14 1 0 0 3 0 3 5 2 6
++39 5 1 10 0 0 0 2 0 0 6 0 2
++40 7 1 10 3 0 0 0 0 0 8 2 2
++41 1 0 9 0 0 0 2 0 0 2 0 2
++42 6 0 15 0 0 0 3 0 0 3 3 2
++43 7 1 15 0 1 0 4 0 2 6 2 1
++44 5 0 4 0 0 0 0 0 1 3 0 0
++45 6 0 10 0 0 0 2 0 2 9 2 3
++46 12 0 16 1 0 0 2 1 0 1 3 1
++47 5 0 7 0 0 1 2 0 0 0 0 1
++48 3 0 11 1 0 0 1 0 1 5 1 2
++49 7 0 7 0 0 0 4 0 2 3 1 1
++50 8 2 7 0 0 0 0 1 2 6 1 1
++51 5 0 7 2 0 0 0 0 3 0 0 3
++52 7 2 2 0 1 0 1 0 0 1 1 2
++53 6 0 12 1 0 1 2 0 3 6 1 4
++54 2 0 7 0 0 0 0 0 0 1 0 1
++55 3 0 3 0 0 2 1 0 1 2 0 0
++56 5 0 9 0 0 0 3 0 0 3 2 0
++57 1 0 1 0 0 0 1 0 0 1 0 0
++58 10 0 7 0 0 0 2 0 2 6 1 2
++59 4 1 8 0 0 1 0 0 0 0 1 3
++60 3 1 14 0 0 0 2 0 1 5 2 1
++61 2 0 4 1 0 0 0 0 0 0 0 0
++62 0 0 0 0 0 0 0 0 0 0 0 0
++63 6 1 5 0 0 0 2 0 0 2 0 1
++64 6 0 7 0 0 0 2 0 0 1 4 1
++66 7 1 8 0 0 0 2 0 0 4 1 1
++67 49 4 45 3 2 2 11 1 4 23 9 8
++68 8 0 10 1 0 0 0 1 2 2 3 1
++>
++> ### permatswap
++> set.seed(4711)
++> methods <- c("swap", "quasiswap", "swsh", "abuswap")
++> margins <- c("rows", "columns", "both")
++> shuffle <- c("samp", "both")
++>
++> incompatible <- function(method, margin)
+++ {
+++ (method == "swap" && margin != "both") ||
+++ (method == "abuswap" && margin == "both") ||
+++ (method == "quasiswap" && margin != "both") ||
+++ (method == "swsh" && margin == "both")
+++ }
++>
++> for(method in methods) {
+++ for(margin in margins) {
+++ for(what in shuffle) {
+++ if (incompatible(method = method, margin = margin))
+++ next
+++ cat("\n*** ", method, " ***\n")
+++ cat("--> margin", margin, " shuffle", what, "<--\n")
+++ set.seed(4711)
+++ m <- permatswap(x, method = method, fixedmar = margin, shuffle
= what,
+++ mtype = "count", thin=100, times=1)$perm[[1]]
+++ margintest(m, gsum, rsum, csum,fill, rfrq, cfrq)
+++ print(m[,1:12])
+++ }
+++ }
+++ }
++
++*** swap ***
++--> margin both shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.1074766"
++col freqs: [1] "Mean relative difference: 0.08316008"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 3 0 2 1 0 2 0 0 1 4 10
++9 3 3 2 2 1 1 12 0 0 0 3 6
++11 38 5 35 9 0 0 5 0 1 0 6 2
++14 41 5 15 0 2 0 2 0 0 24 20 6
++15 11 0 6 0 0 0 1 0 0 0 5 1
++19 14 1 6 0 0 0 4 0 0 16 4 2
++25 12 4 12 2 0 1 0 0 1 1 2 1
++30 20 1 3 0 0 0 7 1 0 0 0 1
++32 4 0 9 0 0 0 5 0 0 5 9 2
++33 38 2 20 0 0 0 2 0 0 1 0 0
++34 5 0 18 1 0 0 1 0 1 12 4 1
++35 1 0 4 0 0 1 1 2 0 26 0 0
++36 3 1 2 0 0 1 3 0 0 20 0 0
++37 3 0 5 0 0 0 2 1 0 36 0 0
++38 13 0 11 0 0 0 12 0 4 0 0 0
++39 2 0 1 0 0 0 0 0 0 0 0 0
++40 1 0 31 0 0 0 0 0 0 1 0 3
++41 2 0 9 0 0 0 0 0 0 0 0 1
++42 0 0 12 0 0 1 0 0 2 0 0 1
++43 5 0 2 0 0 0 1 0 0 0 0 1
++44 0 0 0 0 0 0 6 0 0 0 0 0
++45 11 0 8 0 0 2 0 0 0 1 0 4
++46 4 0 0 0 0 0 2 0 1 12 0 2
++47 0 0 8 0 0 1 0 0 0 18 0 1
++48 0 0 3 0 0 0 0 0 0 0 0 1
++49 10 0 5 0 0 0 0 0 0 1 0 6
++50 4 0 37 0 0 0 0 0 2 2 0 0
++51 0 0 5 0 0 1 0 0 3 0 1 0
++52 1 0 12 0 0 0 2 0 2 2 0 0
++53 0 0 13 0 0 0 3 0 2 0 0 1
++54 2 0 7 0 0 0 0 0 0 0 0 1
++55 1 0 3 0 0 0 0 1 0 0 0 5
++56 1 0 16 0 0 0 0 0 3 0 0 0
++57 0 0 0 0 0 0 0 0 1 0 0 0
++58 0 0 13 0 0 0 0 1 0 0 0 0
++59 1 0 0 0 0 0 0 0 2 0 0 0
++60 1 2 16 0 0 0 0 0 7 0 0 0
++61 6 0 9 0 0 0 0 0 0 0 0 0
++62 3 0 0 0 0 0 0 0 1 0 0 0
++63 19 0 2 0 0 0 15 0 0 0 0 2
++64 1 0 27 0 0 0 0 0 3 0 0 3
++66 8 0 18 0 0 0 0 0 4 0 0 6
++67 4 0 3 0 0 0 0 0 0 0 0 2
++68 6 0 22 0 0 0 0 0 1 0 0 4
++
++*** swap ***
++--> margin both shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.1074766"
++col freqs: [1] "Mean relative difference: 0.08316008"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 3 0 2 1 0 2 0 0 1 4 10
++9 3 3 2 2 1 1 12 0 0 0 3 6
++11 38 5 35 9 0 0 5 0 1 0 6 2
++14 41 5 15 0 2 0 2 0 0 24 20 6
++15 11 0 6 0 0 0 1 0 0 0 5 1
++19 14 1 6 0 0 0 4 0 0 16 4 2
++25 12 4 12 2 0 1 0 0 1 1 2 1
++30 20 1 3 0 0 0 7 1 0 0 0 1
++32 4 0 9 0 0 0 5 0 0 5 9 2
++33 38 2 20 0 0 0 2 0 0 1 0 0
++34 5 0 18 1 0 0 1 0 1 12 4 1
++35 1 0 4 0 0 1 1 2 0 26 0 0
++36 3 1 2 0 0 1 3 0 0 20 0 0
++37 3 0 5 0 0 0 2 1 0 36 0 0
++38 13 0 11 0 0 0 12 0 4 0 0 0
++39 2 0 1 0 0 0 0 0 0 0 0 0
++40 1 0 31 0 0 0 0 0 0 1 0 3
++41 2 0 9 0 0 0 0 0 0 0 0 1
++42 0 0 12 0 0 1 0 0 2 0 0 1
++43 5 0 2 0 0 0 1 0 0 0 0 1
++44 0 0 0 0 0 0 6 0 0 0 0 0
++45 11 0 8 0 0 2 0 0 0 1 0 4
++46 4 0 0 0 0 0 2 0 1 12 0 2
++47 0 0 8 0 0 1 0 0 0 18 0 1
++48 0 0 3 0 0 0 0 0 0 0 0 1
++49 10 0 5 0 0 0 0 0 0 1 0 6
++50 4 0 37 0 0 0 0 0 2 2 0 0
++51 0 0 5 0 0 1 0 0 3 0 1 0
++52 1 0 12 0 0 0 2 0 2 2 0 0
++53 0 0 13 0 0 0 3 0 2 0 0 1
++54 2 0 7 0 0 0 0 0 0 0 0 1
++55 1 0 3 0 0 0 0 1 0 0 0 5
++56 1 0 16 0 0 0 0 0 3 0 0 0
++57 0 0 0 0 0 0 0 0 1 0 0 0
++58 0 0 13 0 0 0 0 1 0 0 0 0
++59 1 0 0 0 0 0 0 0 2 0 0 0
++60 1 2 16 0 0 0 0 0 7 0 0 0
++61 6 0 9 0 0 0 0 0 0 0 0 0
++62 3 0 0 0 0 0 0 0 1 0 0 0
++63 19 0 2 0 0 0 15 0 0 0 0 2
++64 1 0 27 0 0 0 0 0 3 0 0 3
++66 8 0 18 0 0 0 0 0 4 0 0 6
++67 4 0 3 0 0 0 0 0 0 0 0 2
++68 6 0 22 0 0 0 0 0 1 0 0 4
++
++*** quasiswap ***
++--> margin both shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.2614841"
++col freqs: [1] "Mean relative difference: 0.2735675"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 3 5 2 0 0 0 0 0 5 0 2
++9 4 0 13 0 1 0 4 0 0 1 0 5
++11 9 4 21 0 0 0 3 0 5 0 0 0
++14 18 2 22 2 0 0 2 0 0 11 4 0
++15 0 0 15 0 0 0 0 0 0 0 0 0
++19 1 1 6 0 0 0 10 0 0 0 9 2
++25 0 3 10 0 1 0 3 0 0 5 0 3
++30 12 0 17 0 0 2 0 2 2 2 1 0
++32 1 0 0 0 0 0 0 1 0 3 0 0
++33 18 0 0 0 0 0 1 0 1 4 1 0
++34 16 0 18 0 1 0 4 1 3 11 0 3
++35 2 1 8 0 0 0 0 0 3 0 3 11
++36 14 0 22 0 0 0 0 0 0 9 0 0
++37 2 0 13 0 0 0 0 0 2 17 1 5
++38 16 1 11 1 0 0 4 0 0 0 2 0
++39 7 1 7 0 0 0 2 1 0 10 0 2
++40 5 1 0 0 0 0 0 0 0 5 0 9
++41 0 0 19 0 0 0 1 0 1 2 0 0
++42 6 0 16 0 0 0 3 0 0 4 3 0
++43 4 0 22 0 0 0 0 0 2 6 4 6
++44 0 0 9 0 0 0 6 0 0 3 3 0
++45 3 0 3 2 0 0 0 0 0 18 0 0
++46 14 2 22 1 0 0 0 0 5 2 3 6
++47 5 0 6 6 0 0 4 0 0 1 0 0
++48 4 0 5 0 0 0 0 0 0 9 3 0
++49 12 0 6 0 0 0 2 0 2 0 0 2
++50 0 0 7 0 0 0 0 0 0 0 0 0
++51 0 0 0 0 0 2 0 0 0 0 0 3
++52 18 0 0 1 0 1 6 0 0 7 0 1
++53 7 0 15 0 1 0 3 0 0 6 0 0
++54 0 0 10 0 0 0 1 0 0 0 0 1
++55 5 0 0 0 0 0 1 0 1 0 0 0
++56 0 0 1 0 0 0 2 0 0 0 6 0
++57 1 0 0 0 0 0 0 0 0 0 0 4
++58 1 1 2 0 0 1 0 0 0 6 0 2
++59 6 0 0 0 0 0 0 0 0 0 0 5
++60 5 1 0 0 0 0 0 0 1 8 0 0
++61 2 0 5 0 0 1 3 0 0 0 0 0
++62 0 0 0 0 0 0 0 0 0 0 0 4
++63 6 0 0 0 0 0 11 0 5 0 0 0
++64 4 1 0 0 0 1 0 0 0 0 5 0
++66 5 5 3 1 0 0 0 0 3 0 0 0
++67 52 0 72 0 0 1 9 1 2 23 5 0
++68 14 0 19 0 0 0 3 0 3 1 5 0
++
++*** quasiswap ***
++--> margin both shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] "Mean relative difference: 0.2614841"
++col freqs: [1] "Mean relative difference: 0.2735675"
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 0 3 5 2 0 0 0 0 0 5 0 2
++9 4 0 13 0 1 0 4 0 0 1 0 5
++11 9 4 21 0 0 0 3 0 5 0 0 0
++14 18 2 22 2 0 0 2 0 0 11 4 0
++15 0 0 15 0 0 0 0 0 0 0 0 0
++19 1 1 6 0 0 0 10 0 0 0 9 2
++25 0 3 10 0 1 0 3 0 0 5 0 3
++30 12 0 17 0 0 2 0 2 2 2 1 0
++32 1 0 0 0 0 0 0 1 0 3 0 0
++33 18 0 0 0 0 0 1 0 1 4 1 0
++34 16 0 18 0 1 0 4 1 3 11 0 3
++35 2 1 8 0 0 0 0 0 3 0 3 11
++36 14 0 22 0 0 0 0 0 0 9 0 0
++37 2 0 13 0 0 0 0 0 2 17 1 5
++38 16 1 11 1 0 0 4 0 0 0 2 0
++39 7 1 7 0 0 0 2 1 0 10 0 2
++40 5 1 0 0 0 0 0 0 0 5 0 9
++41 0 0 19 0 0 0 1 0 1 2 0 0
++42 6 0 16 0 0 0 3 0 0 4 3 0
++43 4 0 22 0 0 0 0 0 2 6 4 6
++44 0 0 9 0 0 0 6 0 0 3 3 0
++45 3 0 3 2 0 0 0 0 0 18 0 0
++46 14 2 22 1 0 0 0 0 5 2 3 6
++47 5 0 6 6 0 0 4 0 0 1 0 0
++48 4 0 5 0 0 0 0 0 0 9 3 0
++49 12 0 6 0 0 0 2 0 2 0 0 2
++50 0 0 7 0 0 0 0 0 0 0 0 0
++51 0 0 0 0 0 2 0 0 0 0 0 3
++52 18 0 0 1 0 1 6 0 0 7 0 1
++53 7 0 15 0 1 0 3 0 0 6 0 0
++54 0 0 10 0 0 0 1 0 0 0 0 1
++55 5 0 0 0 0 0 1 0 1 0 0 0
++56 0 0 1 0 0 0 2 0 0 0 6 0
++57 1 0 0 0 0 0 0 0 0 0 0 4
++58 1 1 2 0 0 1 0 0 0 6 0 2
++59 6 0 0 0 0 0 0 0 0 0 0 5
++60 5 1 0 0 0 0 0 0 1 8 0 0
++61 2 0 5 0 0 1 3 0 0 0 0 0
++62 0 0 0 0 0 0 0 0 0 0 0 4
++63 6 0 0 0 0 0 11 0 5 0 0 0
++64 4 1 0 0 0 1 0 0 0 0 5 0
++66 5 5 3 1 0 0 0 0 3 0 0 0
++67 52 0 72 0 0 1 9 1 2 23 5 0
++68 14 0 19 0 0 0 3 0 3 1 5 0
++
++*** swsh ***
++--> margin rows shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 0.7988952"
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 8 0 2 0 6 4 0 0 0 0 3 1
++9 30 0 1 3 0 3 7 0 0 0 0 3
++11 1 0 6 2 0 0 7 17 7 1 0 42
++14 2 8 2 0 0 0 0 0 61 1 5 3
++15 1 0 2 0 0 0 0 0 1 1 0 29
++19 1 5 2 0 0 0 0 2 0 16 10 0
++25 2 0 1 0 0 1 0 0 12 5 0 0
++30 2 0 1 0 0 0 0 1 12 0 2 1
++32 5 4 2 0 0 0 0 0 5 0 0 9
++33 1 1 15 0 0 0 0 0 0 0 0 38
++34 9 0 1 1 0 5 2 0 4 22 1 4
++35 21 4 3 0 0 0 0 0 0 26 1 6
++36 20 0 2 0 12 1 4 3 0 0 0 1
++37 1 3 1 0 2 0 0 0 0 0 0 21
++38 1 0 4 0 0 1 2 0 0 0 0 6
++39 1 0 2 0 0 0 1 0 0 0 0 18
++40 6 0 26 0 0 0 1 0 1 0 0 0
++41 0 1 13 0 0 0 0 0 0 0 0 5
++42 1 0 12 0 0 1 0 0 0 0 0 0
++43 14 0 5 0 0 0 1 0 3 1 0 0
++44 0 0 28 0 0 0 0 0 0 0 0 24
++45 52 0 1 0 0 0 11 0 0 0 0 0
++46 2 0 3 0 0 0 0 1 42 4 0 0
++47 0 0 1 0 0 0 20 0 0 0 0 1
++48 6 0 29 0 0 0 0 0 0 0 0 0
++49 10 0 7 0 0 0 17 0 0 0 0 0
++50 8 0 3 0 0 0 0 0 0 0 0 9
++51 1 0 4 0 0 0 0 0 0 0 0 0
++52 5 0 9 0 0 0 0 0 0 1 0 0
++53 9 0 5 0 0 0 9 0 0 2 44 17
++54 1 0 21 0 0 0 0 0 0 1 0 0
++55 0 0 5 0 0 0 15 0 0 5 0 2
++56 17 0 16 0 0 0 0 0 0 0 0 3
++57 0 0 2 0 0 0 0 0 0 0 0 0
++58 0 0 12 0 0 0 1 0 0 2 0 0
++59 2 0 0 0 0 0 1 0 0 0 0 0
++60 2 0 0 0 0 0 0 0 1 0 0 55
++61 6 0 1 6 0 0 0 0 0 2 0 3
++62 1 0 0 0 0 0 0 0 5 0 0 0
++63 2 2 8 0 0 0 5 0 3 14 0 13
++64 4 0 0 0 0 0 1 0 3 0 0 16
++66 25 0 6 8 0 0 4 0 3 3 4 9
++67 0 0 3 0 0 0 0 0 0 0 0 0
++68 17 0 3 0 0 0 0 0 3 0 4 3
++
++*** swsh ***
++--> margin rows shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 0.6956672"
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 4 0 9 0 13 5 0 0 0 0 5 5
++9 7 0 9 10 0 5 4 0 0 0 0 5
++11 9 0 9 10 0 0 9 8 5 8 0 8
++14 15 15 8 0 0 0 0 0 13 15 8 11
++15 5 0 7 0 0 0 0 0 3 5 0 4
++19 7 8 6 0 0 0 0 5 0 5 7 0
++25 10 0 3 0 0 6 0 0 8 9 0 0
++30 1 0 5 0 0 0 0 5 5 0 7 7
++32 4 6 7 0 0 0 0 0 5 0 0 7
++33 4 6 11 0 0 0 0 0 0 0 0 10
++34 8 0 11 12 0 10 9 0 9 8 15 7
++35 6 5 7 0 0 0 0 0 0 5 7 3
++36 5 0 7 0 11 9 5 9 0 0 0 3
++37 13 12 8 0 8 0 0 0 0 0 0 7
++38 14 0 14 0 0 15 11 0 0 0 0 14
++39 7 0 9 0 0 0 7 0 0 0 0 11
++40 13 0 15 0 0 0 7 0 11 0 0 0
++41 0 9 5 0 0 0 0 0 0 0 0 5
++42 12 0 17 0 0 13 0 0 0 0 0 0
++43 10 0 14 0 0 0 12 0 17 16 0 0
++44 0 0 17 0 0 0 0 0 0 0 0 11
++45 9 0 11 0 0 0 8 0 0 0 0 0
++46 12 0 10 0 0 0 0 4 10 15 0 0
++47 0 0 7 0 0 0 10 0 0 0 0 8
++48 6 0 12 0 0 0 0 0 0 0 0 0
++49 7 0 7 0 0 0 11 0 0 0 0 0
++50 16 0 17 0 0 0 0 0 0 0 0 16
++51 6 0 7 0 0 0 0 0 0 0 0 0
++52 11 0 7 0 0 0 0 0 0 6 0 0
++53 6 0 8 0 0 0 8 0 0 11 11 12
++54 9 0 2 0 0 0 0 0 0 8 0 0
++55 0 0 7 0 0 0 6 0 0 6 0 5
++56 14 0 8 0 0 0 0 0 0 0 0 14
++57 0 0 1 0 0 0 0 0 0 0 0 0
++58 0 0 12 0 0 0 11 0 0 11 0 0
++59 11 0 0 0 0 0 16 0 0 0 0 0
++60 12 0 0 0 0 0 0 0 11 0 0 9
++61 6 0 4 7 0 0 0 0 0 7 0 4
++62 2 0 0 0 0 0 0 0 1 0 0 0
++63 5 5 6 0 0 0 6 0 2 6 0 4
++64 7 0 0 0 0 0 9 0 7 0 0 4
++66 3 0 15 9 0 0 11 0 6 5 8 9
++67 0 0 112 0 0 0 0 0 0 0 0 0
++68 5 0 9 0 0 0 0 0 11 0 8 13
++
++*** swsh ***
++--> margin columns shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.6253322"
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 12 0 16 0 2 2 0 0 0 0 3 3
++9 3 0 31 2 0 1 1 0 0 0 0 5
++11 8 0 3 9 0 0 2 1 1 1 0 1
++14 2 4 3 0 0 0 0 0 4 1 5 4
++15 3 0 3 0 0 0 0 0 2 18 0 2
++19 8 5 8 0 0 0 0 1 0 2 5 0
++25 3 0 22 0 0 1 0 0 1 24 0 0
++30 4 0 17 0 0 0 0 1 1 0 6 2
++32 5 1 35 0 0 0 0 0 2 0 0 9
++33 4 4 2 0 0 0 0 0 0 0 0 1
++34 6 0 12 1 0 1 5 0 2 3 20 2
++35 36 3 14 0 0 0 0 0 0 26 4 1
++36 1 0 7 0 1 2 2 1 0 0 0 6
++37 3 1 9 0 1 0 0 0 0 0 0 1
++38 6 0 3 0 0 1 7 0 0 0 0 1
++39 41 0 5 0 0 0 3 0 0 0 0 1
++40 4 0 12 0 0 0 4 0 1 0 0 0
++41 0 7 10 0 0 0 0 0 0 0 0 2
++42 1 0 8 0 0 1 0 0 0 0 0 0
++43 19 0 4 0 0 0 1 0 4 36 0 0
++44 0 0 16 0 0 0 0 0 0 0 0 7
++45 5 0 4 0 0 0 12 0 0 0 0 0
++46 20 0 16 0 0 0 0 2 3 1 0 0
++47 0 0 2 0 0 0 5 0 0 0 0 1
++48 3 0 6 0 0 0 0 0 0 0 0 0
++49 1 0 5 0 0 0 1 0 0 0 0 0
++50 3 0 15 0 0 0 0 0 0 0 0 4
++51 5 0 2 0 0 0 0 0 0 0 0 0
++52 2 0 18 0 0 0 0 0 0 20 0 0
++53 3 0 5 0 0 0 2 0 0 1 4 10
++54 6 0 10 0 0 0 0 0 0 12 0 0
++55 0 0 37 0 0 0 2 0 0 1 0 2
++56 2 0 17 0 0 0 0 0 0 0 0 1
++57 0 0 14 0 0 0 0 0 0 0 0 0
++58 0 0 8 0 0 0 15 0 0 16 0 0
++59 12 0 0 0 0 0 2 0 0 0 0 0
++60 1 0 0 0 0 0 0 0 3 0 0 1
++61 38 0 2 2 0 0 0 0 0 11 0 3
++62 10 0 0 0 0 0 0 0 7 0 0 0
++63 11 2 12 0 0 0 16 0 2 5 0 2
++64 3 0 0 0 0 0 3 0 2 0 0 1
++66 4 0 9 2 0 0 5 0 3 1 9 1
++67 0 0 2 0 0 0 0 0 0 0 0 0
++68 1 0 6 0 0 0 0 0 3 0 2 2
++
++*** swsh ***
++--> margin columns shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.3829337"
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 5 0 14 0 2 1 0 0 0 0 7 1
++9 11 0 10 4 0 1 4 0 0 0 0 1
++11 16 0 15 2 0 0 5 1 2 11 0 1
++14 8 3 9 0 0 0 0 0 5 8 9 2
++15 7 0 9 0 0 0 0 0 2 8 0 3
++19 7 4 13 0 0 0 0 1 0 6 7 0
++25 7 0 19 0 0 1 0 0 3 16 0 0
++30 9 0 5 0 0 0 0 1 4 0 7 4
++32 7 3 14 0 0 0 0 0 1 0 0 2
++33 8 3 9 0 0 0 0 0 0 0 0 4
++34 5 0 6 3 0 2 5 0 2 9 2 1
++35 5 2 6 0 0 0 0 0 0 9 5 3
++36 6 0 7 0 1 1 4 2 0 0 0 3
++37 14 4 14 0 1 0 0 0 0 0 0 2
++38 9 0 14 0 0 1 4 0 0 0 0 2
++39 5 0 7 0 0 0 6 0 0 0 0 5
++40 7 0 14 0 0 0 7 0 3 0 0 0
++41 0 4 14 0 0 0 0 0 0 0 0 2
++42 10 0 7 0 0 2 0 0 0 0 0 0
++43 9 0 12 0 0 0 5 0 3 6 0 0
++44 0 0 14 0 0 0 0 0 0 0 0 2
++45 13 0 8 0 0 0 5 0 0 0 0 0
++46 7 0 10 0 0 0 0 1 3 12 0 0
++47 0 0 8 0 0 0 4 0 0 0 0 2
++48 9 0 7 0 0 0 0 0 0 0 0 0
++49 10 0 10 0 0 0 7 0 0 0 0 0
++50 5 0 9 0 0 0 0 0 0 0 0 3
++51 4 0 4 0 0 0 0 0 0 0 0 0
++52 5 0 12 0 0 0 0 0 0 12 0 0
++53 9 0 8 0 0 0 4 0 0 13 7 1
++54 8 0 7 0 0 0 0 0 0 15 0 0
++55 0 0 9 0 0 0 7 0 0 15 0 2
++56 7 0 14 0 0 0 0 0 0 0 0 5
++57 0 0 13 0 0 0 0 0 0 0 0 0
++58 0 0 11 0 0 0 4 0 0 12 0 0
++59 11 0 0 0 0 0 4 0 0 0 0 0
++60 11 0 0 0 0 0 0 0 4 0 0 5
++61 10 0 11 3 0 0 0 0 0 6 0 4
++62 7 0 0 0 0 0 0 0 1 0 0 0
++63 6 4 19 0 0 0 3 0 1 13 0 2
++64 9 0 0 0 0 0 4 0 1 0 0 6
++66 7 0 14 4 0 0 6 0 4 8 8 3
++67 0 0 12 0 0 0 0 0 0 0 0 0
++68 6 0 12 0 0 0 0 0 2 0 6 5
++
++*** abuswap ***
++--> margin rows shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 0.2473584"
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 5 4 8 2 1 2 0 0 10 1 4 0
++9 3 3 2 2 0 1 12 0 0 0 3 0
++11 36 17 35 9 0 2 5 0 6 1 0 1
++14 41 5 2 0 2 0 3 0 0 24 20 20
++15 0 0 6 0 0 0 1 0 0 29 5 0
++19 12 2 1 1 0 0 4 0 0 16 0 2
++25 12 5 2 0 0 1 1 1 2 1 2 1
++30 1 1 2 0 0 0 7 1 0 0 0 20
++32 4 0 9 0 0 0 5 0 0 5 0 4
++33 38 0 2 0 0 0 0 0 0 1 1 0
++34 5 0 14 0 0 0 1 0 1 11 4 2
++35 1 0 2 0 0 1 29 2 3 0 3 26
++36 3 1 2 0 0 1 4 0 0 0 0 0
++37 3 0 5 0 0 0 2 0 0 36 0 0
++38 8 0 6 0 0 0 15 2 4 0 0 0
++39 0 0 0 0 0 0 0 0 0 0 0 40
++40 1 0 1 0 0 0 0 0 1 3 0 0
++41 1 0 10 0 0 0 0 0 0 0 0 0
++42 0 0 12 0 0 0 0 0 0 0 0 1
++43 5 0 2 0 0 0 16 0 0 0 0 1
++44 0 0 0 0 0 0 1 0 0 0 0 0
++45 6 0 8 0 0 0 0 0 0 52 0 4
++46 4 0 4 0 0 0 2 0 1 12 0 2
++47 0 0 24 0 0 0 0 0 18 0 0 1
++48 0 0 3 0 0 0 0 0 0 6 0 0
++49 10 0 14 0 0 0 25 0 0 1 0 7
++50 4 0 3 37 0 0 9 0 2 0 0 1
++51 2 0 5 0 0 3 0 0 0 0 1 8
++52 1 0 4 0 0 0 0 0 2 0 0 0
++53 3 0 2 0 0 0 0 17 2 0 0 1
++54 2 0 7 0 0 0 0 0 0 0 0 1
++55 6 0 3 0 0 0 0 0 0 0 0 5
++56 1 0 16 0 0 0 0 0 0 0 0 0
++57 0 0 0 0 0 0 0 0 0 0 0 0
++58 1 0 12 0 0 0 0 0 0 2 0 4
++59 1 0 0 0 0 0 0 0 0 0 0 2
++60 1 0 55 0 0 0 0 0 7 0 0 0
++61 6 0 9 0 0 0 0 0 0 0 0 0
++62 3 0 5 0 0 0 0 0 0 0 0 1
++63 19 0 13 0 0 0 0 0 2 0 0 2
++64 3 0 11 0 0 0 0 0 0 3 0 3
++66 4 0 8 0 3 0 0 0 4 0 0 18
++67 4 0 3 0 0 0 0 0 0 0 0 0
++68 6 0 32 0 0 0 0 0 3 0 0 4
++
++*** abuswap ***
++--> margin rows shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] TRUE
++col sums: [1] "Mean relative difference: 0.2473584"
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 5 4 8 2 1 2 0 0 10 1 4 0
++9 3 3 2 2 0 1 12 0 0 0 3 0
++11 36 17 35 9 0 2 5 0 6 1 0 1
++14 41 5 2 0 2 0 3 0 0 24 20 20
++15 0 0 6 0 0 0 1 0 0 29 5 0
++19 12 2 1 1 0 0 4 0 0 16 0 2
++25 12 5 2 0 0 1 1 1 2 1 2 1
++30 1 1 2 0 0 0 7 1 0 0 0 20
++32 4 0 9 0 0 0 5 0 0 5 0 4
++33 38 0 2 0 0 0 0 0 0 1 1 0
++34 5 0 14 0 0 0 1 0 1 11 4 2
++35 1 0 2 0 0 1 29 2 3 0 3 26
++36 3 1 2 0 0 1 4 0 0 0 0 0
++37 3 0 5 0 0 0 2 0 0 36 0 0
++38 8 0 6 0 0 0 15 2 4 0 0 0
++39 0 0 0 0 0 0 0 0 0 0 0 40
++40 1 0 1 0 0 0 0 0 1 3 0 0
++41 1 0 10 0 0 0 0 0 0 0 0 0
++42 0 0 12 0 0 0 0 0 0 0 0 1
++43 5 0 2 0 0 0 16 0 0 0 0 1
++44 0 0 0 0 0 0 1 0 0 0 0 0
++45 6 0 8 0 0 0 0 0 0 52 0 4
++46 4 0 4 0 0 0 2 0 1 12 0 2
++47 0 0 24 0 0 0 0 0 18 0 0 1
++48 0 0 3 0 0 0 0 0 0 6 0 0
++49 10 0 14 0 0 0 25 0 0 1 0 7
++50 4 0 3 37 0 0 9 0 2 0 0 1
++51 2 0 5 0 0 3 0 0 0 0 1 8
++52 1 0 4 0 0 0 0 0 2 0 0 0
++53 3 0 2 0 0 0 0 17 2 0 0 1
++54 2 0 7 0 0 0 0 0 0 0 0 1
++55 6 0 3 0 0 0 0 0 0 0 0 5
++56 1 0 16 0 0 0 0 0 0 0 0 0
++57 0 0 0 0 0 0 0 0 0 0 0 0
++58 1 0 12 0 0 0 0 0 0 2 0 4
++59 1 0 0 0 0 0 0 0 0 0 0 2
++60 1 0 55 0 0 0 0 0 7 0 0 0
++61 6 0 9 0 0 0 0 0 0 0 0 0
++62 3 0 5 0 0 0 0 0 0 0 0 1
++63 19 0 13 0 0 0 0 0 2 0 0 2
++64 3 0 11 0 0 0 0 0 0 3 0 3
++66 4 0 8 0 3 0 0 0 4 0 0 18
++67 4 0 3 0 0 0 0 0 0 0 0 0
++68 6 0 32 0 0 0 0 0 3 0 0 4
++
++*** abuswap ***
++--> margin columns shuffle samp <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.2329697"
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 5 4 8 2 1 2 0 0 1 1 4 0
++9 3 3 2 2 0 1 12 0 0 0 3 0
++11 36 4 35 9 0 2 5 0 3 20 0 1
++14 41 5 10 0 2 0 1 0 0 24 20 1
++15 0 0 18 0 0 0 3 0 0 1 5 0
++19 12 2 12 1 0 0 4 0 0 16 0 2
++25 12 7 17 0 0 1 2 1 1 1 2 1
++30 4 1 2 0 0 0 7 1 0 0 0 2
++32 1 0 9 0 0 0 5 0 0 5 0 1
++33 38 0 15 0 0 0 0 0 0 1 6 0
++34 5 0 14 0 0 0 1 0 1 11 4 2
++35 11 0 3 0 0 1 2 2 2 0 9 3
++36 3 1 2 0 0 1 2 0 0 0 0 0
++37 3 0 5 0 0 0 2 0 0 36 0 0
++38 3 0 6 0 0 0 15 1 4 0 0 0
++39 0 0 0 0 0 0 0 0 0 0 0 6
++40 1 0 2 0 0 0 0 0 3 3 0 0
++41 3 0 10 0 0 0 0 0 0 0 0 0
++42 0 0 12 0 0 0 0 0 0 0 0 1
++43 5 0 2 0 0 0 16 0 0 0 0 2
++44 0 0 0 0 0 0 3 0 0 0 0 0
++45 8 0 8 0 0 0 0 0 0 26 0 4
++46 4 0 4 0 0 0 2 0 1 12 0 2
++47 0 0 16 0 0 0 0 0 3 0 0 1
++48 0 0 3 0 0 0 0 0 0 1 0 0
++49 10 0 14 0 0 0 1 0 0 1 0 7
++50 4 0 17 2 0 0 5 0 2 0 0 1
++51 2 0 5 0 0 1 0 0 0 0 5 1
++52 2 0 4 0 0 0 0 0 2 0 0 0
++53 3 0 37 0 0 0 0 1 2 0 0 1
++54 2 0 7 0 0 0 0 0 0 0 0 2
++55 20 0 3 0 0 0 0 0 0 0 0 5
++56 1 0 16 0 0 0 0 0 0 0 0 0
++57 0 0 0 0 0 0 0 0 0 0 0 0
++58 6 0 12 0 0 0 0 0 0 2 0 1
++59 1 0 0 0 0 0 0 0 0 0 0 1
++60 1 0 22 0 0 0 0 0 7 0 0 0
++61 6 0 9 0 0 0 0 0 0 0 0 0
++62 3 0 5 0 0 0 0 0 0 0 0 10
++63 19 0 31 0 0 0 0 0 2 0 0 2
++64 3 0 8 0 0 0 0 0 0 18 0 3
++66 8 0 6 0 1 0 0 0 4 0 0 9
++67 4 0 3 0 0 0 0 0 0 0 0 0
++68 6 0 16 0 0 0 0 0 3 0 0 4
++
++*** abuswap ***
++--> margin columns shuffle both <--
++grand sum: [1] TRUE
++row sums: [1] "Mean relative difference: 0.2329697"
++col sums: [1] TRUE
++fill: [1] TRUE
++row freqs: [1] TRUE
++col freqs: [1] TRUE
++ Brachy PHTH HPAV RARD SSTR Protopl MEGR MPRO TVIE HMIN HMIN2 NPRA
++8 5 4 8 2 1 2 0 0 1 1 4 0
++9 3 3 2 2 0 1 12 0 0 0 3 0
++11 36 4 35 9 0 2 5 0 3 20 0 1
++14 41 5 10 0 2 0 1 0 0 24 20 1
++15 0 0 18 0 0 0 3 0 0 1 5 0
++19 12 2 12 1 0 0 4 0 0 16 0 2
++25 12 7 17 0 0 1 2 1 1 1 2 1
++30 4 1 2 0 0 0 7 1 0 0 0 2
++32 1 0 9 0 0 0 5 0 0 5 0 1
++33 38 0 15 0 0 0 0 0 0 1 6 0
++34 5 0 14 0 0 0 1 0 1 11 4 2
++35 11 0 3 0 0 1 2 2 2 0 9 3
++36 3 1 2 0 0 1 2 0 0 0 0 0
++37 3 0 5 0 0 0 2 0 0 36 0 0
++38 3 0 6 0 0 0 15 1 4 0 0 0
++39 0 0 0 0 0 0 0 0 0 0 0 6
++40 1 0 2 0 0 0 0 0 3 3 0 0
++41 3 0 10 0 0 0 0 0 0 0 0 0
++42 0 0 12 0 0 0 0 0 0 0 0 1
++43 5 0 2 0 0 0 16 0 0 0 0 2
++44 0 0 0 0 0 0 3 0 0 0 0 0
++45 8 0 8 0 0 0 0 0 0 26 0 4
++46 4 0 4 0 0 0 2 0 1 12 0 2
++47 0 0 16 0 0 0 0 0 3 0 0 1
++48 0 0 3 0 0 0 0 0 0 1 0 0
++49 10 0 14 0 0 0 1 0 0 1 0 7
++50 4 0 17 2 0 0 5 0 2 0 0 1
++51 2 0 5 0 0 1 0 0 0 0 5 1
++52 2 0 4 0 0 0 0 0 2 0 0 0
++53 3 0 37 0 0 0 0 1 2 0 0 1
++54 2 0 7 0 0 0 0 0 0 0 0 2
++55 20 0 3 0 0 0 0 0 0 0 0 5
++56 1 0 16 0 0 0 0 0 0 0 0 0
++57 0 0 0 0 0 0 0 0 0 0 0 0
++58 6 0 12 0 0 0 0 0 0 2 0 1
++59 1 0 0 0 0 0 0 0 0 0 0 1
++60 1 0 22 0 0 0 0 0 7 0 0 0
++61 6 0 9 0 0 0 0 0 0 0 0 0
++62 3 0 5 0 0 0 0 0 0 0 0 10
++63 19 0 31 0 0 0 0 0 2 0 0 2
++64 3 0 8 0 0 0 0 0 0 18 0 3
++66 8 0 6 0 1 0 0 0 4 0 0 9
++67 4 0 3 0 0 0 0 0 0 0 0 0
++68 6 0 16 0 0 0 0 0 3 0 0 4
++Warning messages:
++1: In permatswap(x, method = method, fixedmar = margin, shuffle = what, :
++ quantitative swap method may not yield random null models, use only to
study its properties
++2: In permatswap(x, method = method, fixedmar = margin, shuffle = what, :
++ quantitative swap method may not yield random null models, use only to
study its properties
++> ### end permatswap
++>
++> nm=nullmodel(m,"abuswap_c")
++> sm<-simulate(nm,nsim=100,thin=100,burnin=100)
++>
++> ### clean
++> rm(list = ls())
++>
++> ## end permatfull/swap
++>
++> ## The following vegan functions depend on *oecosimu*: adipart
++> ## hiersimu multipart raupcrick. The following functions directly
++> ## depend on *commsimulator*: permatfull1 permatswap1. All these have
++> ## derived and/or method functions. These should not be broken.
++>
++> ## Do not break raupcrick:
++> set.seed(4711)
++> data(sipoo)
++> as.numeric(raupcrick(sipoo, nsimul = 99))
++ [1] 0.09 0.03 0.29 0.02 0.63 0.48 0.15 0.70 0.76 0.64 0.10 0.20 0.26 0.79
0.80
++ [16] 0.87 0.90 0.02 0.37 0.04 0.03 0.44 0.16 0.26 0.36 0.25 0.15 0.22 0.30
0.47
++ [31] 0.85 0.46 0.57 0.40 0.02 0.26 0.62 0.06 0.71 0.82 0.35 0.06 0.13 0.02
0.59
++ [46] 0.63 0.32 0.93 0.04 0.06 0.05 0.17 0.05 0.11 0.20 0.13 0.14 0.67 0.34
0.33
++ [61] 0.63 0.79 0.02 0.06 0.16 0.44 0.18 0.06 0.01 0.01 0.09 0.15 0.18 0.29
0.40
++ [76] 0.08 0.12 0.01 0.01 0.01 0.08 0.04 0.58 0.02 0.10 0.26 0.37 0.53 0.23
0.11
++ [91] 0.26 0.15 0.16 0.68 0.37 0.35 0.85 0.53 0.01 0.29 0.05 0.10 0.16 0.25
0.39
++[106] 0.79 0.37 0.98 0.02 0.04 0.12 0.04 0.91 0.02 0.12 0.27 0.38 0.07 0.31
0.43
++[121] 0.60 0.34 0.74 0.80 0.99 0.19 0.15 0.60 0.18 0.37 0.69 0.97 0.01 0.07
0.18
++[136] 0.20 0.08 0.55 0.27 0.01 0.01 0.25 0.18 0.63 0.65 0.61 1.00 0.09 0.49
0.15
++[151] 0.15 0.48 0.90
++> rm(list = ls())
++> ## end raupcrick
++>
++>
++>
++> proc.time()
++ user system elapsed
++ 2.046 0.106 2.142
+diff --git a/tests/vegan-tests.R b/tests/vegan-tests.R
+new file mode 100644
+index 0000000..74f8ea2
+--- /dev/null
++++ b/tests/vegan-tests.R
+@@ -0,0 +1,262 @@
++### vegan-tests: unit tests for vegan functions
++
++### This file contains unit tests for vegan functions. This file is
++### run in R CMD check and its results are compared against previously
++### saved results in vegan-tests.Rout.save. If you change tests, you
++### must generate new vegan-tests.Rout.save in this directory.
++
++### The current plan is that tests/ are not included in the CRAN
++### release, but only in the development versin of vegan in R-Forge.
++
++### The tests here are not intended for human reading. The tests need
++### not be ecological or biologically meaningful, but they are only
++### intended for testing strange arguments, protect against
++### regressions and test correctness of results.
++
++### The tests are in a single file and you should clean work space
++### after the unit test. You should set random number seed (if needed)
++### for comparison against vegan-tests.Rout.save, and you should
++### remove the seed after the unit test. If possible, avoid very long
++### lasting tests.
++
++###<--- BEGIN TESTS --->
++suppressPackageStartupMessages(require(vegan))
++###<--- BEGIN anova.cca test --->
++### anova.cca tests: should work with (1) subset, (2) missing values,
++### (3) with functions of variables poly(A1,2), (4) variables in data
++### frame attached or in data=, or (5) in working environment
++set.seed(4711)
++data(dune)
++data(dune.env)
++df <- dune.env
++df$Management[c(1,5)] <- NA
++## formula
++fla <- as.formula("dune ~ Management + poly(A1, 2) + spno")
++### variable in the .GlobalEnv
++spno <- specnumber(dune)
++### data= argument
++## cca/rda
++m <- cca(fla, data=df, na.action=na.exclude, subset = Use != "Pasture" &
spno > 7)
++anova(m, permutations=99)
++## vegan 2.1-40 cannot handle missing data in next two
++##anova(m, by="term", permutations=99)
++##anova(m, by="margin", permutations=99)
++anova(m, by="axis", permutations=99)
++## capscale
++p <- capscale(fla, data=df, na.action=na.exclude, subset = Use != "Pasture" &
spno > 7)
++anova(p, permutations=99)
++## vegan 2.1-40 cannot handle missing data in next two
++##anova(p, by="term", permutations=99)
++##anova(p, by="margin", permutations=99)
++anova(p, by="axis", permutations=99)
++## see that capscale can be updated and also works with 'dist' input
++dis <- vegdist(dune)
++p <- update(p, dis ~ .)
++anova(p, permutations=99)
++## vegan 2.1-40 cannot handle missing data in next two
++##anova(p, by="term", permutations=99)
++##anova(p, by="margin", permutations=99)
++anova(p, by="axis", permutations=99)
++### attach()ed data frame instead of data=
++attach(df)
++q <- cca(fla, na.action = na.omit, subset = Use != "Pasture" & spno > 7)
++anova(q, permutations=99)
++## commented tests below fail in vegan 2.1-40 because number of
++## observations changes
++##anova(q, by="term", permutations=99)
++##anova(q, by="margin", permutations=99)
++anova(q, by="axis", permutations=99)
++### Check that constrained ordination functions can be embedded.
++### The data.frame 'df' is still attach()ed.
++foo <- function(bar, Y, X, ...)
++{
++ bar <- match.fun(bar)
++ bar(Y ~ X, ...)
++}
++foo("cca", dune, Management, na.action = na.omit)
++foo("rda", dune, Management, na.action = na.omit)
++foo("capscale", dune, Management, dist="jaccard", na.action = na.omit)
++foo("capscale", vegdist(dune), Management, na.action = na.omit)
++foo("capscale", dune, Management, na.action = na.omit) ## fails in 2.2-1
++###
++detach(df)
++### Check that statistics match in partial constrained ordination
++m <- cca(dune ~ A1 + Moisture + Condition(Management), dune.env, subset = A1
> 3)
++tab <- anova(m, by = "axis", permutations = 99)
++m
++tab
++all.equal(tab[,2], c(m$CCA$eig, m$CA$tot.chi), check.attributes=FALSE)
++tab[nrow(tab),1] == m$CA$rank
++## clean-up
++rm(df, spno, fla, m, p, q, tab, dis, foo, .Random.seed)
++### <--- END anova.cca test --->
++
++### Sven Neulinger <address@hidden> reported failures in
++### partial analysis which (mostly) were fixed in r2087. Below his test.
++
++set.seed(4711)
++X <- matrix(rnorm(30*6), 30, 6)
++
++A <- factor(rep(rep(c("a","b"), each=3),5))
++B <- factor(rep(c("a","b","c"), 10))
++## Sven Neulinger's tests failed still in 2.2-1, now due to look-up
++## order: function stats::C was found before matrix 'C'. The test was
++## OK when non-function name was used ('CC').
++C <- factor(rep(c(1:5), each=6))
++
++## partial db-RDA
++cap.model.cond <- capscale(X ~ A + B + Condition(C))
++anova(cap.model.cond, by="axis", strata=C) # -> error pre r2287
++anova(cap.model.cond, by="terms", strata=C) # -> error pre r2287
++
++## db-RDA without conditional factor
++cap.model <- capscale(X ~ A + B)
++anova(cap.model, by="axis", strata=C) # -> no error
++anova(cap.model, by="terms", strata=C) # -> no error
++
++# partial RDA
++rda.model.cond <- rda(X ~ A + B + Condition(C))
++anova(rda.model.cond, by="axis", strata=C) # -> no error
++anova(rda.model.cond, by="terms", strata=C) # -> error pre r2287
++
++# RDA without conditional factor
++rda.model <- rda(X ~ A + B)
++anova(rda.model, by="axis", strata=C) # -> no error
++anova(rda.model, by="terms", strata=C) # -> no error
++## clean.up
++rm(X, A, B, C, cap.model.cond, cap.model, rda.model.cond, rda.model)
++### end Sven Neulinger's tests
++
++### Benedicte Bachelot informed us that several anova.cca* functions
++### failed if community data name was the same as a function name: the
++### function name was found first, and used instead ofa data. This
++### seems to be related to the same problem that Sven Neulinger
++### communicated, and his examples still faile if Condition or strata
++### are function names. However, the following examples that failed
++### should work now:
++
++set.seed(4711)
++cca <- dune
++m <- cca(cca ~ ., dune.env)
++anova(m, by="term")
++m <- capscale(cca ~ ., dune.env)
++anova(m, by="term")
++rm(m, cca)
++
++### end Benedicte Bachelot tests
++
++### Richard Telford tweeted this example on 23/2/2015. Fails in 2.2-1,
++### but should work now. Also issue #100 in github.com/vegandevs/vegan.
++set.seed(4711)
++data(dune, dune.env)
++foo <- function(x, env) {
++ m <- rda(x ~ Manure + A1, data = env)
++ anova(m, by = "margin")
++}
++out <- lapply(dune, foo, env = dune.env)
++out$Poatriv
++rm(foo, out)
++### end Richard Telford test
++
++
++### nestednodf: test case by Daniel Spitale in a comment to News on
++### the release of vegan 1.17-6 in vegan.r-forge.r-project.org.
++x <- c(1,0,1,1,1,1,1,1,0,0,0,1,1,1,0,1,1,0,0,0,1,1,0,0,0)
++m1 <- matrix(x, nrow=5, ncol=5, byrow=FALSE)# as in Fig 2 Almeida-Neto et al
2008.
++(nodf1 <- nestednodf(m1, order = FALSE, weighted = FALSE))
++## Now the same matrix but with abundance data
++x <- c(5,0,2,1,1,4,1,1,0,0,0,7,1,1,0,3,1,0,0,0,9,1,0,0,0)
++m <- matrix(x, nrow=5, ncol=5, byrow=FALSE)
++(nodfq <- nestednodf(m, order = FALSE, weighted = FALSE))
++identical(nodf1, nodfq)
++rm(x, m, m1, nodfq, nodf1)
++### end nestednodf
++
++### envfit & plot.envfit: latter failed if na.action resulted in only
++### observation with a given factor level was removed. plot.envfit would
++### fail with error about too long subscript
++### fixed case where data presented to envfit also has extraneous levels
++data(dune)
++data(dune.env)
++## add a new level to one of the factors
++levels(dune.env$Management) <- c(levels(dune.env$Management), "foo")
++## fit nMDS and envfit
++set.seed(1)
++mod <- metaMDS(dune)
++ef <- envfit(mod, dune.env, permutations = 99)
++plot(mod)
++plot(ef, p.max = 0.1)
++rm(mod, ef)
++### end envfit & plot.envfit
++
++### protest (& Procrustes analysis): Stability of the permutations and
++### other results.
++data(mite)
++mod <- rda(mite)
++x <- scores(mod, display = "si", choices=1:6)
++set.seed(4711)
++xp <- x[sample(nrow(x)),]
++pro <- protest(x, xp, permutations = how(nperm = 99))
++pro
++pro$t
++rm(x, xp, pro)
++### end protest
++
++### Check that functions related to predict.rda work correctly for all
++### constrained ordination methods.
++
++### simulate.rda/cca/capscale: based on predict.* and the following
++### should get back the data
++data(dune, dune.env)
++ind <- seq_len(nrow(dune))
++target <- as.matrix(dune)
++## rda
++mod <- rda(dune ~ Condition(Moisture) + Management + A1, dune.env)
++dat <- simulate(mod, indx = ind)
++all.equal(dat, target, check.attributes = FALSE)
++## cca
++mod <- cca(dune ~ Condition(Moisture) + Management + A1, dune.env)
++dat <- simulate(mod, indx = ind)
++all.equal(dat, target, check.attributes = FALSE)
++## capscale: Euclidean distances -- non-Euclidean distances have an
++## imaginary component and will not give back the data.
++d <- dist(dune)
++mod <- capscale(d ~ Condition(Moisture) + Management + A1, dune.env)
++dat <- simulate(mod, indx = ind)
++all.equal(dat, d, check.attributes = FALSE)
++## clean up
++rm(ind, target, mod, dat, d)
++### end simulate.*
++
++### test metaMDS works with long expression for comm
++### originally reported to GLS by Richard Telford
++data(varespec)
++set.seed(1)
++mod <- metaMDS(subset(varespec, select = colSums(varespec) > 0, subset =
rowSums(varespec) > 0))
++mod
++rm(mod)
++### The above should run without error & last lines tests changes to the
++### printed output
++
++## dbrda tests
++
++## the following three should be all equal
++data(varespec, varechem)
++(mr <- rda(varespec ~ Al + P + Condition(pH), varechem))
++(md <- dbrda(varespec ~ Al + P + Condition(pH), varechem))
++(mc <- capscale(varespec ~ Al + P + Condition(pH), varechem))
++## the following two should be zero (within 1e-15)
++p <- shuffleSet(nrow(varespec), 999)
++range(permustats(anova(mr, permutations=p))$permutations -
++ permustats(anova(md, permutations=p))$permutations)
++range(permustats(anova(mr, permutations=p))$permutations -
++ permustats(anova(mc, permutations=p))$permutations)
++## the following two should be equal
++d <- vegdist(wisconsin(sqrt(varespec)))
++(md <- dbrda(d ~ Al + P + Condition(pH), varechem))
++(mc <- capscale(d ~ Al + P + Condition(pH), varechem))
++(amd <- anova(md, permutations = p))
++(amc <- anova(mc, permutations = p))
++## should be zero (within 1e-15)
++range(permustats(amd)$permutations - permustats(amc)$permutations)
++rm(mr, md, mc, d, amd, amc)
+diff --git a/tests/vegan-tests.Rout.save b/tests/vegan-tests.Rout.save
+new file mode 100644
+index 0000000..e5c5028
+--- /dev/null
++++ b/tests/vegan-tests.Rout.save
+@@ -0,0 +1,886 @@
++
++R Under development (unstable) (2016-06-08 r70729) -- "Unsuffered
Consequences"
++Copyright (C) 2016 The R Foundation for Statistical Computing
++Platform: x86_64-pc-linux-gnu (64-bit)
++
++R is free software and comes with ABSOLUTELY NO WARRANTY.
++You are welcome to redistribute it under certain conditions.
++Type 'license()' or 'licence()' for distribution details.
++
++R is a collaborative project with many contributors.
++Type 'contributors()' for more information and
++'citation()' on how to cite R or R packages in publications.
++
++Type 'demo()' for some demos, 'help()' for on-line help, or
++'help.start()' for an HTML browser interface to help.
++Type 'q()' to quit R.
++
++> ### vegan-tests: unit tests for vegan functions
++>
++> ### This file contains unit tests for vegan functions. This file is
++> ### run in R CMD check and its results are compared against previously
++> ### saved results in vegan-tests.Rout.save. If you change tests, you
++> ### must generate new vegan-tests.Rout.save in this directory.
++>
++> ### The current plan is that tests/ are not included in the CRAN
++> ### release, but only in the development versin of vegan in R-Forge.
++>
++> ### The tests here are not intended for human reading. The tests need
++> ### not be ecological or biologically meaningful, but they are only
++> ### intended for testing strange arguments, protect against
++> ### regressions and test correctness of results.
++>
++> ### The tests are in a single file and you should clean work space
++> ### after the unit test. You should set random number seed (if needed)
++> ### for comparison against vegan-tests.Rout.save, and you should
++> ### remove the seed after the unit test. If possible, avoid very long
++> ### lasting tests.
++>
++> ###<--- BEGIN TESTS --->
++> suppressPackageStartupMessages(require(vegan))
++> ###<--- BEGIN anova.cca test --->
++> ### anova.cca tests: should work with (1) subset, (2) missing values,
++> ### (3) with functions of variables poly(A1,2), (4) variables in data
++> ### frame attached or in data=, or (5) in working environment
++> set.seed(4711)
++> data(dune)
++> data(dune.env)
++> df <- dune.env
++> df$Management[c(1,5)] <- NA
++> ## formula
++> fla <- as.formula("dune ~ Management + poly(A1, 2) + spno")
++> ### variable in the .GlobalEnv
++> spno <- specnumber(dune)
++> ### data= argument
++> ## cca/rda
++> m <- cca(fla, data=df, na.action=na.exclude, subset = Use != "Pasture" &
spno > 7)
++> anova(m, permutations=99)
++Permutation test for cca under reduced model
++Permutation: free
++Number of permutations: 99
++
++Model: cca(formula = dune ~ Management + poly(A1, 2) + spno, data = df,
na.action = na.exclude, subset = Use != "Pasture" & spno > 7)
++ Df ChiSquare F Pr(>F)
++Model 6 1.25838 1.3106 0.07 .
++Residual 5 0.80011
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ## vegan 2.1-40 cannot handle missing data in next two
++> ##anova(m, by="term", permutations=99)
++> ##anova(m, by="margin", permutations=99)
++> anova(m, by="axis", permutations=99)
++Permutation test for cca under reduced model
++Marginal tests for axes
++Permutation: free
++Number of permutations: 99
++
++Model: cca(formula = dune ~ Management + poly(A1, 2) + spno, data = df,
na.action = na.exclude, subset = object$subset)
++ Df ChiSquare F Pr(>F)
++CCA1 1 0.46993 2.9366 0.01 **
++CCA2 1 0.26217 1.6384 0.15
++CCA3 1 0.19308 1.2066 0.29
++CCA4 1 0.18345 1.1464 0.37
++CCA5 1 0.08871 0.5544 0.77
++CCA6 1 0.06104 0.3815 0.90
++Residual 5 0.80011
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ## capscale
++> p <- capscale(fla, data=df, na.action=na.exclude, subset = Use != "Pasture"
& spno > 7)
++> anova(p, permutations=99)
++Permutation test for capscale under reduced model
++Permutation: free
++Number of permutations: 99
++
++Model: capscale(formula = dune ~ Management + poly(A1, 2) + spno, data = df,
na.action = na.exclude, subset = Use != "Pasture" & spno > 7)
++ Df Variance F Pr(>F)
++Model 6 59.582 1.6462 0.04 *
++Residual 5 30.160
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ## vegan 2.1-40 cannot handle missing data in next two
++> ##anova(p, by="term", permutations=99)
++> ##anova(p, by="margin", permutations=99)
++> anova(p, by="axis", permutations=99)
++Permutation test for capscale under reduced model
++Marginal tests for axes
++Permutation: free
++Number of permutations: 99
++
++Model: capscale(formula = dune ~ Management + poly(A1, 2) + spno, data = df,
na.action = na.exclude, subset = object$subset)
++ Df Variance F Pr(>F)
++CAP1 1 25.0252 4.1487 0.03 *
++CAP2 1 15.8759 2.6319 0.07 .
++CAP3 1 8.0942 1.3419 0.25
++CAP4 1 5.0675 0.8401 0.64
++CAP5 1 3.5671 0.5914 0.85
++CAP6 1 1.9520 0.3236 0.96
++Residual 5 30.1605
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ## see that capscale can be updated and also works with 'dist' input
++> dis <- vegdist(dune)
++> p <- update(p, dis ~ .)
++> anova(p, permutations=99)
++Permutation test for capscale under reduced model
++Permutation: free
++Number of permutations: 99
++
++Model: capscale(formula = dis ~ Management + poly(A1, 2) + spno, data = df,
na.action = na.exclude, subset = Use != "Pasture" & spno > 7)
++ Df SumOfSqs F Pr(>F)
++Model 6 1.52798 1.6548 0.11
++Residual 5 0.76948
++> ## vegan 2.1-40 cannot handle missing data in next two
++> ##anova(p, by="term", permutations=99)
++> ##anova(p, by="margin", permutations=99)
++> anova(p, by="axis", permutations=99)
++imaginary variation was discarded
++Permutation test for capscale under reduced model
++Marginal tests for axes
++Permutation: free
++Number of permutations: 99
++
++Model: capscale(formula = dis ~ Management + poly(A1, 2) + spno, data = df,
na.action = na.exclude, subset = object$subset)
++ Df SumOfSqs F Pr(>F)
++CAP1 1 0.77834 4.9533 0.02 *
++CAP2 1 0.45691 2.9078 0.03 *
++CAP3 1 0.14701 0.9355 0.50
++CAP4 1 0.11879 0.7560 0.65
++CAP5 1 0.04213 0.2681 0.98
++CAP6 1 0.00522 0.0332 1.00
++Residual 5 0.76948
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ### attach()ed data frame instead of data=
++> attach(df)
++> q <- cca(fla, na.action = na.omit, subset = Use != "Pasture" & spno > 7)
++> anova(q, permutations=99)
++Permutation test for cca under reduced model
++Permutation: free
++Number of permutations: 99
++
++Model: cca(formula = dune ~ Management + poly(A1, 2) + spno, na.action =
na.omit, subset = Use != "Pasture" & spno > 7)
++ Df ChiSquare F Pr(>F)
++Model 6 1.25838 1.3106 0.12
++Residual 5 0.80011
++> ## commented tests below fail in vegan 2.1-40 because number of
++> ## observations changes
++> ##anova(q, by="term", permutations=99)
++> ##anova(q, by="margin", permutations=99)
++> anova(q, by="axis", permutations=99)
++Permutation test for cca under reduced model
++Marginal tests for axes
++Permutation: free
++Number of permutations: 99
++
++Model: cca(formula = dune ~ Management + poly(A1, 2) + spno, na.action =
na.omit, subset = object$subset)
++ Df ChiSquare F Pr(>F)
++CCA1 1 0.46993 2.9366 0.03 *
++CCA2 1 0.26217 1.6384 0.20
++CCA3 1 0.19308 1.2066 0.34
++CCA4 1 0.18345 1.1464 0.33
++CCA5 1 0.08871 0.5544 0.69
++CCA6 1 0.06104 0.3815 0.89
++Residual 5 0.80011
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ### Check that constrained ordination functions can be embedded.
++> ### The data.frame 'df' is still attach()ed.
++> foo <- function(bar, Y, X, ...)
+++ {
+++ bar <- match.fun(bar)
+++ bar(Y ~ X, ...)
+++ }
++> foo("cca", dune, Management, na.action = na.omit)
++Call: cca(formula = Y ~ X, na.action = ..1)
++
++ Inertia Proportion Rank
++Total 2.0949 1.0000
++Constrained 0.6236 0.2977 3
++Unconstrained 1.4713 0.7023 14
++Inertia is mean squared contingency coefficient
++2 observations deleted due to missingness
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3
++0.31573 0.20203 0.10584
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11
++0.4478 0.1910 0.1788 0.1409 0.1202 0.0949 0.0732 0.0570 0.0531 0.0448 0.0312
++ CA12 CA13 CA14
++0.0181 0.0104 0.0098
++
++> foo("rda", dune, Management, na.action = na.omit)
++Call: rda(formula = Y ~ X, na.action = ..1)
++
++ Inertia Proportion Rank
++Total 85.1176 1.0000
++Constrained 32.7765 0.3851 3
++Unconstrained 52.3412 0.6149 14
++Inertia is variance
++2 observations deleted due to missingness
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2 RDA3
++15.956 13.621 3.199
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
++14.893 9.136 6.042 5.674 3.638 2.865 2.504 1.968 1.888 1.239 0.959
++ PC12 PC13 PC14
++ 0.779 0.501 0.255
++
++> foo("capscale", dune, Management, dist="jaccard", na.action = na.omit)
++Call: bar(formula = Y ~ X, distance = "jaccard", na.action = ..2)
++
++ Inertia Proportion Rank
++Total 5.1432 1.0000
++Constrained 1.6453 0.3199 3
++Unconstrained 3.4979 0.6801 14
++Inertia is squared Jaccard distance
++2 observations deleted due to missingness
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2 CAP3
++0.8504 0.6047 0.1902
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11
++1.0474 0.4406 0.4386 0.4054 0.2847 0.1947 0.1546 0.1506 0.0957 0.0935 0.0761
++ MDS12 MDS13 MDS14
++0.0603 0.0436 0.0120
++
++> foo("capscale", vegdist(dune), Management, na.action = na.omit)
++Call: bar(formula = Y ~ X, na.action = ..1)
++
++ Inertia Proportion Eigenvals Rank
++Total 3.7652 1.0000 3.9493
++Constrained 1.4179 0.3766 1.4456 3
++Unconstrained 2.3473 0.6234 2.5037 13
++Imaginary -0.1841 4
++Inertia is squared Bray distance
++2 observations deleted due to missingness
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2 CAP3
++0.7910 0.5497 0.1050
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11
++1.0756 0.3691 0.3349 0.2695 0.1651 0.0931 0.0726 0.0673 0.0286 0.0174 0.0093
++ MDS12 MDS13
++0.0011 0.0001
++
++> foo("capscale", dune, Management, na.action = na.omit) ## fails in 2.2-1
++Call: bar(formula = Y ~ X, na.action = ..1)
++
++ Inertia Proportion Rank
++Total 85.1176 1.0000
++Constrained 32.7765 0.3851 3
++Unconstrained 52.3412 0.6149 14
++Inertia is mean squared Euclidean distance
++2 observations deleted due to missingness
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2 CAP3
++15.956 13.621 3.199
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11
++14.893 9.136 6.042 5.674 3.638 2.865 2.504 1.968 1.888 1.239 0.959
++ MDS12 MDS13 MDS14
++ 0.779 0.501 0.255
++
++> ###
++> detach(df)
++> ### Check that statistics match in partial constrained ordination
++> m <- cca(dune ~ A1 + Moisture + Condition(Management), dune.env, subset =
A1 > 3)
++> tab <- anova(m, by = "axis", permutations = 99)
++> m
++Call: cca(formula = dune ~ A1 + Moisture + Condition(Management), data
++= dune.env, subset = A1 > 3)
++
++ Inertia Proportion Rank
++Total 2.0976 1.0000
++Conditional 0.6251 0.2980 3
++Constrained 0.5555 0.2648 4
++Unconstrained 0.9170 0.4372 10
++Inertia is mean squared contingency coefficient
++
++Eigenvalues for constrained axes:
++ CCA1 CCA2 CCA3 CCA4
++0.27109 0.14057 0.08761 0.05624
++
++Eigenvalues for unconstrained axes:
++ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9
CA10
++0.31042 0.13634 0.11974 0.09408 0.07763 0.06425 0.04449 0.02925 0.02785
0.01299
++
++> tab
++Permutation test for cca under reduced model
++Marginal tests for axes
++Permutation: free
++Number of permutations: 99
++
++Model: cca(formula = dune ~ A1 + Moisture + Condition(Management), data =
dune.env, subset = object$subset)
++ Df ChiSquare F Pr(>F)
++CCA1 1 0.27109 2.9561 0.04 *
++CCA2 1 0.14057 1.5329 0.21
++CCA3 1 0.08761 0.9553 0.72
++CCA4 1 0.05624 0.6132 0.97
++Residual 10 0.91705
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> all.equal(tab[,2], c(m$CCA$eig, m$CA$tot.chi), check.attributes=FALSE)
++[1] TRUE
++> tab[nrow(tab),1] == m$CA$rank
++[1] TRUE
++> ## clean-up
++> rm(df, spno, fla, m, p, q, tab, dis, foo, .Random.seed)
++> ### <--- END anova.cca test --->
++>
++> ### Sven Neulinger <address@hidden> reported failures in
++> ### partial analysis which (mostly) were fixed in r2087. Below his test.
++>
++> set.seed(4711)
++> X <- matrix(rnorm(30*6), 30, 6)
++>
++> A <- factor(rep(rep(c("a","b"), each=3),5))
++> B <- factor(rep(c("a","b","c"), 10))
++> ## Sven Neulinger's tests failed still in 2.2-1, now due to look-up
++> ## order: function stats::C was found before matrix 'C'. The test was
++> ## OK when non-function name was used ('CC').
++> C <- factor(rep(c(1:5), each=6))
++>
++> ## partial db-RDA
++> cap.model.cond <- capscale(X ~ A + B + Condition(C))
++> anova(cap.model.cond, by="axis", strata=C) # -> error pre r2287
++Permutation test for capscale under reduced model
++Marginal tests for axes
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = X ~ A + B + Condition(C))
++ Df Variance F Pr(>F)
++CAP1 1 0.2682 1.3075 0.242
++CAP2 1 0.0685 0.3339 0.921
++CAP3 1 0.0455 0.2217 0.966
++Residual 22 4.5130
++> anova(cap.model.cond, by="terms", strata=C) # -> error pre r2287
++Permutation test for capscale under reduced model
++Terms added sequentially (first to last)
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = X ~ A + B + Condition(C))
++ Df Variance F Pr(>F)
++A 1 0.1316 0.6415 0.680
++B 2 0.2506 0.6108 0.824
++Residual 22 4.5130
++>
++> ## db-RDA without conditional factor
++> cap.model <- capscale(X ~ A + B)
++> anova(cap.model, by="axis", strata=C) # -> no error
++Permutation test for capscale under reduced model
++Marginal tests for axes
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = X ~ A + B)
++ Df Variance F Pr(>F)
++CAP1 1 0.2682 1.3267 0.240
++CAP2 1 0.0685 0.3388 0.913
++CAP3 1 0.0455 0.2249 0.964
++Residual 26 5.2565
++> anova(cap.model, by="terms", strata=C) # -> no error
++Permutation test for capscale under reduced model
++Terms added sequentially (first to last)
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = X ~ A + B)
++ Df Variance F Pr(>F)
++A 1 0.1316 0.6509 0.693
++B 2 0.2506 0.6198 0.829
++Residual 26 5.2565
++>
++> # partial RDA
++> rda.model.cond <- rda(X ~ A + B + Condition(C))
++> anova(rda.model.cond, by="axis", strata=C) # -> no error
++Permutation test for rda under reduced model
++Marginal tests for axes
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = X ~ A + B + Condition(C))
++ Df Variance F Pr(>F)
++RDA1 1 0.2682 1.3075 0.286
++RDA2 1 0.0685 0.3339 0.921
++RDA3 1 0.0455 0.2217 0.963
++Residual 22 4.5130
++> anova(rda.model.cond, by="terms", strata=C) # -> error pre r2287
++Permutation test for rda under reduced model
++Terms added sequentially (first to last)
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = X ~ A + B + Condition(C))
++ Df Variance F Pr(>F)
++A 1 0.1316 0.6415 0.669
++B 2 0.2506 0.6108 0.827
++Residual 22 4.5130
++>
++> # RDA without conditional factor
++> rda.model <- rda(X ~ A + B)
++> anova(rda.model, by="axis", strata=C) # -> no error
++Permutation test for rda under reduced model
++Marginal tests for axes
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = X ~ A + B)
++ Df Variance F Pr(>F)
++RDA1 1 0.2682 1.3267 0.258
++RDA2 1 0.0685 0.3388 0.898
++RDA3 1 0.0455 0.2249 0.971
++Residual 26 5.2565
++> anova(rda.model, by="terms", strata=C) # -> no error
++Permutation test for rda under reduced model
++Terms added sequentially (first to last)
++Blocks: strata
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = X ~ A + B)
++ Df Variance F Pr(>F)
++A 1 0.1316 0.6509 0.695
++B 2 0.2506 0.6198 0.820
++Residual 26 5.2565
++> ## clean.up
++> rm(X, A, B, C, cap.model.cond, cap.model, rda.model.cond, rda.model)
++> ### end Sven Neulinger's tests
++>
++> ### Benedicte Bachelot informed us that several anova.cca* functions
++> ### failed if community data name was the same as a function name: the
++> ### function name was found first, and used instead ofa data. This
++> ### seems to be related to the same problem that Sven Neulinger
++> ### communicated, and his examples still faile if Condition or strata
++> ### are function names. However, the following examples that failed
++> ### should work now:
++>
++> set.seed(4711)
++> cca <- dune
++> m <- cca(cca ~ ., dune.env)
++> anova(m, by="term")
++Permutation test for cca under reduced model
++Terms added sequentially (first to last)
++Permutation: free
++Number of permutations: 999
++
++Model: cca(formula = cca ~ A1 + Moisture + Management + Use + Manure, data =
dune.env)
++ Df ChiSquare F Pr(>F)
++A1 1 0.22476 2.5704 0.010 **
++Moisture 3 0.51898 1.9783 0.008 **
++Management 3 0.39543 1.5074 0.064 .
++Use 2 0.10910 0.6238 0.905
++Manure 3 0.25490 0.9717 0.493
++Residual 7 0.61210
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> m <- capscale(cca ~ ., dune.env)
++> anova(m, by="term")
++Permutation test for capscale under reduced model
++Terms added sequentially (first to last)
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = cca ~ A1 + Moisture + Management + Use + Manure,
data = dune.env)
++ Df Variance F Pr(>F)
++A1 1 8.1148 2.7156 0.008 **
++Moisture 3 21.6497 2.4150 0.008 **
++Management 3 19.1153 2.1323 0.009 **
++Use 2 4.7007 0.7865 0.730
++Manure 3 9.6257 1.0737 0.377
++Residual 7 20.9175
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> rm(m, cca)
++>
++> ### end Benedicte Bachelot tests
++>
++> ### Richard Telford tweeted this example on 23/2/2015. Fails in 2.2-1,
++> ### but should work now. Also issue #100 in github.com/vegandevs/vegan.
++> set.seed(4711)
++> data(dune, dune.env)
++> foo <- function(x, env) {
+++ m <- rda(x ~ Manure + A1, data = env)
+++ anova(m, by = "margin")
+++ }
++> out <- lapply(dune, foo, env = dune.env)
++> out$Poatriv
++Permutation test for rda under reduced model
++Marginal effects of terms
++Permutation: free
++Number of permutations: 999
++
++Model: rda(formula = x ~ Manure + A1, data = env)
++ Df Variance F Pr(>F)
++Manure 4 4.7257 5.7006 0.009 **
++A1 1 0.0153 0.0736 0.783
++Residual 14 2.9014
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> rm(foo, out)
++> ### end Richard Telford test
++>
++>
++> ### nestednodf: test case by Daniel Spitale in a comment to News on
++> ### the release of vegan 1.17-6 in vegan.r-forge.r-project.org.
++> x <- c(1,0,1,1,1,1,1,1,0,0,0,1,1,1,0,1,1,0,0,0,1,1,0,0,0)
++> m1 <- matrix(x, nrow=5, ncol=5, byrow=FALSE)# as in Fig 2 Almeida-Neto et
al 2008.
++> (nodf1 <- nestednodf(m1, order = FALSE, weighted = FALSE))
++N columns : 53.33333
++N rows : 63.33333
++NODF : 58.33333
++Matrix fill: 0.56
++> ## Now the same matrix but with abundance data
++> x <- c(5,0,2,1,1,4,1,1,0,0,0,7,1,1,0,3,1,0,0,0,9,1,0,0,0)
++> m <- matrix(x, nrow=5, ncol=5, byrow=FALSE)
++> (nodfq <- nestednodf(m, order = FALSE, weighted = FALSE))
++N columns : 53.33333
++N rows : 63.33333
++NODF : 58.33333
++Matrix fill: 0.56
++> identical(nodf1, nodfq)
++[1] TRUE
++> rm(x, m, m1, nodfq, nodf1)
++> ### end nestednodf
++>
++> ### envfit & plot.envfit: latter failed if na.action resulted in only
++> ### observation with a given factor level was removed. plot.envfit would
++> ### fail with error about too long subscript
++> ### fixed case where data presented to envfit also has extraneous levels
++> data(dune)
++> data(dune.env)
++> ## add a new level to one of the factors
++> levels(dune.env$Management) <- c(levels(dune.env$Management), "foo")
++> ## fit nMDS and envfit
++> set.seed(1)
++> mod <- metaMDS(dune)
++Run 0 stress 0.1192678
++Run 1 stress 0.1886532
++Run 2 stress 0.1192678
++... New best solution
++... Procrustes: rmse 4.497004e-05 max resid 0.0001297349
++... Similar to previous best
++Run 3 stress 0.1192678
++... New best solution
++... Procrustes: rmse 1.801542e-05 max resid 5.078152e-05
++... Similar to previous best
++Run 4 stress 0.1183186
++... New best solution
++... Procrustes: rmse 0.02025988 max resid 0.06488321
++Run 5 stress 0.1192685
++Run 6 stress 0.1939202
++Run 7 stress 0.1808919
++Run 8 stress 0.1183186
++... Procrustes: rmse 1.538285e-05 max resid 4.789705e-05
++... Similar to previous best
++Run 9 stress 0.1192679
++Run 10 stress 0.1183186
++... New best solution
++... Procrustes: rmse 1.244871e-05 max resid 3.745684e-05
++... Similar to previous best
++Run 11 stress 0.1192686
++Run 12 stress 0.1192683
++Run 13 stress 0.1886532
++Run 14 stress 0.1183186
++... Procrustes: rmse 2.260742e-06 max resid 5.293932e-06
++... Similar to previous best
++Run 15 stress 0.1192678
++Run 16 stress 0.1183186
++... Procrustes: rmse 3.225938e-06 max resid 7.314283e-06
++... Similar to previous best
++Run 17 stress 0.2075713
++Run 18 stress 0.1192678
++Run 19 stress 0.1192685
++Run 20 stress 0.1183186
++... Procrustes: rmse 2.295609e-05 max resid 6.384852e-05
++... Similar to previous best
++*** Solution reached
++> ef <- envfit(mod, dune.env, permutations = 99)
++> plot(mod)
++> plot(ef, p.max = 0.1)
++> rm(mod, ef)
++> ### end envfit & plot.envfit
++>
++> ### protest (& Procrustes analysis): Stability of the permutations and
++> ### other results.
++> data(mite)
++> mod <- rda(mite)
++> x <- scores(mod, display = "si", choices=1:6)
++> set.seed(4711)
++> xp <- x[sample(nrow(x)),]
++> pro <- protest(x, xp, permutations = how(nperm = 99))
++> pro
++
++Call:
++protest(X = x, Y = xp, permutations = how(nperm = 99))
++
++Procrustes Sum of Squares (m12 squared): 0.9147
++Correlation in a symmetric Procrustes rotation: 0.292
++Significance: 0.08
++
++Permutation: free
++Number of permutations: 99
++
++> pro$t
++ [1] 0.2056649 0.2008622 0.2062525 0.2656158 0.2739148 0.1990895 0.3124461
++ [8] 0.2287779 0.2033140 0.2396172 0.1868019 0.2736072 0.1830199 0.2026019
++[15] 0.2402570 0.2286169 0.2791609 0.2214468 0.2356083 0.2188325 0.2097262
++[22] 0.2596154 0.2560289 0.2190367 0.2174689 0.2438396 0.1991101 0.2374612
++[29] 0.2030745 0.2205115 0.2566758 0.1448623 0.2354268 0.2419134 0.3144177
++[36] 0.2368505 0.2442661 0.2272011 0.3184241 0.2200089 0.2598430 0.2301587
++[43] 0.2763337 0.2332606 0.1935269 0.2315727 0.2573623 0.2021127 0.2387335
++[50] 0.2419087 0.2213094 0.2231619 0.2577845 0.2828219 0.2554605 0.2830915
++[57] 0.2342396 0.2137396 0.2151201 0.2432896 0.1956238 0.2055716 0.2329736
++[64] 0.2086125 0.2039124 0.1955711 0.2038045 0.2358886 0.2579199 0.1808128
++[71] 0.2155189 0.2967100 0.2480105 0.2010537 0.2718478 0.2182713 0.2471893
++[78] 0.2310223 0.2007771 0.2902640 0.2392380 0.2551756 0.3476679 0.2632614
++[85] 0.2289929 0.2222382 0.2503562 0.1983700 0.2084773 0.2406556 0.2478302
++[92] 0.2136439 0.3251671 0.2048928 0.3098776 0.2501555 0.2677039 0.2427125
++[99] 0.2693285
++> rm(x, xp, pro)
++> ### end protest
++>
++> ### Check that functions related to predict.rda work correctly for all
++> ### constrained ordination methods.
++>
++> ### simulate.rda/cca/capscale: based on predict.* and the following
++> ### should get back the data
++> data(dune, dune.env)
++> ind <- seq_len(nrow(dune))
++> target <- as.matrix(dune)
++> ## rda
++> mod <- rda(dune ~ Condition(Moisture) + Management + A1, dune.env)
++> dat <- simulate(mod, indx = ind)
++> all.equal(dat, target, check.attributes = FALSE)
++[1] TRUE
++> ## cca
++> mod <- cca(dune ~ Condition(Moisture) + Management + A1, dune.env)
++> dat <- simulate(mod, indx = ind)
++> all.equal(dat, target, check.attributes = FALSE)
++[1] TRUE
++> ## capscale: Euclidean distances -- non-Euclidean distances have an
++> ## imaginary component and will not give back the data.
++> d <- dist(dune)
++> mod <- capscale(d ~ Condition(Moisture) + Management + A1, dune.env)
++> dat <- simulate(mod, indx = ind)
++> all.equal(dat, d, check.attributes = FALSE)
++[1] TRUE
++> ## clean up
++> rm(ind, target, mod, dat, d)
++> ### end simulate.*
++>
++> ### test metaMDS works with long expression for comm
++> ### originally reported to GLS by Richard Telford
++> data(varespec)
++> set.seed(1)
++> mod <- metaMDS(subset(varespec, select = colSums(varespec) > 0, subset =
rowSums(varespec) > 0))
++Square root transformation
++Wisconsin double standardization
++Run 0 stress 0.1843196
++Run 1 stress 0.2455912
++Run 2 stress 0.2169407
++Run 3 stress 0.2313231
++Run 4 stress 0.1974406
++Run 5 stress 0.1858402
++Run 6 stress 0.1948414
++Run 7 stress 0.2265717
++Run 8 stress 0.222507
++Run 9 stress 0.2023215
++Run 10 stress 0.2673177
++Run 11 stress 0.1976151
++Run 12 stress 0.1852397
++Run 13 stress 0.2341085
++Run 14 stress 0.1955867
++Run 15 stress 0.2137409
++Run 16 stress 0.2109638
++Run 17 stress 0.1825658
++... New best solution
++... Procrustes: rmse 0.04169825 max resid 0.1521436
++Run 18 stress 0.1843197
++Run 19 stress 0.2570119
++Run 20 stress 0.3760596
++*** No convergence -- monoMDS stopping criteria:
++ 20: stress ratio > sratmax
++> mod
++
++Call:
++metaMDS(comm = subset(varespec, select = colSums(varespec) > 0, subset =
rowSums(varespec) > 0))
++
++global Multidimensional Scaling using monoMDS
++
++Data: wisconsin(sqrt(subset(varespec, select = colSums(varespec) > 0,
subset = rowSums(varespec) > 0)))
++Distance: bray
++
++Dimensions: 2
++Stress: 0.1825658
++Stress type 1, weak ties
++No convergent solutions - best solution after 20 tries
++Scaling: centring, PC rotation, halfchange scaling
++Species: expanded scores based on 'wisconsin(sqrt(subset(varespec, select =
colSums(varespec) > 0, subset = rowSums(varespec) > 0)))'
++
++> rm(mod)
++> ### The above should run without error & last lines tests changes to the
++> ### printed output
++>
++> ## dbrda tests
++>
++> ## the following three should be all equal
++> data(varespec, varechem)
++> (mr <- rda(varespec ~ Al + P + Condition(pH), varechem))
++Call: rda(formula = varespec ~ Al + P + Condition(pH), data = varechem)
++
++ Inertia Proportion Rank
++Total 1825.6594 1.0000
++Conditional 234.0961 0.1282 1
++Constrained 424.4375 0.2325 2
++Unconstrained 1167.1258 0.6393 20
++Inertia is variance
++
++Eigenvalues for constrained axes:
++ RDA1 RDA2
++314.60 109.84
++
++Eigenvalues for unconstrained axes:
++ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
++692.3 171.3 106.1 59.9 35.1 30.1 22.4 16.8
++(Showed only 8 of all 20 unconstrained eigenvalues)
++
++> (md <- dbrda(varespec ~ Al + P + Condition(pH), varechem))
++Call: dbrda(formula = varespec ~ Al + P + Condition(pH), data =
++varechem)
++
++ Inertia Proportion Rank
++Total 1825.6594 1.0000
++Conditional 234.0961 0.1282 1
++Constrained 424.4375 0.2325 2
++Unconstrained 1167.1258 0.6393 20
++Inertia is mean squared Euclidean distance
++
++Eigenvalues for constrained axes:
++dbRDA1 dbRDA2
++314.60 109.84
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++692.3 171.3 106.1 59.9 35.1 30.1 22.4 16.8
++(Showed only 8 of all 20 unconstrained eigenvalues)
++
++> (mc <- capscale(varespec ~ Al + P + Condition(pH), varechem))
++Call: capscale(formula = varespec ~ Al + P + Condition(pH), data =
++varechem)
++
++ Inertia Proportion Rank
++Total 1825.6594 1.0000
++Conditional 234.0961 0.1282 1
++Constrained 424.4375 0.2325 2
++Unconstrained 1167.1258 0.6393 20
++Inertia is mean squared Euclidean distance
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2
++314.60 109.84
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++692.3 171.3 106.1 59.9 35.1 30.1 22.4 16.8
++(Showed only 8 of all 20 unconstrained eigenvalues)
++
++> ## the following two should be zero (within 1e-15)
++> p <- shuffleSet(nrow(varespec), 999)
++> range(permustats(anova(mr, permutations=p))$permutations -
+++ permustats(anova(md, permutations=p))$permutations)
++[1] -1.776357e-15 3.996803e-15
++> range(permustats(anova(mr, permutations=p))$permutations -
+++ permustats(anova(mc, permutations=p))$permutations)
++[1] -1.776357e-15 4.884981e-15
++> ## the following two should be equal
++> d <- vegdist(wisconsin(sqrt(varespec)))
++> (md <- dbrda(d ~ Al + P + Condition(pH), varechem))
++Call: dbrda(formula = d ~ Al + P + Condition(pH), data = varechem)
++
++ Inertia Proportion Rank RealDims
++Total 2.54753 1.00000
++Conditional 0.18911 0.07423 1
++Constrained 0.39839 0.15638 2 2
++Unconstrained 1.96003 0.76939 20 17
++Inertia is squared Bray distance
++
++Eigenvalues for constrained axes:
++ dbRDA1 dbRDA2
++0.29646 0.10193
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++0.4503 0.3031 0.2389 0.1677 0.1495 0.1444 0.1339 0.1112
++(Showed only 8 of all 20 unconstrained eigenvalues)
++
++> (mc <- capscale(d ~ Al + P + Condition(pH), varechem))
++Call: capscale(formula = d ~ Al + P + Condition(pH), data = varechem)
++
++ Inertia Proportion Eigenvals Rank
++Total 2.54753 1.00000 2.59500
++Conditional 0.18911 0.07423 0.19141 1
++Constrained 0.39839 0.15638 0.39920 2
++Unconstrained 1.96003 0.76939 2.00439 19
++Imaginary -0.04747 4
++Inertia is squared Bray distance
++
++Eigenvalues for constrained axes:
++ CAP1 CAP2
++0.29718 0.10201
++
++Eigenvalues for unconstrained axes:
++ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
++0.4505 0.3032 0.2394 0.1677 0.1504 0.1450 0.1341 0.1113
++(Showed only 8 of all 19 unconstrained eigenvalues)
++
++> (amd <- anova(md, permutations = p))
++Permutation test for dbrda under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: dbrda(formula = d ~ Al + P + Condition(pH), data = varechem)
++ Df SumOfSqs F Pr(>F)
++Model 2 0.39839 2.0325 0.002 **
++Residual 20 1.96003
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> (amc <- anova(mc, permutations = p))
++Permutation test for capscale under reduced model
++Permutation: free
++Number of permutations: 999
++
++Model: capscale(formula = d ~ Al + P + Condition(pH), data = varechem)
++ Df SumOfSqs F Pr(>F)
++Model 2 0.39839 2.0325 0.002 **
++Residual 20 1.96003
++---
++Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
++> ## should be zero (within 1e-15)
++> range(permustats(amd)$permutations - permustats(amc)$permutations)
++[1] -8.881784e-16 6.661338e-16
++> rm(mr, md, mc, d, amd, amc)
++>
++> proc.time()
++ user system elapsed
++ 9.516 0.056 9.566
--
2.7.4