Re: Smooth line approximating minima of a data series

[Ben Abbott]

On Wednesday, February 24, 2010, at 03:04AM, "Matthias Brennwald" 
<address@hidden> wrote:
>Dear all
>
>Consider a series of data values that reflect a smooth function (e.g.  
>a low-degree polynomial), but there might be additional features in  
>the data (e.g. narrow peaks or noise). I'd like to fit a polynomial to  
>this data, whereby this polynomial reflects a smooth approximation of  
>the minima of the raw data (I call this the "base line"). The  
>following might help to illustrate what I'm trying to accomplish:
>
>     x = [-1:0.01:1]; % x-axis values
>     p = [-3 2 1 0]; yp = polyval (p,x); % make up a polynomial  
>reflecting the "base line" for illustration
>     y = yp + rand(size(x)); % this would be the raw data
>     plot (x,y,x,yp); legend ('raw data','base line') % plot the raw  
>data and the polynomial for illustration
>
>Has anyone an idea of how to accomplish this? Are there standard  
>methods? I'd appreciate any hints.
>
>Thanks
>Matthias
>

Have you considered using convhull() in combination with polyfit(), or 
interp1() ?

Ben