Scilab is a great and powerful mathematical tool that can be used for fitting correlations to measured data.
For me, the help functions and literature write-ups for least-squares fitting in Scilab were tedious and I found them difficult to follow. At an elementary level, I have sorted out my problems. I would like to report on it, but I am grateful for any further suggestions. First: linear least-squares fitting: with measurement data x=[2 7 12]'; y=[2 4.5 6.5]'; it is easy, to fit a straight line and plot it: M=[ones(x) x]; a=M\y; plot2d(x,M*a); plot(x,y,'r.'); Strangely, you find the recipe under "backslash" and this is not very straightforward. The method is really neat, because you can easily fit a polynomial of 25th order or any correlation that is linear in the parameters. Correlations not linear in the parameters need non-linear least-squares fitting, e.g. with "leastsq", where the Scilab help function is terribly complex. In my case, I wanted to simultaneously fit three straight lines through three measurement series with the condition that all 3 lines start at the same point on the x-axis. So the following worked well for me: x=[2 7 12]'; y=[2 4.5 6.5]'; y1=[1 2 3]'; y2=[0 1 1.4]'; par0 = [.5 .15 .09 -5]; function e=err(par, x,y,y1,y2) e= [y-par(1)*(x+par(4)); y1-par(2)*(x+par(4)); y2-par(3)*(x+par(4))] endfunction [f,paropt] = leastsq ( list(err,x,y,y1,y2), par0); plot(x,[y y1 y2],'.'); xx=-5:15; plot(xx,paropt(1)*(xx+paropt(4)),'b--'); plot(xx,paropt(2)*(xx+paropt(4)),'g--'); plot(xx,paropt(3)*(xx+paropt(4)),'r--'); I guess there are more elegant ways to write this code, are they? I find write-ups of leastsq in the help function and in Scilab books terribly messy.... Greetings Heinz PS: Why do I need "list" in the leastsq function call? _______________________________________________ users mailing list users@lists.scilab.org http://lists.scilab.org/mailman/listinfo/users
