Hi;
Dang, Christophe wrote > In fact, you don't need a specific function as polynomial fitting is just > a multiple linear regression considering each x^n is a variable[..] I think it's not really so bad if we have such a function, is it? David Chèze wrote > you could use the polyfit function that is made available in the stixbox > module: it is equivalent to matlab's polyfit. > http://atoms.scilab.org/toolboxes/stixbox/2.2/ And, besides the polyfit() from Stixbox, there is another function with the same name in Fitters toolbox (I more prefer this one.) see: https://atoms.scilab.org/toolboxes/fitters Last year when I was writing a tutorial about polynomials handling (in Persian), I use the following example to show how one can use this function for curve fitting. x=[1 2 3 4 5]; y=[5.5 43.1 128 290.7 498.4]; exec('polyfit.sci',-1) p=polyfit(x,y,3) xp=-.5:.1:5.5; yp=horner(p,xp); plot(x,y,'o',xp,yp), set(gca(),'grid',[6 6]*color('blue')) So I suggest Scilab Team to add ployfit() as a core Scilab function. Mersi, Mehran _ -- View this message in context: http://mailinglists.scilab.org/polyfit-for-Scilab-tp4030877p4030898.html Sent from the Scilab users - Mailing Lists Archives mailing list archive at Nabble.com. _______________________________________________ users mailing list users@lists.scilab.org http://lists.scilab.org/mailman/listinfo/users
