Hey guys & girls, I found a script on the net that uses leasqr to fit experimental data to a function and adapted it to my needs. Works like a charm =)
I do still have some trouble with understanding/using the weighing properly and hoped maybe someone could help me out. Here is my situation: I have experimental x-y data, the process is roughly an exponential decay. As always with real life data, its noisy and especially in the tail of the exponential, the noise is a problem. I know the absolute size of noise, let's call this number "noize". What I am used to doing in experimental data fitting is to take the weighing factor for each y-point as the inverse of its relative error, i.e. noize/yvalue. But the "wt" used in leasqr has to be an N*N-matrix, where N is the number of y-values ... I am a bit confused as to how I would construct the proper wt-matrix for the problem at hand. Help would be much appreciated, thanks in advance, Timo ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://p.sf.net/sfu/appdyn_sfd2d_oct _______________________________________________ Octave-dev mailing list Octave-dev@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/octave-dev
