Thanks Olaf, you are of course right. It was late yesterday and I didn't take enough time to actually think before asking.
Maybe I wasn't clear in the discription of my problem. The absolute error is the same for every data-point, i.e. I measure voltages something like 0.10 +/- 0.05 V, the +/-0.05 stays the same for each x-y pair (this is waht I called "noize" in my initial eMail). In the tail of the exponential, the voltages I measure are small, like 0.06 +/- 0.05 V and thus have a much greater relative error associated with them. In my fit, I want those low, tail voltage values to be weighted less strongly. Actuallly the weight w_i for data point y_i should be equal to (y_i / noize)^2, so can I just use wt=ones(size(y)).*(y/noize).^2 ? Thanks once more, Timo 2012/10/17 Olaf Till <i7t...@t-online.de>: > On Wed, Oct 17, 2012 at 12:23:12AM +0200, Timo Bretten wrote: >> That works indeed, cheers Nir! >> I was confused because by default, wt is set to ones(size(y)) ... also > > ones(size(y)) have the same dimensions as y > >> from the documentation - which kind of clashed with the initial >> statement, that wt should be of the same dimension as y ... > > so there should be no clash of these statements. > >> Anyways, thanks for the quick reply, I consider my problem fixed. > > But for the record, you do not need to specify weights if they are > constant. > > Olaf ------------------------------------------------------------------------------ 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
