On Wed, Oct 17, 2012 at 03:20:30PM +0200, Timo Bretten wrote: > 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.
Actually I wouldn't take the greater _relative_ variance as a reason to assign different weights. > 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 ? The choice of weights is outside the scope of this optimization algorithm. Other weights than suggested in the help of leasqr can be reasonable (but I don't see this in your case, as I said). Only the reasonable computation of some additional statistics (corp, covp, covr, Z) depends on the specification of weights as suggested in the help text. I consider nonlin_residmin (or nonlin_curvefit) as preferable to leasqr (while their default backend algorithm is identical to that of leasqr). They delegate the computation of statistics into an extra function (residmin_stat) and so do not mention any restriction on the weights in their help text. Olaf > Thanks once more, > Timo -- public key id EAFE0591, e.g. on x-hkp://pool.sks-keyservers.net
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