Hi there,
it might be a very simple question and I'd be glad to even get a link to
some useful documentation...
I have several data sets, I'd like to fit to a gaussian distribution.
I've tried to give an estimate of the mean and the sd of this
distribution but still, I run into problems if these estimates are not
close enough.
For example, nls() breaks with this message:
singular gradient matrix at initial parameter estimates
I don't know how to avoid these bad start values because their estimate
is automated. Better start values are often quite close.
I was wondering whether there is any way to test several start-values as
long as nls does not succeed.
I would do it with a while construct but maybe there is another approach?
Any hint is very welcome!
Ciao,
Antje
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