antje-4 wrote:
>
> 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.
>
>
If you really want to fit a single gaussian distribution, there are probably
better ways, for example by using the a QQ plot. For some nls fits, starting
with a slight-off value (e.g. 0.001 instead of 0) might help, but I doubt
this could be a problem with a simple Gaussian distribution.
I many cases, using the log of a parameter you want to force to be >0 works
well; this is the standard method in some fields, e.g. pharmacodynamics.
As a nice tool to learn about nls, try package nlstools.
Dieter
--
View this message in context:
http://www.nabble.com/nls---find-good-starting-values-tp24474843p24475260.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
