# [R] Nealder Meade and nlm

Roger Peng rpeng at stat.ucla.edu
Tue Oct 29 00:42:11 CET 2002

```If you need standard errors, you can estimate the hessian matrix by
setting `hessian = TRUE' in the optim() function.  Then, take the diagonal
of the inverse.

-roger
_______________________________
UCLA Department of Statistics
rpeng at stat.ucla.edu
http://www.stat.ucla.edu/~rpeng

On 28 Oct 2002, Matthew L. Fidler wrote:

> Hello,
>
> I have been using R to fit my data using non-linear least squares.  I
> have used the optimize routine to minimize the sum of squared errors
> (using Nealder Meade optimization routine), but couldn't get the
> non-linear model in R to converge to the estimates acheived in a
> gradient.  I was wondering if there is any way to estimate the
> covariance matrix for the parameters when using the Nealder Meade
> optimization method.  If so is there a book, or article, that I can
> refer to about how this is done, it would be greatly appreciated.
>
> Thanks
>
> Matthew L. Fidler
>
> fidler at math.utah.edu
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