[R] Warning with mle

Antje Niederlein niederlein-rstat at yahoo.de
Fri Jan 28 09:50:43 CET 2011

Hi Ben,

thanks a lot for your answer.

>  There are four reasonable solutions to your problems:
> 1. ignore the warnings, as long as they are all of the
> same type (NaNs/NAs being produced by dbinom or dpois),
> and as long as the final results look sensible.

probably fine for me. The fit for my dummy data was nice, but for the
real data it's not so nice... (see below - other ideas...)

> 2. use method="L-BFGS-B" and set lower and upper bounds
> on your parameters (this can be a little bit finicky because
> L-BFGS-B will often try parameters *on* the boundary, and
> it can't handle NAs or infinities, so you may have to set
> the lower and upper bounds a little bit in from their theoretical
> limits (e.g. 0.002 instead of 0).

I tried it but even if I use the following statement, I get the
warnings with confint()

fit <- mle(ll, method = "L-BFGS-B", lower = c(0.001,0), upper = c(Inf,1))

I added the output of the current parameters for my ll-function and
obviously the second parameter goes far beyond the limit. Is there
anything wrong with how I tried to set the limits?
Is it possible that mle() takes the boundaries into account but
confint() does not?

> 3. Fit your parameters on the transformed scale (typically logit
> for probabilities, log for Poisson intensities).  This will cause
> problems if the parameter really lies on the boundary, e.g.
> if the best estimate of your zero-inflation parameter is zero
> or very close to it.

Not my prefered solution (I'm too new to this area and afraid to do
anything wrong)

> 4. Use the pscl package, which has reasonably robust and
> efficient built-in functions for fitting zero-inflated (and
> hurdle) models.

I played around with it but I cannot find a way how to simply estimate
the parameters for my distribution. My data is simply discrete
histogram data (counts) and I'm probably too stupid to put it into a
model... If you can give me any hint - I would be happy.

Other ideas concerning my approach: Do I use the right criteria to
minimize on (so far I use the sum of squared errors). May it make
sense to use the pearsons chi-squared test? (Is there any easy way to
do it in R?)



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