[ADMB Users] glmm.admb-R. Desired function, a call for calculation of 'lambda' to be used in model validation as in (fitted(Model)) vs. observed(data).

Schweizer, Peter E. schweizerpe at ornl.gov
Thu Jan 13 06:34:03 PST 2011

Dear colleagues,

We find the glmm.admb a very interesting tool for ecological modeling. Unfortunately, the at this time still rather sparse documentation of its R-application make for somehow progress in our analysis.

A few days ago we posted a question regarding the calculation and interpretation of 'residuals(Model)' in glmm.admb-R, and Hans Julius Skaug (many thanks Hans) responded. Please see below our initial question;

> > We are using glmmADMB in R to model land cover and water

> > quality influence on species diversity of fishes within a study area with

> > several subregions.

> >

> > We defined subregion as a random factor and also ask for individual

> > intercepts for the different subregions.

> >

> > A 'global' model for overdispersed count data was formulated as

> >

> > GM<-glmm.admb( N_Species ~ b1 + b2 + b3 + ...+ bn +

> Subregion, random = ~ 1,

> > group="Subregion", data=input, family="nbinom")

> >

> > We subsequently evaluated several candidate models that

> represent various

> > subsets of variables from the global model.

> >

> > Our input file is A1, with A1$NO representing the observed number of

> > species. During the process of examining model performance we used

> >

> > Observed - Predicted   (A1$NO -(fitted(best)))  for the

> 'best' model based

> > on lowest AICc to derive residuals for predicted Nspecies.

> However, using

> > 'residuals(best)' produced considerable different (smaller)

> values which we

> > find somehow puzzling. Are we wrong to assume that

> (Nspecies predicted by

> > 'best' model,  + residuals(best)) should add up to Nspecies observed

> > (A1$NO)?

> ----------------------------------------------------------------------------------------------------------
Hans Julius Skaug kindly provided the following answer;
> I think residuals(best) returns


> [A1$NO -(fitted(best)] / SD


> where SD is the standard deviation, which depends on the distribution

> at hand. The code inside glmm.admb that determines SD is:


>    tmpsd <- switch(family, poisson = sqrt(lambda), nbinom =

> sqrt(lambda *

>         (1 + lambda/out$alpha)), binomial = sqrt(out$fitted *

>         (1 - out$fitted)))
> ---------------------------------------------------------------------------------------
Now, since model validation for any application is an essential component of the modeling process, we are asking the ADMB community: would be possible to modify the glmm.admb R-package in the near future so that lambda can be provided in the output?

Ideally, a desired function to be developed would be a call that provides fitted(model) vs. 'Observed(model)' [=measured data from data input file, something akin to Predicted vs.Observed].

Also, at current glmm.admb-R output provides alpha as a measure of dispersion of the negative binomial distribution but without a stated lambda value, derivation of SD to calculate a P/O fit is still a challenge to be solved.

I'm sure that other colleagues in ecological research would appreciate such contribution too ...

Comments and suggestions are welcome, and thank you for your time.



-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.admb-project.org/pipermail/users/attachments/20110113/4b553e33/attachment.html>

More information about the Users mailing list