[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).

[H. Skaug]

Peter,

Thanks for your feedback.

Fitted values are avaliable as "model$fitted".

The package will be (slowly) improved. We ill make sure too include a
fitted() function.

Hans


On Thu, Jan 13, 2011 at 3:34 PM, Schweizer, Peter E.
<schweizerpe at ornl.gov> wrote:
> 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.
>
>
>
> Cheers,
>
>
>
> Peter
>
>
>
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