[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).

[Ben Bolker]

   I'm pretty certain that the fitted() method exists in the current
version of the package.  If you try it (e.g.

library(glmmADMB)
example(glmm.admb)
fitted(om)

) and it doesn't work, could you please let me know?
We will work on the documentation.

  cheers
    Ben Bolker

On 11-01-14 05:05 PM, H. Skaug wrote:
> 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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>>
>>
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