[R-sig-ME] HPD intervals for fixed parameters in model

[Ben Bolker]

On 12-06-04 03:03 AM, John.Morrongiello at csiro.au wrote:
> Hi Ben
> 
> Thanks very much for the response. I've tried both MCMCglmm and glmmADMB and am happy with the parameter estimates and the HPD intervals they produce. The only issue was specifying the random age slope in glmmADMB (as per M1admb below). It seems to only accept random factors- is it possible to specify a random slope? 
> 
> M1admb<-glmmadmb(log(Increment)~log(Age)+temp+(log(Age)|FishID)+(1|fYear),data,family='gaussian')
> 
> Warning message:
> In Ops.factor(log(Age), FishID) : | not meaningful for factors
> Error in model.matrix(as.formula(paste("~", lbit)), mdata) : 
>   error in evaluating the argument 'object' in selecting a method for function 'model.matrix': Error in parse(text = x) : <text>:2:0: unexpected end of input
> 1: ~ 
>   ^
> 
> Cheers
> 
> John

  As a quick possible workaround, try defining data$logAge <-
log(data$Age) and using (logAge|FishID) in your model formula; I'm not
sure that glmmADMB can handle defining variable transformations on the
fly in that context.

  Also be aware that the default for lme4 is to use the full correlation
matrix while glmmADMB uses a diagonal matrix, so (e.g.) (logAge|FishID)
would estimate three parameters (variation in intercept across fish,
variation in slope across fish, covariance between intercept and slope)
while glmmADMB would only estimate the first two.  (I think you have to
specify this choice explicitly in MCMCglmm.)

> 
> 
> Message: 4
> Date: Fri, 1 Jun 2012 16:44:51 +0000 (UTC)
> From: Ben Bolker <bbolker at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] HPD intervals for fixed parameters in model
> 	with	crossed and correlated random effects
> Message-ID: <loom.20120601T183904-68 at post.gmane.org>
> Content-Type: text/plain; charset=us-ascii
> 
>  <John.Morrongiello at ...> writes:
> 
> 
>> I'd very much like to calculate some HPD intervals for the fixed
>> parameters in a lmer model containing crossed and correlated random
>> effects. However, I have not been able to find a solution for this
>> type of model after a pretty thorough search of discussion threats
>> or worked examples. Is this possible at the moment with lme4
>> (function or work around), or do I have to change packages and try
>> something like mcmcglmm?
> 
>   I think if you want to compute HPD intervals you are more or less
> stuck with MCMCglmm or glmmADMB ....
>  
>> My data contains 1917 growth measurements (Increment) from 704 fish
>> (FishID) and the data is sampled from 29 years (Year). I'm
>> interested in the effects of age (2-18 years) and water temperature
>> (temp) on growth.  My model is:
>  
>> M1<-lmer(log(Increment)~log(Age)+temp+(log(Age)|FishID)+(1|Year),
>>   data,REML=T)
>>
>> HPDinterval requires an mcmc object, but when I try
>> mcmcsamp(M1, n=1000)
>>
>> I receive the following error:
>> Error in .local(object, n, verbose, ...) :
>>   Code for non-trivial theta_T not yet written
> 
> 
>> I understand this is because MCMCsamp doesn't yet cope with
>> correlated random effects? On the glmm wikidot page there is code to
>> generate confidence and prediction intervals on predictions- could I
>> use some of N this to get a what I'm after (if so, which bits)? I
>> haven't used mcmcglmm before, so ideally would like to stay with the
>> more familiar lme4 if possible to expedite the analyses! 
> 
>    I don't think so, I think you'll need MCMCglmm or glmmADMB.
> 
> In principle glmmADMB should work with your model more or less as-is
> (although I don't remember whether it can do REML-y stuff or not),
> and you can use mcmc=TRUE to get it to run an MCMC chain on the
> parameters for you.  However, based on my limited experience, if
> you do want to get MCMC working it will be easier using MCMCglmm
> (which is intrinsically based on MCMC, and does a bunch of clever
> stuff to make the chains mix well).
> 
>   good luck,
>     Ben Bolker
>