[R] Problem on glmer

[Zhang,Yanwei]

Hglm does the work! Thanks!

Also, I find that the developing version of lme4, called lme4a, has the capability to fit Gamma models. And both lme4a and hglm produce results consistent with the published ones.  

Problems solved! 


Wayne (Yanwei) Zhang
Statistical Research 
>CNA

-----Original Message-----
From: Dennis Murphy [mailto:djmuser at gmail.com] 
Sent: Tuesday, May 17, 2011 12:46 PM
To: Zhang,Yanwei
Cc: r-help at r-project.org
Subject: Re: [R] Problem on glmer

Hi:

This might be helpful:

http://glmm.wikidot.com/faq

It has some commentary and potential workarounds re gamma GLMMs toward
the bottom of the page. I might also suggest that r-sig-mixed-models
might be a better forum for these types of questions; you can
subscribe by going to

http://www.r-project.org/mail.html

and scrolling down a bit to where the special interest groups are
listed. Same process as subscribing to R-help.

Dennis


On Tue, May 17, 2011 at 7:17 AM, Zhang,Yanwei <Yanwei.Zhang at cna.com> wrote:
> Hi Dennis,
>
> Thanks for your input. I am quite shocked that the Gamma model is not implemented in glmer yet, but do you happen to know if there is other packages in R that could fit Gamma hierarchical models besides MASS? The glmmPQL results are a bit off compared to what have been published.  Thanks.
>
>
>
> Wayne (Yanwei) Zhang
> Statistical Research
>>CNA
>
> -----Original Message-----
> From: Dennis Murphy [mailto:djmuser at gmail.com]
> Sent: Tuesday, May 17, 2011 3:05 AM
> To: Zhang,Yanwei
> Cc: r-help at r-project.org
> Subject: Re: [R] Problem on glmer
>
> Hi:
>
> I believe that the problem with the glmer() call is that you are
> invoking the Gamma family, which appears not to be implemented at
> present. See
>
> http://r-project.markmail.org/search/?q=r-sig-mixed-models#query:r-sig-mixed-models%20date%3A201105%20+page:8+mid:q2b2uoupejdzmvtl+state:results
>
> Dennis
>
> On Mon, May 16, 2011 at 3:31 PM, Zhang,Yanwei <Yanwei.Zhang at cna.com> wrote:
>> Hi all,
>>
>> I was trying to fit a Gamma hierarchical model using "glmer", but got weird error message that I could not understand. On the other hand, a similar call to the glmmPQL leads to results that are close to what I expect. I also tried to change tha "nAGQ" argument in "glmer", but it did not solve the problem. The model I was fitting has a simple structure - one hierarchy and one offset, and I did not expect "glmer" to fail on this simple data set. Did I specify something wrong here? I appreciate any help. Thanks in advance.
>>
>> #############################################################
>> ## Code to fit the model
>> #############################################################
>> library(MASS)
>> library(lme4)
>> address <- "http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Longitudinal%20and%20Panel%20Data/Book/Data/TXTData/WorkerC.txt"
>> wc <- read.table(address,header=T)
>> # clean data
>> cl <- unique(wc$CL[wc$LOSS==0])
>> wc2 <- subset(wc, !(CL %in% cl) )
>> # fit model
>> fit1 <- glmmPQL(LOSS~ YR+offset(log(PR)), random=~1|CL,family=Gamma(link="log"),
>>          data=wc2)
>> fit2 <- glmer(LOSS~ (1|CL) + YR+offset(log(PR)), family=Gamma(link="log"),
>>          data=wc2)
>>
>>
>> #############################################################
>> ## Results of fit1 and fit2
>> #############################################################
>>
>>> fit1
>> Linear mixed-effects model fit by maximum likelihood
>>  Data: wc2
>>  Log-likelihood: NA
>>  Fixed: LOSS ~ YR + offset(log(PR))
>>  (Intercept)            YR
>> -4.2830507147  0.0005085944
>>
>> Random effects:
>>  Formula: ~1 | CL
>>        (Intercept)  Residual
>> StdDev:   0.8313193 0.5346455
>>
>> Variance function:
>>  Structure: fixed weights
>>  Formula: ~invwt
>> Number of Observations: 700
>> Number of Groups: 100
>>> fit2
>> Error in asMethod(object) : matrix is not symmetric [1,2]
>>
>>
>>
>>
>> Wayne (Yanwei) Zhang
>> Statistical Research
>> CNA
>> Email: Yanwei.Zhang at cna.com<mailto:Yanwei.Zhang at cna.com>
>>
>>
>>
>>
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