fitting mixed effects logistic regression with weights
Dear Ben, Let me state my questions if I could get more definitive answers on these problems that I have been encountering recently: 1. is it currently possible to fit mixed effects logit model for survey sampled data in R as in Proc GLIMMIX? 2. Does MCMCglmm support inclusion of sampling weights for binomial data? The problem with lmer is that the responses become non integer with the weights and it does not seem to like it. 3. How do we specify the parametric bootstrap for my model given below? I am thinking that this will provide an alternative SE/CI estimates to the REML/RSPL. For my large data (n>800K) this may not be useful, actually. 4. How do we account for the sampling weights in the computation of the HPD intervals? Thank you!
From: Ben Bolker [bbolker at gmail.com]
Sent: Friday, June 10, 2011 4:45 PM
To: Gebregziabher, Mulugeta
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] fitting mixed effects logistic regression with weights
Sent: Friday, June 10, 2011 4:45 PM
To: Gebregziabher, Mulugeta
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] fitting mixed effects logistic regression with weights
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On 06/10/2011 04:44 PM, Gebregziabher, Mulugeta wrote:
> Dear Ben,
>
> Thank you for your prompt response. Does MCMCglmm provide the usual
> REML/RSPL estimates or Bayesian estimates for the fixed effects? I
> mean are the coefficient and SE estimates REML/RSPL or Bayesian? I
> want to be sure what I am getting.
Bayesian.
>
> Mulugeta ________________________________________ From:
> r-sig-mixed-models-bounces at r-project.org
> [r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ben Bolker
> [bbolker at gmail.com] Sent: Friday, June 10, 2011 4:33 PM To:
> r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] fitting
> mixed effects logistic regression with weights
>
> On 06/10/2011 04:24 PM, Gebregziabher, Mulugeta wrote:
>> Dear all,
>>
>> I have been trying to fit a logistic regression model with random
>> intercept for survey sampled data using the lmer in the lme4
>> package. I also want to get HPD intervals. I tried family=binomial
>> as well as family=quasibinomial. I am encountering the following
>> errors. "Eror in .local(object, n, verbose, ...) : Update not yet
>> written" and "Error in HPD interval(mcmcsamp(fit, 1000)) : error
>> in evaluating the argument 'object' in selecting a method for
>> function 'HPDinterval'.
>
> As the error message suggests (obliquely!), mcmcsamp() does not yet
> work for GLMMs (i.e. [g]lmer with 'family' specified). Furthermore,
> quasi-likelihood fitting no longer works with the latest releases of
> lme4 (because the author decided that he didn't really understand
> what it was doing, hence safer to omit it).
>
> Have you considered MCMCglmm, or parametric bootstrapping
> (help("simulate-mer")) ?
>
>
> See more below:
>>
>> Does anyone know how to go around this problem? Thanks.
>>
>>> library(lme4)
>> Loading required package: Matrix Loading required package: lattice
>> Attaching package: 'Matrix' The following object(s) are masked
>> from 'package:base': det Attaching package: 'lme4' The following
>> object(s) are masked from 'package:stats': AIC
>>> attach(cohort)
>>>
>>> f <- a1cge8 ~ (1|id) + time + nhb + hispanic + other +
>>> male + mstat +svcpct +urban + comor1 +comor2 + comor3
>>>
>>>
>>> est <- round(slot(summary(fit), "coefs")[,1], digits=2) t
>>> <- slot(summary(fit), "coefs")[,3] df <- c(1, 1, 3, 3, 3,
>>> 1, 1, 1, 1, 3, 3, 3) p.value <- ifelse(t<0, round(pt(t,
>>> df=df), digits=3), round(1-pt(t, df), digits=3) ) se <-
>>> round(slot(summary(fit), "coefs")[,2], digits=4) ci <-
>>> HPDinterval(mcmcsamp(fit, 1000))
>> Error in .local(object, n, verbose, ...) : Update not yet written
>> Error in HPDinterval(mcmcsamp(fit, 1000)) : error in evaluating
>> the argument 'object' in selecting a method for function
>> 'HPDinterval'
>>> lower <- round(ci$fixef[,1], digits=2)
>> Error: object 'ci' not found
>>> upper <- round(ci$fixef[,2], digits=2)
>> Error: object 'ci' not found
>> __________________________________________ Mulugeta Gebregziabher,
>> PhD Assistant Professor Division of Biostatistics and Epidemiology
>> 135 cannon St. Suite 303 Charleston, SC 29425 Tel: 843-876-1112;
>> Fax: 843-876-1126 E-mail: gebregz at musc.edu
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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