From: Roel de Jong <dejongroel_at_gmail.com>

Date: Sat 12 Nov 2005 - 06:39:32 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sat Nov 12 06:59:32 2005

Date: Sat 12 Nov 2005 - 06:39:32 EST

I recently took Dave Fournier up on his offer to evaluate his AD Model
builder package (http://otter-rsch.com/admodel.html) when fitting a GLMM
under the binomial probit link.

I conducted a simulation study in which I drawed 500 samples each containing 1500 observations from the following model specification:

y = (intercept*f1+pred2*f2+pred3*f3)+(intercept*ri+pred2*rs)

where pred2 and pred3 are predictors distributed N(0,1) f1..f3 are fixed effects, f1=-1, f2=1.5, f3=0.5 ri is random intercept with associated variance var_ri=0.2 rs is random slope with associated variance var_rs=0.4 the covariance between ri and rs=0

we have 50 level 2 units, so 30 observations/level 2 unit

I then proceeded with the analysis of the 500 samples with the AD Model builder package. To check for bias, I calculated the average of the parameter estimates of the 500 samples and compared them to the true population parameters. There was virtually no bias:

parameter average parameter estimate true value

f1 -1.001 -1.000 f2 1.510 1.500 f3 0.499 0.500 var_ri 0.197 0.200 var_rs 0.396 0.400

Then I checked the coverage with alpha=0.95, where asymmetrical confidence intervals were calculated for the variance components:

parameter coverage (alpha=0.95)

f1 .928 f2 .948 f3 .956 var_ri .960 var_rs .984

The coverages are quite good, only the variance of the random slope is high, which suggests that the associated standard error is too large.

Where AD model builder really shines is the fact that convergence was reached without problems in all 500 samples, where R alternatives like lmer and glmmPQL, which use Penalized Quasi Likelihood, tend to run in computational problems. I therefore highly recommend the software for analyzing binomial mixed models, and I encourage Dave to add it to his existing negative binomial package for R.

Regards,

Roel de Jong

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sat Nov 12 06:59:32 2005

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