[R-sig-ME] Prediction variance for GLMM
D. Rizopoulos
d@rizopoulo@ @ending from er@@mu@mc@nl
Sat Jan 5 23:54:23 CET 2019
It is not clear what type of predictions you want to calculate. In GLMMs, and because of the nonlinear link function you have three types of predictions:
(1) Using only the fixed effects, i.e., setting the random effects to zero. These are predictions for the average subject.
(2) Population predictions, i.e., predictions averaged over the subjects. Note that these are not the same as the predictions in (1).
(3) Subject-specific predictions, which are calculated conditionally on the random effects, either for existing or new subjects. In this case, you can also calculate dynamic predictions that are updated as new measurements are recorded for the subject.
For all type of predictions you could calculate standard errors.
Using the GLMMadaptive package (https://drizopoulos.github.io/GLMMadaptive/), you can get both the predictions and the standard errors using the predict() method. For examples, check the vignettes:
https://drizopoulos.github.io/GLMMadaptive/articles/Methods_MixMod.html#predictions
and
https://drizopoulos.github.io/GLMMadaptive/articles/Dynamic_Predictions.html
Best,
Dimitris
- - - - - -
Dimitris Rizopoulos
Professor of Biostatistics
Erasmus University Medical Center
The Netherlands
From: Levine, Michael <mlevins using purdue.edu<mailto:mlevins using purdue.edu>>
Date: Saturday, 05 Jan 2019, 7:49 PM
To: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>>
Cc: 'Jiexin Duan' <duan32 using purdue.edu<mailto:duan32 using purdue.edu>>
Subject: [R-sig-ME] Prediction variance for GLMM
Dear all,
I would like to ask the following question. Is it possible to obtain prediction variances for GLMMs in the package lme4 , based e.g. on the function glmer? I know that it is possible to do it with "pure" GLM's but I don't see any options for GLMM's. I realize there is a problem there because such a variance can be defined in several different ways...
Let me know and thanks a lot in advance!
Yours,
Michael Levine
Associate Professor, Statistics
Department of Statistics
Purdue University
250 North University Street
West Lafayette, IN 47907 USA
email: mlevins using purdue.edu
Phone: +1-765-496-7571
Fax: +1-765-494-0558
URL: www.stat.purdue.edu/~mlevins<http://www.stat.purdue.edu/~mlevins>
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