force lmer/glmer to use known random effects
Dear Alexia, IMHO that is not possible with lme4. I think you can do it with INLA which has a "copy" feature. See http://www.r-inla.org/models/tools#TOC-Copying-a-model. You will need to fit both models simultaneous. Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2017-01-07 20:29 GMT+01:00 Alexia Jolicoeur-Martineau < alexia.jolicoeur-martineau at mail.mcgill.ca>:
In SAS, there is an option to use a known covariance matrix for your random effects (See here: https://support.sas.com/ documentation/cdl/en/statug/63033/HTML/default/statug_mixed_sect033.htm). In lme4 we cannot use covariance matrices but we can use random effects. Is there a way for me to do force lmer/glmer to use known random effects variances? My algorithm works in two steps. In step 1, I fit a generalized linear mixed model with a known variable "x". In step 2, I fit the generalized linear mixed model but this time I assume "x" to be unknown and every other parameters to be known (using the parameter estimates from step 1). This is what we call alternating optimization. I thus want to be able to fix the random parameters from the model in step 2 to be the estimates of the random effects from step 1. Is this possible to do? I already implemented my method in SAS but I wish I could also implement in R because 1) SAS macros are slow and 2) SAS is not free so not everyone could use it. Alexia [[alternative HTML version deleted]]
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