Dear all,
I am working with my doctoral thesis and trying to fit a generalized linear mixed effects model by using ‘MCMCglmm’ package in R. And actually this is the first time I work with it. I had repeatedly read Jarrod's tutorial materials and papers and they are very helpful for understanding the MCMCglmm method. However, there are still some problems about the priors specification I failed to figure out. I had been working with them for a couple of weeks but I cannot solve them.
In my research, the dependent variable is the number of people participating in household energy conservation program (count outcome). It has been repeatedly measured for each day over approximately three years for each of 360 communities (the data are thus quite big and n = 371, 520). In addition, these communities are located at different districts (there are a total of 90 districts). Thus, the longitudinal daily count data are nested within communities, which are nested within districts. My research aims to investigate which time-variant and time-invariant factors would influence the (daily) number of participants in such program. The basic model is (over-dispersed) Poisson model and the codes are cited as follows.
# load the data
load("dat.big.rdata")
#the requisite package
require(MCMCglmm)
#give the priors
prior.poi
I suggest to swithch to Python in order to make own implementatin from scratch.
This way wil be simple you will control your own code and will not be dependent on R prebuild library .
I find brms easier to work with than MCMCglmm as an R package. It is easier to set priors and runs a wider range of R code. As it is an interface for working with Stan you can also get switch to Stan if your prefer.
I don't think it would be sensible to implement your own probabilistic programming language in R.
Here is a good starting set of articles on brms:
https://paul-buerkner.github.io/blog/brms-blogposts/
Dear Ling,
I am not familiar to package MCMCglmm. I agree with Dr. Baguley that using brms is flexible and powerful. You can use brms for mixed logistic, mixed negative binomial and linear mixed and many other models.
Thank you all for these suggestions. I use this prior and MCMCglmm method in my analysis for the time being, and I will try brms later. Thanks again!
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