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For many model classes in R an implementation of the logLik generic is provided. I often use this on holdout data (this usually requires manipulating the fitted object) to compare different models. Thus I would only need REML=FALSE and would appreciate a newdata argument.
The text was updated successfully, but these errors were encountered:
I like the suggestion. Do you think the function should treat the overdispersion as fixed or would you want the combined likelihood of mean estimates and overdispersion?
I am not 100% sure what the model is that is actually fit. I understood
that the overdispersion estimates are fixed estimates so they should should
only apear on the conditioning part P(new data| coef) where coef are gene
means, fixed effects, overdispersion). The size factors are probably harder
to deal with...
However, I noticed that a `newdata` argument is far from standard for
`logLik`in R (not even for `lm` and usually requires some hacks to get) I
guess I was spoiled by Stan/Pyro when requesting that.
For many model classes in R an implementation of the
logLik
generic is provided. I often use this on holdout data (this usually requires manipulating the fitted object) to compare different models. Thus I would only needREML=FALSE
and would appreciate anewdata
argument.The text was updated successfully, but these errors were encountered: