sdmTMB
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pbs-assess
:earth_americas: An R package for spatial and spatiotemporal GLMMs with TMB
I followed [these](https://stackoverflow.com/a/70664229) instructions (M1 Mac specific, I think). Then worked on [this](https://github.com/pbs-assess/sdmTMB/tree/parallel) branch. Tested in [this](https://github.com/pbs-assess/sdmTMB/blob/parallel/inst/test-open-mp.R) script. Depends strongly on the model, but I'm getting ~50% speed ups up...
Initial development in CAR branch: To do list: - spatial and spatiotemporal components currently included, with the latter as IID. Does it make sense to also include AR(1)? - matrix.inverse()...
Just ran across the sigma() function in glmmTMB. Seems useful for nbinom2, etc. Also in lme4 https://github.com/glmmTMB/glmmTMB/blob/41a8abcd99692d6fe9e7d82f454e4ec57ab3f162/glmmTMB/R/VarCorr.R#L87
The majority of this is now done in the [dispformula](https://github.com/pbs-assess/sdmTMB/tree/dispformula) branch. See below. Still todo: - [ ] add to `tidy.sdmTMB()` - [ ] make sure simulation interfaces work -...
The model likely needs to be tweaked then to return all random effects for normalize = true.
See mgcv for inspiration. Lets you omit some predictors from the newdata. Also, in mgcv, they have an additional argument in predict() 'newdata.guaranteed' which if TRUE results in the function...
@Kotkot made the suggestion and provided some example code. > Imagine we have rand1, rand2, and rand3 random effects acting on the intercept in the model. Each have 10, 20,...
follow same general approach as glmmTMB
Hi, I'm trying to make a prediction for validation ( using 20% of the original dataset) from a fitted ( using 80% of the original dataset) GLM model with spatial-temporal...
Currently silently doing nothing. Offset is meant to be via `offset` argument.
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:earth_americas: An R package for spatial and spatiotemporal GLMMs with TMB