Comments (4)
I've been a bit inconsistent with this but where appropriate I planned to support lm()
and glm()
from base R — appraise()
works for those models for example. I hadn't considered supporting glmmTMB()
; I'll be more motivated to do this if/once we have penalised splines in glmmTMB()
.
Are there models/functions/packages that you are especially interested in? And which functions in gratia would you like to see supported for them?
There are other more general options for a wider range of model types, like marginaleffects.
from gratia.
That makes sense. I was not aware that appraise
works for lm
and glm
, but that's great to hear. The functions in gratia that I use the most are draw
and appraise
, so those would have to be my top choices.
Outside of mgcv
, glmmTMB
, and base R, the only other modeling package I use frequently is sdmTMB
. sdmTMB
models can include smooths fitted via mgcv
, but I haven't tried them with gratia
yet. It would be very cool to be able to visualize spatiotemporal predictions from sdmTMB
models using draw
alone, similar to how you can use draw
to visualize 2D smooths etc. The predictions from sdmTMB
models are already tidy too!
Tagging @seananderson in the hopes that this one day becomes reality
from gratia.
Opening this up again as glmmTMB has added spline support, see
glmmTMB/glmmTMB#928
There is some discussion of adding support in gratia. Looking through the code it seems that there would need to be a draw.glmmTMB method as well as a smooths.glmmTMB method to find all the smooth terms in the glmmTMB structure. Is there anything I am missing, pretty sure there is more
from gratia.
@authagag Oh, that saves me a job then as I had been looking at what would be required to add smooths to glmmTMB, gamm4-style.
The main thing that would be needed would be smooth_estimates()
and parametric_effects()
methods for class "glmmTMB"
. Assuming that glmmTMB does sensible things like store the actual mgcv smooth objects in model$smooths
then most things should just work, and specific methods could simply just call the "gam"
methods for the plotting code. Things could get more complex if the objects returned by glmmTMB()
don't have many of the things that I'd find in a "gam"
object as then specific methods for many internal functions could be needed.
I can take a look at glmmTMB/glmmTMB#928 and install from the repo and see what would be needed from gratia's point of view.
from gratia.
Related Issues (20)
- prediction-based plots when using by matrices in linear functionals HOT 3
- difference_smooths for models with multiple by= variables HOT 5
- Derivatives of the difference between two smooths
- Support for models with `gfam()` family
- `data_sim()`
- gfam support in `appraise()`
- `draw()` works with gfam models HOT 1
- gfam support in `fitted_samples()`
- gfam support in `posterior_samples()`
- gfam support in `smooth_samples()`
- gfam support in `simulate.gam()`
- `compute_partial_residuals` is wrong if deviance residuals are not equivalent to pearson residuals? HOT 2
- Ensure continuity of gam from start of series to end of series HOT 1
- Tests fail with current version of `Matrix` (1.6-5): `function 'chm_factor_ldetL2' not provided by package 'Matrix'` HOT 3
- Improve plotting code for SOS (spline-on-the-sphere) smooths
- Work towards bayesplot and loo integration?
- Select mvn_method when running `fitted_samples()` HOT 1
- shift=TRUE not working for confint.gam() HOT 5
- Possible PR: making posterior draws compatible with MCMC diagnostic packages
- `draw()` fails with `parametric = TRUE` if there aren't any parametric effects to actually plot
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from gratia.