[R-sig-ME] glmmTMB model requires weights in prediction - what to supply
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Fri Oct 20 02:16:11 CEST 2023
It would probably be harmless to specify weight = 1 for the
prediction. I suspect this is just a side effect of the way that the
prediction machinery works (I haven't looked). If you have a
reproducible example, could you post this as an issue at the glmmTMB
issues list?
On 2023-10-19 6:53 a.m., Werner Poschenrieder wrote:
> Dear Mixed-Models Community,
>
>
>
> I have to predict from a model of class glmmTMB that has been calibrated as
> follows
>
>
>
> the_formula = as.formula("used_avail ~ SHA_DI_cat +
> scale(forest_dist_raster) + scale(resid_dist_raster) +
> scale(woody_dist_raster) + scale(road_dist_raster) +
> scale(I(road_dist_raster^2)) + grassland + (1|study.area) + (1|IDyear)")
>
>
>
> tmbmod = glmmTMB::glmmTMB(the_formula , data = group_df, weights = weight,
> family = binomial(link = "logit"), na.action = na.omit)
>
>
>
> I am surprised that prediction from that model using newdata does complain:
>
>
>
> predict(tmbmod, newdata = df_newdata, type = type, re.form = "~0", se.fit =
> T, allow.new.levels=TRUE)
>
> Error in eval(extras, data, env) : object 'weight' not found
>
>
>
> where df_newdata includes any of the predictors, i.e. fixed and random
> effects. However, it does not include the column weight, as to my
> understanding weights are for giving the residual a particular weight in
> the course of fitting the model.
>
> Such a weight thus results in an according value of the resulting effects.
>
>
>
> What are weights for in prediction with glmmTMB, and thus, what value to
> set them to?
>
>
>
> Thanks in advance, best
>
>
>
> Werner
>
> [[alternative HTML version deleted]]
>
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