CODEWITHSUNDEEP
rlogistic-regressionrasterrandom-effectsglmmtmb
Beardedant
Beardedant

Reputation: 150

glmmTMB predictions error concerning random effect

I am trying to create a raster with predictions for a model, using glmmTMB. This is based on a model, and a rasterstack. I converted the rasterstack to a data frame, as I think this is a requirement for the function predict.glmmTMB to run.

The model

model6 <- glmmTMB(Used~scale(Road_density)+scale(nonforprop)+scale(devprop)+
                  scale(forprop)+scale(nonfordist_cap3000)+scale(fordist_cap3000)+
                  scale(agridist_cap3000)+scale(devdist_cap3000)+(1|animal_ID),
            data=rasterpoints3,na.action=na.omit,family=binomial(link="logit"))

The data frame containing the rasterstack values to predict for

predstack <- as.data.frame(stack2)

The error

 glmmTMB:::predict.glmmTMB(model6,predstack,re.form=NA)

Error in eval(predvars, data, env) : object 'animal_ID' not found

I was hoping someone more experienced could help me resolve this. animal_ID is the random intercept in my glmmTMB object model 6. I am using this package, and not e.g. raster::predict, exactly because it should be able to deal with random effects. To my understanding, re.form=NA should take care of this?

Upvotes: 2

Views: 617

Answers (2)

Robert Hijmans
Robert Hijmans

Reputation: 47481

Given Ben's answer, this ought to work as well with either the raster or terra package:

 p <- predict(stack2, model6, const=data.frame(animal_ID=NA), re.form=NA)

(But in the absence of an example I cannot check it)

Upvotes: 1

Ben Bolker
Ben Bolker

Reputation: 226741

There's an open issue about this, but the workaround should be easy: define

predstack$animal_ID <- NA

The random effect variable has to exist in the data, but it's not used. (Due to the internal structure of glmmTMB it's not completely trivial to fix this at the package level.)

Upvotes: 2

Related Questions