glmmTMB (version 0.2.2.0)

predict.glmmTMB: prediction

Description

prediction

Usage

# S3 method for glmmTMB
predict(object, newdata = NULL, se.fit = FALSE, re.form,
  allow.new.levels = FALSE, type = c("link", "response", "conditional",
  "zprob", "zlink"), zitype = NULL, na.action = na.pass, debug = FALSE,
  ...)

Arguments

object

a glmmTMB object

newdata

new data for prediction

se.fit

return the standard errors of the predicted values?

re.form

(not yet implemented) specify which random effects to condition on when predicting

allow.new.levels

allow previously unobserved levels in random-effects variables? see details.

type

Denoting \(mu\) as the mean of the conditional distribution and p as the zero-inflation probability, the possible choices are:

"link"

conditional mean on the scale of the link function, or equivalently the linear predictor of the conditional model

"response"

expected value; this is \(mu*(1-p)\) for zero-inflated models and mu otherwise

"conditional"

mean of the conditional response; mu for all models (i.e., synonymous with "response" in the absence of zero-inflation

"zprob"

the probability of a structural zero (gives an error for non-zero-inflated models)

##'

"zilink"

predicted zero-inflation probability on the scale of the logit link function

zitype

deprecated: formerly used to specify type of zero-inflation probability. Now synonymous with type

na.action

how to handle missing values in newdata (see na.action); the default (na.pass) is to predict NA

debug

(logical) return the TMBStruc object that will be used internally for debugging?

…

unused - for method compatibility

Details

Prediction of new random effect levels is possible as long as the model specification (fixed effects and parameters) is kept constant. However, to ensure intentional usage, a warning is triggered if allow.new.levels=FALSE (the default).

Examples

# NOT RUN {
data(sleepstudy,package="lme4")
g0 <- glmmTMB(Reaction~Days+(Days|Subject),sleepstudy)
predict(g0, sleepstudy)
## Predict new Subject
nd <- sleepstudy[1,]
nd$Subject <- "new"
predict(g0, newdata=nd, allow.new.levels=TRUE)
# }