glmmTMB (version 1.0.2.1)

predict.glmmTMB: prediction

Description

prediction

Usage

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

Arguments

object

a glmmTMB object

newdata

new data for prediction

newparams

new parameters for prediction

se.fit

return the standard errors of the predicted values?

re.form

NULL to specify individual-level predictions; ~0 or NA to specify population-level predictions (i.e., setting all random effects to zero)

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)

"zlink"

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

"disp"

dispersion parameter however it is defined for that particular family as described in sigma.glmmTMB

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

• To compute population-level predictions for a given grouping variable (i.e., setting all random effects for that grouping variable to zero), set the grouping variable values to NA. Finer-scale control of conditioning (e.g. allowing variation among groups in intercepts but not slopes when predicting from a random-slopes model) is not currently possible.

• 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).

• Prediction using "data-dependent bases" (variables whose scaling or transformation depends on the original data, e.g. poly, ns, or poly) should work properly; however, users are advised to check results extra-carefully when using such variables. Models with different versions of the same data-dependent basis type in different components (e.g. formula= y ~ poly(x,3), dispformula= ~poly(x,2)) will probably not produce correct predictions.

Examples

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