prediction
prediction
## S3 method for class '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,
...
)object |
a |
newdata |
new data for prediction |
newparams |
new parameters for prediction |
se.fit |
return the standard errors of the predicted values? |
re.form |
|
allow.new.levels |
allow previously unobserved levels in random-effects variables? see details. |
type |
Denoting mu as the mean of the conditional distribution and
|
zitype |
deprecated: formerly used to specify type of zero-inflation probability. Now synonymous with |
na.action |
how to handle missing values in |
debug |
(logical) return the |
... |
unused - for method compatibility |
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.