predict.merMod: Predictions from a model at new data values

Description Usage Arguments Details Value Examples

View source: R/predict.R

Description

The predict method for merMod objects, i.e. results of lmer(), glmer(), etc.

Usage

1
2
3
4
5
6
## S3 method for class 'merMod'
predict(object, newdata = NULL, newparams = NULL,
	re.form = NULL, ReForm, REForm, REform,
        random.only=FALSE, terms = NULL,
	type = c("link", "response"), allow.new.levels = FALSE,
	na.action = na.pass, ...)

Arguments

object

a fitted model object

newdata

data frame for which to evaluate predictions.

newparams

new parameters to use in evaluating predictions, specified as in the start parameter for lmer or glmer – a list with components theta and/or (for GLMMs) beta.

re.form

(formula, NULL, or NA) specify which random effects to condition on when predicting. If NULL, include all random effects; if NA or ~0, include no random effects.

ReForm, REForm, REform

allowed for backward compatibility: re.form is now the preferred argument name.

random.only

(logical) ignore fixed effects, making predictions only using random effects?

terms

a terms object - unused at present.

type

character string - either "link", the default, or "response" indicating the type of prediction object returned.

allow.new.levels

logical if new levels (or NA values) in newdata are allowed. If FALSE (default), such new values in newdata will trigger an error; if TRUE, then the prediction will use the unconditional (population-level) values for data with previously unobserved levels (or NAs).

na.action

function determining what should be done with missing values for fixed effects in newdata. The default is to predict NA: see na.pass.

...

optional additional parameters. None are used at present.

Details

Value

a numeric vector of predicted values

Examples

1
2
3
4
5
6
7
8
(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), cbpp, binomial))
str(p0 <- predict(gm1))            # fitted values
str(p1 <- predict(gm1,re.form=NA))  # fitted values, unconditional (level-0)
newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd)))
str(p2 <- predict(gm1,newdata))    # new data, all RE
str(p3 <- predict(gm1,newdata,re.form=NA)) # new data, level-0
str(p4 <- predict(gm1,newdata,re.form= ~(1|herd))) # explicitly specify RE
stopifnot(identical(p2, p4))

lme4 documentation built on June 22, 2021, 9:07 a.m.