lme4 (version 1.1-7)

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

Description

predict method for merMod objects

Usage

## S3 method for class 'merMod':
predict(object, newdata = NULL,
    newparams=NULL, newX=NULL,
    re.form = NULL, ReForm, REForm, REform,
    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<
newX
new design matrix to use in evaluating predictions (alternative to newdata)
re.form
formula for random effects to condition on. If NULL, include all random effects; if NA or ~0, include no random effects
ReForm
allowed for backward compatibility: re.form is now the preferred argument name
REForm
allowed for backward compatibility: re.form is now the preferred argument name
REform
allowed for backward compatibility: re.form is now the preferred argument name
terms
a terms object - not used at present
type
character string - either "link", the default, or "response" indicating the type of prediction object returned
allow.new.levels
(logical) if FALSE (default), then any new levels (or NA values) detected in newdata will trigger an error; if TRUE, then the prediction will use the unconditional (population-level) values for data with previously unobserved
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.

Value

  • a numeric vector of predicted values

Examples

Run this code
(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), cbpp, binomial))
str(p0 <- predict(gm1))            # fitted values
str(p1 <- predict(gm1,ReForm=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,ReForm=NA)) # new data, level-0
str(p4 <- predict(gm1,newdata,ReForm=~(1|herd))) # explicitly specify RE

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