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

(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