R: Predictions from a model at new data values

predict.merMod {lme4}

R Documentation

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` and/or (for GLMMs) `beta`
`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 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.

Value

a numeric vector of predicted values

Note

There is no option for computing standard errors of predictions because it is difficult to define an efficient method that incorporates uncertainty in the variance parameters; we recommend bootMer for this task.

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

[Package lme4 version 1.1-5 Index]