predict.merMod {lme4} | R Documentation

## Predictions from a model at new data values

### Description

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

### Usage

    ## 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 for random effects to condition on. 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

- If any random effects are included in `re.form` (see below), `newdata` must contain columns corresponding to all of the grouping variables and random effects used in the original model, even if not all are used in prediction; however, they can be safely set to `NA` in this case.

- 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.

### 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,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))

[Package lme4 version 1.1-21 Index]