# predict.glmmPQL

From MASS v7.3-47
by Brian Ripley

##### Predict Method for glmmPQL Fits

Obtains predictions from a fitted generalized linear model with random effects.

- Keywords
- models

##### Usage

```
# S3 method for glmmPQL
predict(object, newdata = NULL, type = c("link", "response"),
level, na.action = na.pass, ...)
```

##### Arguments

- object
- a fitted object of class inheriting from
`"glmmPQL"`

. - newdata
- optionally, a data frame in which to look for variables with which to predict.
- type
- the type of prediction required. The default is on the
scale of the linear predictors; the alternative
`"response"`

is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and`type = "response"`

gives the predicted probabilities. - level
- an optional integer vector giving the level(s) of grouping to be used in obtaining the predictions. Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions. Defaults to the highest or innermost level of grouping.
- na.action
- function determining what should be done with missing
values in
`newdata`

. The default is to predict`NA`

. - …
- further arguments passed to or from other methods.

##### Value

If `level`

is a single integer, a vector otherwise a data frame.

##### See Also

##### Examples

`library(MASS)`

```
fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID,
family = binomial, data = bacteria)
predict(fit, bacteria, level = 0, type="response")
predict(fit, bacteria, level = 1, type="response")
```

*Documentation reproduced from package MASS, version 7.3-47, License: GPL-2 | GPL-3*

### Community examples

Looks like there are no examples yet.