glmtoolbox (version 0.1.12)

predict.glmgee: Predictions for Generalized Estimating Equations

Description

Produces predictions and optionally estimates standard errors of those predictions from a fitted generalized estimating equation.

Usage

# S3 method for glmgee
predict(
  object,
  ...,
  newdata,
  se.fit = FALSE,
  type = c("link", "response"),
  varest = c("robust", "df-adjusted", "model", "bias-corrected")
)

Value

A matrix with so many rows as newdata and one column with the predictions. If se.fit=TRUE then a second column with estimates standard errors is included.

Arguments

object

an object of the class glmgee.

...

further arguments passed to or from other methods.

newdata

an (optional) data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.

se.fit

an (optional) logical switch indicating if standard errors are required. As default, se.fit is set to FALSE.

type

an (optional) character string giving the type of prediction required. The default, "link", is on the scale of the linear predictors, and the alternative, "response", is on the scale of the response variable.

varest

an (optional) character string indicating the type of estimator which should be used to the variance-covariance matrix of the interest parameters. The available options are: robust sandwich-type estimator ("robust"), degrees-of-freedom-adjusted estimator ("df-adjusted"), bias-corrected estimator ("bias-corrected"), and the model-based or naive estimator ("model"). As default, varest is set to "robust".

Examples

###### Example 1: Effect of ozone-enriched atmosphere on growth of sitka spruces
data(spruces)
mod1 <- size ~ poly(days,4) + treat
fit1 <- glmgee(mod1, id=tree, family=Gamma(log), data=spruces, corstr="AR-M-dependent")
newdata1 <- data.frame(days=c(556,556),treat=as.factor(c("normal","ozone-enriched")))
predict(fit1,newdata=newdata1,type="response",se.fit=TRUE)

###### Example 2: Treatment for severe postnatal depression
data(depression)
mod2 <- depressd ~ visit + group
fit2 <- glmgee(mod2, id=subj, family=binomial(logit), corstr="AR-M-dependent", data=depression)
newdata2 <- data.frame(visit=c(6,6),group=as.factor(c("placebo","oestrogen")))
predict(fit2,newdata=newdata2,type="response",se.fit=TRUE)

###### Example 3: Treatment for severe postnatal depression (2)
mod3 <- dep ~ visit*group
fit3 <- glmgee(mod3, id=subj, family=gaussian(identity), corstr="AR-M-dependent", data=depression)
newdata3 <- data.frame(visit=c(6,6),group=as.factor(c("placebo","oestrogen")))
predict(fit3,newdata=newdata3,type="response",se.fit=TRUE)