R: Predict Method for GLM Fits

predict.glm {base}

R Documentation

Predict Method for GLM Fits

Description

Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized linear model object.

Usage

predict.glm(object, newdata=NULL, type=c("link", "response", "terms"),
            se.fit=FALSE, dispersion=NULL, terms=NULL, ...)

Arguments

object

A fitted object of class inheriting from "glm".

newdata

Optionally, a new data frame from which to make the predictions. If omitted, the fitted linear predictors are used.

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. The "terms" option returns a matrix giving the fitted values of each term in the model formula on the linear predictor scale

The value of this argument can be abbreviated.

se.fit

A switch indicating if standard errors are required.

dispersion

The dispersion of the GLM fit to be assumed in computing the standard errors. If omitted, that returned by summary applied to the object is used.

terms

With type="terms" by default all terms are returned. A vector of strings specifies which terms are to be returned

Value

If se = FALSE, a vector or matrix of predictions. If se = TRUE, a list with components

fit

Predictions

se.fit

Estimated standard errors

residual.scale

A scalar giving the square root of the dispersion used in computing the standard errors.

Note

This method is also currently used for objects of class "survreg" (parametric survival fits from package survival4) and possibly others. The assumptions made by predict.glm may not always be right for such objects.

Author(s)

B.D. Ripley

Examples

## example from Venables and Ripley (1997, pp. 231-3.)
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive=20-numdead)
budworm.lg <- glm(SF ~ sex*ldose, family=binomial)
summary(budworm.lg)

plot(c(1,32), c(0,1), type="n", xlab="dose",
   ylab="prob", log="x")
text(2^ldose, numdead/20,as.character(sex))
ld <- seq(0, 5, 0.1)
lines(2^ld, predict(budworm.lg, data.frame(ldose=ld,
   sex=factor(rep("M", length(ld)), levels=levels(sex))),
   type="response"))
lines(2^ld, predict(budworm.lg, data.frame(ldose=ld,
   sex=factor(rep("F", length(ld)), levels=levels(sex))),
   type="response"))

[Package base version 0.90 ]