predict.glm {stats}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

## S3 method for class 'glm':
predict(object, newdata = NULL,
            type = c("link", "response", "terms"),
            se.fit = FALSE, dispersion = NULL, terms = NULL,
            na.action = na.pass, ...)

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 logical 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 character vector specifies which terms are to be returned
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 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.

See Also

glm, SafePrediction

Examples

## example from Venables and Ripley (2002, pp. 190-2.)
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"))
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