| 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"),
se.fit=FALSE, dispersion=NULL, ...)
Arguments
object |
A fitted object of class inheriting from |
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 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
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
|
Value
If se = FALSE, a vector 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
See Also
glm
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"))