predict.glm {base} | R Documentation |
Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized linear model object.
predict.glm(object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE, dispersion = NULL, terms = NULL, ...)
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 |
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. |
B.D. Ripley
## 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"))