| predict.glm {stats} | R Documentation |
Obtains predictions and optionally estimates standard errors of those predictions from a fitted generalized linear model object.
## 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, ...)
object |
a fitted object of class inheriting from "glm". |
newdata |
optionally, a data frame in which to look for variables with which to predict. 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. |
If newdata is omitted the predictions are based on the data
used for the fit. In that case how cases with missing values in the
original fit is determined by the na.action argument of that
fit. If na.action = na.omit omitted cases will not appear in
the residuals, whereas if na.action = na.exclude they will
appear (in predictions and standard errors), with residual value
NA. See also napredict.
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. |
Variables are first looked for in newdata and then searched for
in the usual way (which will include the environment of the formula
used in the fit). A warning will be given if the
variables found are not of the same length as those in newdata
if it was supplied.
## 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"))