predprob.glm {pscl} | R Documentation |
Obtains predicted probabilities from a fitted generalized linear model object.
## S3 method for class 'glm': predprob(obj,newdata=NULL,...)
obj |
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. |
... |
arguments passed to or from other methods |
This method is only defined for glm objects with
family=binomial
or family=poisson
, or negative
binomial count models fit with the glm.nb
function in library(MASS)
.
a matrix of predicted probabilities. Each row in the matrix is a
vector of probabilities, assigning predicted probabilities over the range of
responses actually observed in the data. For instance, for models
with family=binomial
, the matrix has two columns for the "zero"
(or failure) and "one" (success) outcomes, respectively, and
trivially, each row in the matrix sums to 1.0.
For counts fit with family=poisson
or via glm.nb
, the
matrix has length(min(y):max(y))
columns. Each observation
used in fitting the model generates a row to the returned matrix; alternatively, if
newdata
is supplied, the returned matrix will have as many rows
as in newdata
.
Simon Jackman jackman@stanford.edu
data(bioChemists) glm1 <- glm(art ~ ., data=bioChemists, family=poisson, trace=TRUE) ## poisson GLM phat <- predprob(glm1) apply(phat,1,sum) ## almost all 1.0