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profile.glm Method for Profiling glm Objects

Description

Investigates the profile log-likelihood function for a fitted model of class "glm".

Usage

## S3 method for class 'glm'
profile(fitted, which = 1:p, alpha = 0.01, maxsteps = 10,
        del = zmax/5, trace = FALSE, ...)

Arguments

fitted	the original fitted model object.
which	the original model parameters which should be profiled. This can be a numeric or character vector. By default, all parameters are profiled.
alpha	highest significance level allowed for the profile t-statistics.
maxsteps	maximum number of points to be used for profiling each parameter.
del	suggested change on the scale of the profile t-statistics. Default value chosen to allow profiling at about 10 parameter values.
trace	logical: should the progress of profiling be reported?
...	further arguments passed to or from other methods.

Details

The profile t-statistic is defined as the square root of change in sum-of-squares divided by residual standard error with an appropriate sign.

Value

A list of classes "profile.glm" and "profile" with an element for each parameter being profiled. The elements are data-frames with two variables

par.vals	a matrix of parameter values for each fitted model.
tau	the profile t-statistics.

Author(s)

Originally, D. M. Bates and W. N. Venables. (For S in 1996.)

See Also

glm, profile, plot.profile

Examples

options(contrasts = c("contr.treatment", "contr.poly"))
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)
pr1 <- profile(budworm.lg)
plot(pr1)
pairs(pr1)