R: Method for Profiling glm Objects

profile.glm {MASS} | R Documentation

## 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)

[Package MASS version 7.3-35 Index]