# profile.glm

From MASS v7.3-47
by Brian Ripley

##### Method for Profiling glm Objects

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

.

- Keywords
- models, regression

##### Usage

```
# S3 method for 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

##### See Also

##### Examples

`library(MASS)`

```
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)
```

*Documentation reproduced from package MASS, version 7.3-47, License: GPL-2 | GPL-3*

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