profile.glm {stats} | 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, test = c("LRT", "Rao"), ...)
```

### 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 z-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? |

`test` |
profile Likelihood Ratio test or Rao Score test. |

`...` |
further arguments passed to or from other methods. |

### Details

The profile z-statistic is defined either as (case `test = "LRT"`

) the square root of change in
deviance with an
appropriate sign, or (case `test = "Rao"`

) as the similarly
signed square root of the Rao Score test
statistic. The latter is defined as the squared gradient of the profile log likelihood
divided by the profile Fisher information, but more conveniently
calculated via the deviance of a Gaussian GLM fitted to the residuals
of the profiled model.

### 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 or z` |
the profile t or z-statistics (the name depends on whether there is an estimated dispersion parameter.) |

### Author(s)

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

### See Also

### 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)
```

*stats*version 4.4.0 Index]