glm {safeBinaryRegression}R Documentation

Fitting Generalized Linear Models

Description

This function overloads the glm function so that a check for the existence of the maximum likelihood estimate is computed before fitting a ‘glm’ with a binary response.

Usage

glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control = glm.control(...), model = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, ..., separation = c("find", "test"))

Arguments

The arguments are identical to the arguments of the glm function provided in the ‘stats’ package with the exception of

separation either “find” or “test”. Both options prevent the model from being fit to binary data when the maximum likelihood estimate does not exist. Additionally, when separation = "find", the terms separating the sample points are identified when the maximum likelihood estimate is found not to exist.
formula see glm
family see glm
data see glm
weights see glm
subset see glm
na.action see glm
start see glm
etastart see glm
mustart see glm
offset see glm
control see glm
model see glm
method see glm
x see glm
y see glm
contrasts see glm
... see glm

Details

This function checks for the existence of the maximum likelihood estimate before the ‘glm’ function is used to fit binary regression models by solving the linear program proposed in Konis (2007).

Value

See the return value for the glm function.

Author(s)

Kjell Konis kjell.konis@epfl.ch

References

Kjell Konis (2007). Linear programming algorithms for detecting separated data in binary logistic regression models. DPhil, University of Oxford http://ora.ouls.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a

See Also

glm.

Examples

## A set of 4 completely separated sample points ##
x <- c(-2, -1, 1, 2)
y <- c(0, 0, 1, 1)

## Not run: glm(y ~ x, family = binomial)

## A set of 4 quasicompletely separated sample points ##
x <- c(-2, 0, 0, 2)
y <- c(0, 0, 1, 1)

## Not run: glm(y ~ x, family = binomial)

[Package safeBinaryRegression version 0.1-2 Index]