safeBinaryRegression (version 0.1-3)

glm: 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

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

Value

See the return value for the glm function.

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).

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)

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