R: Separation Identification.

separation.detection {brglm} | R Documentation

## Separation Identification.

### Description

Provides a tool for identifying whether or not separation has occurred.

### Usage

    separation.detection(fit, nsteps = 30)

### Arguments

`fit` | the result of a `glm` call.
`nsteps` | Starting from `maxit = 1`, the GLM is refitted for `maxit = 2`, `maxit = 3`, ..., `maxit = nsteps`. Default value is 30.

### Details

Identifies separated cases for binomial-response GLMs, by refitting the model. At each iteration the maximum number of allowed IWLS iterations is fixed starting from 1 to `nsteps` (by setting `control = glm.control(maxit = j)`, where `j` takes values 1, ..., nsteps in `glm`). For each value of `maxit`, the estimated asymptotic standard errors are divided to the corresponding ones resulted for `control = glm.control(maxit = 1)`. Based on the results in Lesaffre & Albert (1989), if the sequence of ratios in any column of the resultant matrix diverges, then separation occurs and the maximum likelihood estimate for the corresponding parameter has value minus or plus infinity.

### Value

A matrix of dimension `nsteps` by `length(coef(fit))`, that contains the ratios of the estimated asymptotic standard errors.

### Author(s)

Ioannis Kosmidis, i.kosmidis@ucl.ac.uk

### References

Lesaffre, E. and Albert, A. (1989). Partial separation in logistic discrimination. J. R. Statist. Soc. B, 51, 109–116.

### Examples

    ## Begin Example
    y <- c(1,1,0,0)
    totals <- c(2,2,2,2)
    x1 <- c(1,0,1,0)
    x2 <- c(1,1,0,0)
    m1 <- glm(y/totals ~ x1 + x2, weights = totals, family = binomial())
    # No warning from glm...
    m1
    # However estimates for (Intercept) and x2 are unusually large in
    # absolute value... Investigate further:
    #
    separation.detection(m1,nsteps=30)
    # Note that the values in the column for (Intercept) and x2 diverge,
    # while for x1 converged. Hence, separation has occurred and the
    # maximum lieklihood estimate for (Intercept) is minus infinity and
    # for x2 is plus infinity. The signs for infinity are taken from the
    # signs of (Intercept) and x1 in coef(m1).
    ## End Example

[Package brglm version 0.5-9 Index]