glm.diag: Generalized Linear Model Diagnostics

In SMPracticals: Practicals for Use with Davison (2003) Statistical Models

Description

Calculates jackknife deviance residuals, standardized deviance residuals, standardized Pearson residuals, approximate Cook statistic, leverage and estimated dispersion.

Usage

## S3 method for class 'diag'
glm(glmfit)

Arguments

glmfit

glmfit is a glm.object - the result of a call to glm()

Value

A list containing the following items:

res	The vector of jackknife deviance residuals.
rd	The vector of standardized deviance residuals.
rp	The vector of standardized Pearson residuals.
cook	The vector of approximate Cook statistics.
h	The vector of leverages of the observations.
sd	The value used to standardize the residuals. This is the the estimate of residual standard deviation in the Gaussian family and is the square root of the estimated shape parameter in the Gamma family. In all other cases it is 1.

Note

See the helpfile for glm.diag.plots for an example of the use of glm.diag.

Author(s)

Anthony Davison <[e-mail]>

References

Davison, A.C. and Snell, E.J. (1991) Residuals and diagnostics. In Statistical Theory and Modelling: In Honour of Sir David Cox. D.V. Hinkley, N. Reid and E.J. Snell (editors), 83-106. Chapman and Hall.

See Also

glm,plot.glm.diag,summary.glm

SMPracticals documentation built on May 2, 2019, 11:12 a.m.