# glm.diag

0th

Percentile

##### Generalized Linear Model Diagnostics

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

Keywords
regression, dplot
##### Usage
glm.diag(glmfit)
##### Arguments
glmfit

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

##### Value

Returns a list with the following components

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 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 help for glm.diag.plots for an example of the use of glm.diag.

##### 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.

glm, glm.diag.plots, summary.glm