glm.diag                package:boot                R Documentation

Generalized Linear Model Diagnostics

Description:

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

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 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.

See Also:

     'glm', 'glm.diag.plots', 'summary.glm'

