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boot (version 1.2-10)

glm.diag: 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
  • resThe vector of jackknife deviance residuals.
  • rdThe vector of standardized deviance residuals.
  • rpThe vector of standardized Pearson residuals.
  • cookThe vector of approximate Cook statistics.
  • hThe vector of leverages of the observations.
  • sdThe 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.

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