stats (version 3.3)

glm.summaries: Accessing Generalized Linear Model Fits

Description

These functions are all methods for class glm or summary.glm objects.

Usage

## S3 method for class 'glm':
family(object, \dots)
## S3 method for class 'glm':
residuals(object, type = c("deviance", "pearson", "working",
                           "response", "partial"), ...)

Arguments

object

an object of class glm, typically the result of a call to glm.

type

the type of residuals which should be returned. The alternatives are: "deviance" (default), "pearson", "working", "response", and "partial". Can be abbreviated.

...

further arguments passed to or from other methods.

Details

The references define the types of residuals: Davison & Snell is a good reference for the usages of each.

The partial residuals are a matrix of working residuals, with each column formed by omitting a term from the model.

How residuals treats cases with missing values in the original fit is determined by the na.action argument of that fit. If na.action = na.omit omitted cases will not appear in the residuals, whereas if na.action = na.exclude they will appear, with residual value NA. See also naresid.

For fits done with y = FALSE the response values are computed from other components.

References

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

Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

See Also

glm for computing glm.obj, anova.glm; the corresponding generic functions, summary.glm, coef, deviance, df.residual, effects, fitted, residuals.

influence.measures for deletion diagnostics, including standardized (rstandard) and studentized (rstudent) residuals.