wle.glm.summaries: Accessing Generalized Linear Model Robust Fits

In wle: Weighted Likelihood Estimation

Description

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

Usage

## S3 method for class 'wle.glm'
family(object, ...)

## S3 method for class 'wle.glm'
residuals(object, type = c("deviance", "pearson", "working",
                           "response", "partial"), root="all", ...)

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".
root	vector of integer or characters. If "all" the residuals of all roots are reported, otherwise the position of the root should be supplied.
...	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.

See Also

wle.glm, anova.wle.glm.root; the corresponding generic functions, summary.wle.glm, coef, deviance, df.residual, effects, fitted, residuals.

wle documentation built on May 29, 2017, 11:48 a.m.