robcbi (version 1.1-4)

cubinf.summaries: Functions required by the corresponding access functions

Description

Auxiliary functions for residuals(), summary(), covar(), deviance(), family(), Rank(), rscale(), weights().

Usage

# S3 method for cubinf
residuals(object, type = c("deviance", "pearson", "response"), ...)
# S3 method for cubinf
summary(object, ...)
 
# S3 method for cubinf
covar(object)
 
# S3 method for cubinf
deviance(object, ...)
# S3 method for cubinf
family(object, ...)
 
# S3 method for cubinf
Rank(object)
 
# S3 method for cubinf
rscale(object)
 
# S3 method for cubinf
weights(object)

Value

summary.cubinf returns a list with the following components:

call

The model formula used in glm.

terms

Terms object used in fitting the model.

coefficients

A matrix with three columns, containing the coefficients, their standard errors and the corresponding t-statistics.

dispersion

Dispersion coefficient

df

Degrees of freedom for model and residuals.

deviance.resid

Deviance residuals

family

The family function used: binomial or poisson

cov.unscaled

Unscaled covariance matrix of coefficient estimates.

correlation

Correlation matrix of coefficient estimates.

deviance

Deviance.

null.deviance

Null deviance.

iter

Number of iterations of the main algorithm.

nas

A logical vector whose i-th component is TRUE if the i-th coefficient is NA.

Arguments

object

An object inheriting from class "cubinf".

type

A character string for the residual type.

...

Optional arguments. For summary, it can be correlation=TRUE.

Details

The generic functions coef, residuals, fitted, formula, deviance, rscale, r.squared, covar, correl, weights and Rank can be used to extract elements from an object of class "cubinf" returned by glm. The class "lm" functions effects, alias, add1, drop1, codekappa, proj, step, influence, anova and sensitivity are not implemented to objects of class "cubinf".

See Also

The model fitting function glm, cubinf

Examples

 library(robcbi)
 data(Finney)
 Vol <- Finney$Vol; Rate <- Finney$Rate; Resp <- Finney$Resp
 lVol <-log(Vol); lRate <- log(Rate)
 z.glm <- glm(Resp~lVol+lRate,family=binomial)
 summary(z.glm)
 z.cub <- glm(Resp~lVol+lRate,family=binomial,method="cubinf", ufact=3.2)
 summary(z.cub)
 weights(z.cub)
 covar(z.cub)
 deviance(z.cub)
 Rank(z.cub)
 residuals(z.cub)
 rscale(z.cub)