robcbi (version 1.1-2)

robust.print.summaries: Print methods for objects of class "cubinf", "cubinf.i", "summary.cubinf" or "glm.i"

Description

Printing linear model fits provided by glm or with method="cubinf"

Usage

# S3 method for cubinf
print(x, ai = FALSE, ci = FALSE, A.mat = FALSE, ...)
# S3 method for summary.cubinf
print(x, ...)
# S3 method for glm.i
print(x, ...)

Arguments

x

An object result of a call to summary.cubinf (first usage), to glm with method="cubinf" (second usage), to rscale.cubinf or to summary.cubinf or to weights.cubinf or to covar.cubinf (third usage) and respectively to rscale.glm or or to covar.glm or to weights.glm.

ai

Vector with components a_i=ufact/|Ax_i| (where x_i^T denotes the ith row of the model matrix).

ci

Vector of the final bias corrections.

A.mat

The final value of the matrix A.

...

Further optional arguments according to the print method. Implicit argument in all these functions is digits = max(3, .Options$digits - 3).

References

Kuensch, H.R., Stefanski L.A., Carroll R.J. (1989). Conditionally unbiased bounded-influence estimation in general regression models, with application to generalized linear models. Journal of the American Statistical Association, 84, 460-466.

Marazzi, A. (1993). Algorithms, Routines, and S-functions for robust Statistics. Chapman and Hall, New York.

See Also

The model fitting function glm, cubinf

Examples

# NOT RUN {
library(robcbi)
 
## Dobson (1990) Page 93: Randomized Controlled Trial :
 counts <- c(18,17,15,20,10,20,25,13,12)
 outcome <- gl(3,1,9)
 treatment <- gl(3,3)
 print(d.AD <- data.frame(treatment, outcome, counts))
 zD93 <- glm(counts ~ outcome + treatment, family=poisson,method="cubinf",ufact=3.2)
 summary(zD93)
 print(zD93)
 wi <- weights(zD93)
 print(wi)
# }