lmsglm: Forward Search in Generalized Linear Models

In forward: Robust Analysis using Forward Search

Forward Search in Generalized Linear Models

Description

This function computes the Least Median Square robust fit for generalized linear models using deviance residuals.

Usage

lmsglm(x, y, family, weights, offset, n.samples = 100, max.samples = 200,
    epsilon = 1e-04, maxit = 50, trace = FALSE)

Arguments

x	a matrix or data frame containing the explanatory variables.
y	the response: a vector of length the number of rows of x.
family	a description of the error distribution and link function to be used in the model. See family for details.
weights	an optional vector of weights to be used in the fitting process.
offset	optional, a priori known component to be included in the linear predictor during fitting.
n.samples	number of good subsets to fit. It can be a numeric value or "all".
max.samples	maximal number of subsets to fit. By default is set to twice n.samples.
epsilon	positive convergence tolerance epsilon. See glm.control for details.
maxit	integer giving the maximal number of IWLS iterations. See glm.control for details.
trace	logical, if TRUE a message is printed for every ten iterations completed during the search.

Details

This function is used by fwdglm to select the starting subset for the forward search. For this reason, users do not generally need to use it.

Value

The function returns a list with the following components:

bsb	a vector giving the best subset found
dev.res	a vector giving the deviance residuals for all the observations
message	a short message about the status of the algorithm
model	the model provided by glm.fit using the units in the best subset found

Author(s)

Originally written for S-Plus by: Kjell Konis [e-mail] and Marco Riani [e-mail]
Ported to R by Luca Scrucca [e-mail]

References

Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer, Chapter 6.

See Also

fwdglm, fwdlm, lmsreg, fwdsco.

forward documentation built on May 9, 2022, 9:05 a.m.