plot.fwdglm: Forward Search in Generalized Linear Models

Description

This function plots the results of a forward search analysis in generalized linear models.

Usage

# S3 method for fwdglm
plot(x, which.plots = 1:11, squared = FALSE, scaled =FALSE, 
     ylim = NULL, xlim = NULL, th.Res = 4, th.Lev = 0.25, sig.Tst =2.58, 
     sig.score = 1.96, plot.pf = FALSE, labels.in.plot = TRUE, ...)

Arguments

x

a "fwdglm" object.

which.plots

select which plots to draw, by default all. Each graph is addressed by an integer:

leverages
maximum deviance residuals
minimum deviance residuals
coefficients
t statistics, i.e. coef.est/SE(coef.est)
likelihood matrix: deviance, deviance explained, pseudo R-squared, dispersion parameter
score statistic for the goodness of link test
forward Cook's distances
modified forward Cook's distances
weights used at each step of the forward search for the units included

squared

logical, if TRUE plots squared deviance residuals.

scaled

logical, if TRUE plots scaled coefficient estimates.

ylim

a two component vector for the min and max of the y axis.

xlim

a two component vector for the min and max of the x axis.

th.Res

numerical, a threshold for labelling the residuals.

th.Lev

numerical, a threshold for labelling the leverages.

sig.Tst

numerical, a value used to draw the confidence interval on the plot of the t statistics.

sig.score

numerical, a value used to draw the confidence interval on the plot of the score test statistic.

plot.pf

logical, in case of binary response if TRUE graphs contain all the step of the forward search, otherwise only those in which there is no perfect fit.

labels.in.plot

logical, if TRUE units are labelled in the plots when required.

...

further arguments passed to or from other methods.

Author

Originally written for S-Plus by: Kjell Konis kkonis@insightful.com and Marco Riani mriani@unipr.it
Ported to R by Luca Scrucca luca@stat.unipg.it

References

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

Examples

Run this code

if (FALSE) {
data(cellular)
mod <- fwdglm(y ~ as.factor(TNF) + as.factor(IFN), data=cellular, 
              family=poisson(log), nsamp=200)
summary(mod)
plot(mod)
}