plot.fwdglm: Forward Search in Generalized Linear Models In forward: Robust Analysis using Forward Search

Description

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

Usage

 1 2 3 4 ## S3 method for class '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: deviance residuals 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(s)

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.