# 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.

`fwdglm`, `fwdlm`, `fwdsco`.
 ```1 2 3 4 5 6 7 8 9``` ``` ## Not run: data(cellular) mod <- fwdglm(y ~ as.factor(TNF) + as.factor(IFN), data=cellular, family=poisson(log), nsamp=200) summary(mod) plot(mod) ## End(Not run) ```