plot.fwdlm: Forward Search in Linear Regression In forward: Robust Analysis using Forward Search

 plot.fwdlm R Documentation

Forward Search in Linear Regression

Description

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

Usage

```## S3 method for class 'fwdlm'
plot(x, which.plots = 1:10, squared = FALSE, scaled = TRUE,
ylim = NULL, xlim = NULL, th.Res = 2, th.Lev = 0.25, sig.Tst = 2.58,
labels.in.plot = TRUE, ...)
```

Arguments

 `x` a `"fwdlm"` object. `which.plots` select which plots to draw, by default all. Each graph is addressed by an integer: scaled residuals leverages maximum studentized residuals minimum deletion residuals coefficients statistics forward Cook's distances modified forward Cook's distances S^2 values R^2 values `squared` logical, if `TRUE` plots squared 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 (on the scale of the t statistics) used to draw the confidence interval on the plot of the t statistics. `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, Chapters 2-3.

`fwdlm`, `fwdsco`, `fwdglm`.

Examples

```library(MASS)
data(forbes)
plot(forbes)
mod <- fwdlm(100*log10(pres) ~ bp, data=forbes)
summary(mod)
## Not run: plot(mod)
```

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