plot.lts {rrcov}R Documentation

Robust Regression Diagnostic Plots

Description

Four plots (selectable by 'which') are currently provided: a plot of the standardized residuals versus their index, a plot of the standardized residuals versus fitted values, a Normal Q-Q plot of the standardized residuals, and a regression diagnostic plot (standardized residuals versus robust distances of the predictor variables).

Usage

    ## S3 method for class 'lts':
    plot.lts(x, which = c("all","rqq","rindex","rfit","rdiag"), classic=FALSE, ask=(which=="all" && dev.interactive()), id.n=3, ...)
    

Arguments

x a lts object, typically result of ltsReg.
which Which plot to show? See Details for description of the options. Default is which="all".
classic whether to plot the classical plots too. Default is classic=FALSE.
ask logical; if 'TRUE', the user is asked before each plot, see 'par(ask=.)'. Default is ask = which=="all" && dev.interactive().
id.n Number of observations to identify by a label starting with the most extreme. Default is id.n = 3.
... other parameters to be passed through to plotting functions.

Details

This function produces several plots based on the robust and classical regression estimates. Which of them to select is specified by the attribute which. The possible options are:

rqq - Normal Q-Q plot of the standardized residuals;

rindex - plot of the standardized residuals versus their index;

rfit - plot of the standardized residuals versus fitted values;

tolellipse - regression diagnostic plot.

The regression diagnostic Plot, introduced by Rousseeuw and van Zomeren (1990), displays the standardized residuals versus robust distances. Following Rousseeuw and van Zomeren (1990), the horizontal dashed lines are located at +2.5 and -2.5 and the vertical line is located at the upper 0.975 percent point of the chi-squared distribution with p degrees of freedom.

References

P. J. Rousseeuw and van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association 85, 633-639.

P. J. Rousseeuw and K. van Driessen (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223.

See Also

covPlot

Examples


data(hbk)
lts <- ltsReg(hbk.x,hbk.y)
plot(lts, which="rqq")


[Package rrcov version 0.2-5 Index]