plot.robmlm: Plot observation weights from a robust multivariate linear...

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Plot observation weights from a robust multivariate linear models

Description

Creates an index plot of the observation weights assigned in the last iteration of robmlm. Observations with low weights have large residual squared distances and are potential multivariate outliers with respect to the fitted model.

Usage

## S3 method for class 'robmlm'
plot(
  x,
  labels,
  id.weight = 0.7,
  id.pos = 4,
  pch = 19,
  col = palette()[1],
  cex = par("cex"),
  segments = FALSE,
  xlab = "Case index",
  ylab = "Weight in robust MANOVA",
  ...
)

Arguments

x	A "robmlm" object
labels	Observation labels; if not specified, uses rownames from the original data
id.weight	Threshold for identifying observations with small weights
id.pos	Position of observation label relative to the point
pch	Point symbol(s); can be a vector of length equal to the number of observations in the data frame
col	Point color(s)
cex	Point character size(s)
segments	logical; if TRUE, draw line segments from 1.o down to the point
xlab	x axis label
ylab	y axis label
...	other arguments passed to plot

Value

Returns invisibly the weights for the observations labeled in the plot

Author(s)

Michael Friendly

See Also

robmlm

Examples

data(Skulls)
sk.rmod <- robmlm(cbind(mb, bh, bl, nh) ~ epoch, data=Skulls)
plot(sk.rmod, col=Skulls$epoch)
axis(side=3, at=15+seq(0,120,30), labels=levels(Skulls$epoch), cex.axis=1)

# Pottery data

data(Pottery, package = "carData")
pottery.rmod <- robmlm(cbind(Al,Fe,Mg,Ca,Na)~Site, data=Pottery)
plot(pottery.rmod, col=Pottery$Site, segments=TRUE)

# SocialCog data

data(SocialCog)
SC.rmod <- robmlm(cbind( MgeEmotions, ToM, ExtBias, PersBias) ~ Dx,
               data=SocialCog)
plot(SC.rmod, col=SocialCog$Dx, segments=TRUE)

heplots documentation built on May 29, 2024, 9:24 a.m.