plot.robmlm {heplots}

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)