Plot observation weights from a robust multivariate linear models

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

Michael Friendly

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)