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,
  groups,
  group.axis = TRUE,
  id.weight = 0.7,
  id.pos = 4,
  pch = 19,
  col = palette()[1],
  cex = par("cex"),
  segments = FALSE,
  xlab = "Case index",
  ylab = "Weight in robust MLM",
  ...
)

Arguments

x: A "robmlm" object
labels: Observation labels for point identification. If not specified, uses rownames from the original data
groups: Optional grouping variable, a factor with length equal to the number of observations, used to identify groups in the plot.
group.axis: Logical; whether to draw an axis at the top identifying the groups. Not drawn if groups is missing.
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). Multiple colors can be specified so that each point can be given its own color. If there are fewer colors than points they are recycled in the standard fashion.
cex: Point character size(s)
segments: logical; if TRUE, draw line segments from 1.0 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$epoch, segments=TRUE)
axis(side=3, at=15+seq(0,120,30), labels=levels(Skulls$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$Dx, segments=TRUE)
# label the groups 
ctr <- split(seq(nrow(SocialCog)), SocialCog$Dx) |> lapply(mean)
axis(side = 3, at=ctr, labels = names(ctr), cex.axis=1.2)


# use the groups arg
colors = c("red", "darkgreen", "blue") 
ids <- plot(SC.rmod, 
     groups=SocialCog$Dx, 
     col = colors,
     pch = 15:17,
     segments=TRUE)


# the cases labeled and their weights
ids
#>        15        42       119       120       122 
#> 0.0000000 0.6788939 0.5034684 0.3506323 0.5966688