heplots: plot.robmlm – R documentation

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plot.robmlm

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

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

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

Visualizing Hypothesis Tests in Multivariate Linear Models

v1.3-8

GPL (>= 2)

Authors

John Fox [aut], Michael Friendly [aut, cre], Georges Monette [ctb], Phil Chalmers [ctb]

Initial release

2021-01-20