heplots (version 1.3-8)

covEllipses: Draw classical and robust covariance ellipses for one or more groups

Description

The function draws covariance ellipses for one or more groups and optionally for the pooled total sample. It uses either the classical product-moment covariance estimate, or a robust alternative, as provided by cov.rob. Provisions are provided to do this for more than two variables, in a scatterplot matrix format.

Usage

covEllipses(x, ...)

# S3 method for boxM covEllipses(x, ...)

# S3 method for data.frame covEllipses(x, group, pooled = TRUE, method = c("classical", "mve", "mcd"), ...)

# S3 method for matrix covEllipses(x, group, pooled = TRUE, method = c("classical", "mve", "mcd"), ...)

# S3 method for default covEllipses(x, means, df, labels = NULL, variables = 1:2, level = 0.68, segments = 60, center = FALSE, center.pch = "+", center.cex = 2, col = getOption("heplot.colors", c("red", "blue", "black", "darkgreen", "darkcyan", "brown", "magenta", "darkgray")), lty = 1, lwd = 2, fill = FALSE, fill.alpha = 0.3, label.pos = 0, xlab, ylab, vlabels, var.cex=2, main = "", xlim, ylim, axes = TRUE, offset.axes, add = FALSE, ...)

Arguments

x

The generic argument. For the default method, this is a list of covariance matrices. For the data.frame and matrix methods, this is a numeric matrix of two or more columns supplying the variables to be analyzed.

group

a factor defining groups, or a vector of length n=nrow(x) doing the same. If missing, a single covariance ellipse is drawn.

pooled

Logical; if TRUE, the pooled covariance matrix for the total sample is also computed and plotted

method

the covariance method to be used: classical product-moment ("classical"), or minimum volume ellipsoid ("mve"), or minimum covariance determinant ("mcd").

means

For the default method, a matrix of the means for all groups (followed by the grand means, if pooled=TRUE). Rows are the groups, and columns are the variables. It is assumed that the means have column names corresponding to the variables in the covariance matrices.

df

For the default method, a vector of the degrees of freedom for the covariance matrices

labels

Either a character vector of labels for the groups, or TRUE, indicating that group labels are taken as the names of the covariance matrices. Use labels="" to suppress group labels, e.g., when add=TRUE

variables

indices or names of the response variables to be plotted; defaults to 1:2. If more than two variables are supplied, the function plots all pairwise covariance ellipses in a scatterplot matrix format.

level

equivalent coverage of a data ellipse for normally-distributed errors, defaults to 0.68.

segments

number of line segments composing each ellipse; defaults to 40.

center

If TRUE, the covariance ellipses are centered at the centroid.

center.pch

character to use in plotting the centroid of the data; defaults to "+".

center.cex

size of character to use in plotting the centroid of the data; defaults to 2.

col

a color or vector of colors to use in plotting ellipses --- recycled as necessary A single color can be given, in which case it is used for all ellipses. For convenience, the default colors for all plots produced in a given session can be changed by assigning a color vector via options(heplot.colors =c(...). Otherwise, the default colors are c("red", "blue", "black", "darkgreen", "darkcyan", "magenta", "brown", "darkgray").

lty

vector of line types to use for plotting the ellipses; the first is used for the error ellipse, the rest --- possibly recycled --- for the hypothesis ellipses; a single line type can be given. Defaults to 2:1.

lwd

vector of line widths to use for plotting the ellipses; the first is used for the error ellipse, the rest --- possibly recycled --- for the hypothesis ellipses; a single line width can be given. Defaults to 1:2.

fill

A logical vector indicating whether each ellipse should be filled or not. The first value is used for the error ellipse, the rest --- possibly recycled --- for the hypothesis ellipses; a single fill value can be given. Defaults to FALSE for backward compatibility. See Details below.

fill.alpha

Alpha transparency for filled ellipses, a numeric scalar or vector of values within [0,1], where 0 means fully transparent and 1 means fully opaque. Defaults to 0.3.

label.pos

Label position, a vector of integers (in 0:4) or character strings (in c("center", "bottom", "left", "top", "right")) use in labeling ellipses, recycled as necessary. Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above and to the right of the max/min coordinates of the ellipse; the value 0 specifies the centroid of the ellipse object. The default, label.pos=NULL uses the correlation of the ellipse to determine "top" (r>=0) or "bottom" (r<0).

xlab

x-axis label; defaults to name of the x variable.

ylab

y-axis label; defaults to name of the y variable.

vlabels

Labels for the variables can also be supplied through this argument, which is more convenient when length(variables) > 2.

var.cex

character size for variable labels in the pairs plot

main

main plot label; defaults to "", and presently has no effect.

xlim

x-axis limits; if absent, will be computed from the data.

ylim

y-axis limits; if absent, will be computed from the data.

axes

Whether to draw the x, y axes; defaults to TRUE

offset.axes

proportion to extend the axes in each direction if computed from the data; optional.

add

if TRUE, add to the current plot; the default is FALSE. This argument is has no effect when more than two variables are plotted.

Other arguments passed to the default method for plot, text, and points

Value

Nothing is returned. The function is used for its side-effect of producing a plot.

Details

These plot methods provide one way to visualize possible heterogeneity of within-group covariance matrices in a one-way MANOVA design. When covariance matrices are nearly equal, their covariance ellipses should all have the same shape. When centered at a common mean, they should also all overlap.

The can also be used to visualize the difference between classical and robust covariance matrices.

See Also

heplot, boxM,

cov.rob

Examples

# NOT RUN {
data(iris)

# compare classical and robust covariance estimates
covEllipses(iris[,1:4], iris$Species)
covEllipses(iris[,1:4], iris$Species, fill=TRUE, method="mve", add=TRUE, labels="")

# method for a boxM object	
x <- boxM(iris[, 1:4], iris[, "Species"])
x
covEllipses(x, fill=c(rep(FALSE,3), TRUE) )
covEllipses(x, fill=c(rep(FALSE,3), TRUE), center=TRUE, label.pos=1:4 )

# method for a list of covariance matrices
cov <- c(x$cov, pooled=list(x$pooled))
df <- c(table(iris$Species)-1, nrow(iris)-3)
covEllipses(cov, x$means, df, label.pos=3, fill=c(rep(FALSE,3), TRUE))
 
covEllipses(cov, x$means, df, label.pos=3, fill=c(rep(FALSE,3), TRUE), center=TRUE)

# scatterplot matrix version
covEllipses(iris[,1:4], iris$Species, 
	fill=c(rep(FALSE,3), TRUE), variables=1:4, 
	fill.alpha=.1)

# }