heplot.candisc {candisc} R Documentation

## Canonical Discriminant HE plots

### Description

These functions plot ellipses (or ellipsoids in 3D) in canonical discriminant space representing the hypothesis and error sums-of-squares-and-products matrices for terms in a multivariate linear model. They provide a low-rank 2D (or 3D) view of the effects for that term in the space of maximum discrimination.

### Usage

## S3 method for class 'candisc'
heplot(
mod,
which = 1:2,
scale,
asp = 1,
var.col = "blue",
var.lwd = par("lwd"),
var.cex = par("cex"),
var.pos,
rev.axes = c(FALSE, FALSE),
prefix = "Can",
suffix = TRUE,
terms = mod$term, ... )  ### Arguments  mod A candisc object for one term in a mlm which A numeric vector containing the indices of the canonical dimensions to plot. scale Scale factor for the variable vectors in canonical space. If not specified, the function calculates one to make the variable vectors approximately fill the plot window. asp Aspect ratio for the horizontal and vertical dimensions. The defaults, asp=1 for heplot.candisc and asp="iso" for heplot3d.candisc ensure equal units on all axes, so that angles and lengths of variable vectors are interpretable. As well, the standardized canonical scores are uncorrelated, so the Error ellipse (ellipsoid) should plot as a circle (sphere) in canonical space. For heplot3d.candisc, use asp=NULL to suppress this transformation to iso-scaled axes. var.col Color for variable vectors and labels var.lwd Line width for variable vectors var.cex Text size for variable vector labels var.pos Position(s) of variable vector labels wrt. the end point. If not specified, the labels are out-justified left and right with respect to the end points. rev.axes Logical, a vector of length(which). TRUE causes the orientation of the canonical scores and structure coefficients to be reversed along a given axis. prefix Prefix for labels of canonical dimensions. suffix Suffix for labels of canonical dimensions. If suffix=TRUE the percent of hypothesis (H) variance accounted for by each canonical dimension is added to the axis label. terms Terms from the original mlm whose H ellipses are to be plotted in canonical space. The default is the one term for which the canonical scores were computed. If terms=TRUE, all terms are plotted. ... Arguments to be passed down to heplot or heplot3d ### Details The generalized canonical discriminant analysis for one term in a mlm is based on the eigenvalues, \lambda_i, and eigenvectors, V, of the H and E matrices for that term. This produces uncorrelated canonical scores which give the maximum univariate F statistics. The canonical HE plot is then just the HE plot of the canonical scores for the given term. For heplot3d.candisc, the default asp="iso" now gives a geometrically correct plot, but the third dimension, CAN3, is often small. Passing an expanded range in zlim to heplot3d usually helps. ### Value heplot.candisc returns invisibly an object of class "heplot", with coordinates for the various hypothesis ellipses and the error ellipse, and the limits of the horizontal and vertical axes. Similarly, heploted.candisc returns an object of class "heplot3d". ### Author(s) Michael Friendly and John Fox ### References Friendly, M. (2006). Data Ellipses, HE Plots and Reduced-Rank Displays for Multivariate Linear Models: SAS Software and Examples Journal of Statistical Software, 17(6), 1-42. https://www.jstatsoft.org/v17/i06/ doi:10.18637/jss.v017.i06 Friendly, M. (2007). HE plots for Multivariate General Linear Models. Journal of Computational and Graphical Statistics, 16(2) 421–444. http://datavis.ca/papers/jcgs-heplots.pdf, doi:10.1198/106186007X208407. ### See Also candisc, candiscList, heplot, heplot3d, aspect3d ### Examples  ## Pottery data, from car package data(Pottery, package = "carData") pottery.mod <- lm(cbind(Al, Fe, Mg, Ca, Na) ~ Site, data=Pottery) pottery.can <-candisc(pottery.mod) heplot(pottery.can, var.lwd=3) if(requireNamespace("rgl")){ heplot3d(pottery.can, var.lwd=3, scale=10, zlim=c(-3,3), wire=FALSE) } # reduce example for CRAN checks time grass.mod <- lm(cbind(N1,N9,N27,N81,N243) ~ Block + Species, data=Grass) grass.can1 <-candisc(grass.mod,term="Species") grass.canL <-candiscList(grass.mod) heplot(grass.can1, scale=6) heplot(grass.can1, scale=6, terms=TRUE) heplot(grass.canL, terms=TRUE, ask=FALSE) heplot3d(grass.can1, wire=FALSE) # compare with non-iso scaling rgl::aspect3d(x=1,y=1,z=1) # or, # heplot3d(grass.can1, asp=NULL) ## Can't run this in example # rgl::play3d(rgl::spin3d(axis = c(1, 0, 0), rpm = 5), duration=12) # reduce example for CRAN checks time ## FootHead data, from heplots package library(heplots) data(FootHead) # use Helmert contrasts for group contrasts(FootHead$group) <- contr.helmert

foot.mod <- lm(cbind(width, circum,front.back,eye.top,ear.top,jaw)~group, data=FootHead)
foot.can <- candisc(foot.mod)
heplot(foot.can, main="Candisc HE plot",
hypotheses=list("group.1"="group1","group.2"="group2"),
col=c("red", "blue", "green3", "green3" ), var.col="red")



[Package candisc version 0.9.0 Index]