heplot.candisc {candisc} | R Documentation |
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.
## S3 method for class 'candisc': heplot(mod, which = 1:2, scale, asp = 1, var.col = "blue", var.lwd = par("lwd"), prefix = "Can", suffix = TRUE, terms = mod$term, ...) ## S3 method for class 'candisc': heplot3d(mod, which = 1:3, scale, asp="iso", var.col = "blue", var.lwd=par("lwd"), prefix = "Can", suffix = FALSE, terms = mod$term, ...)
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 |
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 |
The generalized canonical discriminant analysis for on 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.
No useful value; used for the side-effect of producing a canonical HE plot.
Michael Friendly and John Fox
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. http://www.jstatsoft.org/v17/i06/
Friendly, M. (2007). HE plots for Multivariate General Linear Models. Journal of Computational and Graphical Statistics, 16 (2), 421-444. http://www.math.yorku.ca/SCS/Papers/jcgs-heplots.pdf
candisc
, candiscList
,
heplot
, heplot3d
, aspect3d
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) # compare with non-iso scaling aspect3d(x=1,y=1,z=1) # or, # heplot3d(grass.can1, asp=NULL) ## Pottery data, from car package pottery.mod <- lm(cbind(Al, Fe, Mg, Ca, Na) ~ Site, data=Pottery) (pottery.can <-candisc(pottery.mod)) heplot(pottery.can, var.lwd=3) heplot3d(pottery.can, var.lwd=3, scale=10, zlim=c(-3,3)) ## Not run: play3d(spin3d(axis = c(1, 0, 0), rpm = 5), duration=12) ## End(Not run)