Canonical discriminant analyses

candiscList performs a generalized canonical discriminant analysis for all terms in a multivariate linear model (i.e., an mlm object), computing canonical scores and vectors.

Usage

candiscList(mod, ...)

# S3 method for mlm
candiscList(mod, type = "2", manova, ndim, ...)

# S3 method for candiscList
print(x, ...)

# S3 method for candiscList
summary(object, ...)

# S3 method for candiscList
plot(x, term, ask = interactive(), graphics = TRUE, ...)

Arguments

mod

An mlm object, such as computed by lm() with a multivariate response

...

arguments to be passed down.

type

type of test for the model term, one of: "II", "III", "2", or "3"

manova

the Anova.mlm object corresponding to mod. Normally, this is computed internally by Anova(mod)

ndim

Number of dimensions to store in the means, structure, scores and coeffs.* components. The default is the rank of the H matrix for the hypothesis term.

object, x

A candiscList object

term

The name of one term to be plotted for the plot method. If not specified, one candisc plot is produced for each term in the mlm object.

ask

If TRUE (the default, when running interactively), a menu of terms is presented; if ask is FALSE, canonical plots for all terms are produced.

graphics

if TRUE (the default, when running interactively), then the menu of terms to plot is presented in a dialog box rather than as a text menu.

Value

An object of class candiscList which is a list of

"candisc" objects for the terms in the mlm.

Methods (by class)

• candiscList(mlm): "mlm" method.

Methods (by generic)

• print(candiscList): print() method for "candiscList" objects.

• summary(candiscList): summary() method for "candiscList" objects.

• plot(candiscList): plot() method for "candiscList" objects.

See also

candisc, heplot, heplot3d

Author

Michael Friendly and John Fox

Examples

grass.mod <- lm(cbind(N1,N9,N27,N81,N243) ~ Block + Species, data=Grass)

grass.canL <-candiscList(grass.mod)
names(grass.canL)
#> [1] "Block"   "Species"
names(grass.canL$Species)
#>  [1] "dfh"         "dfe"         "eigenvalues" "canrsq"      "pct"        
#>  [6] "rank"        "ndim"        "means"       "factors"     "term"       
#> [11] "terms"       "coeffs.raw"  "coeffs.std"  "structure"   "scores"     

if (FALSE) {
print(grass.canL)
}
plot(grass.canL, type="n", ask=FALSE)
#> Vector scale factor set to 4.527

#> Vector scale factor set to 5.545

heplot(grass.canL$Species, scale=6)

heplot(grass.canL$Block, scale=2)