varOrder {candisc}  R Documentation 
Order variables according to canonical structure or other criteria
Description
The varOrder
function implements some features of “effect
ordering” (Friendly & Kwan (2003) for variables in a multivariate
data display to make the displayed relationships more coherent.
This can be used in pairwise HE plots, scatterplot matrices, parallel coordinate plots, plots of multivariate means, and so forth.
For a numeric data frame, the most useful displays often order variables according to the angles of variable vectors in a 2D principal component analysis or biplot. For a multivariate linear model, the analog is to use the angles of the variable vectors in a 2D canonical discriminant biplot.
Usage
varOrder(x, ...)
## S3 method for class 'mlm'
varOrder(
x,
term,
variables,
type = c("can", "pc"),
method = c("angles", "dim1", "dim2", "alphabet", "data", "colmean"),
names = FALSE,
descending = FALSE,
...
)
## S3 method for class 'data.frame'
varOrder(
x,
variables,
method = c("angles", "dim1", "dim2", "alphabet", "data", "colmean"),
names = FALSE,
descending = FALSE,
...
)
## Default S3 method:
varOrder(x, ...)
Arguments
x 
A multivariate linear model or a numeric data frame 
... 
Arguments passed to methods 
term 
For the 
variables 
indices or names of the variables to be ordered; defaults to all response variables an MLM or all numeric variables in a data frame. 
type 
For an MLM, 
method 
One of

names 
logical; if 
descending 
If 
Value
A vector of integer indices of the variables or a character vector of their names.
Methods (by class)

varOrder(mlm)
:"mlm"
method. 
varOrder(data.frame)
:"data.frame"
method. 
varOrder(default)
:"default"
method.
Author(s)
Michael Friendly
References
Friendly, M. & Kwan, E. (2003). Effect Ordering for Data Displays, Computational Statistics and Data Analysis, 43, 509539. doi:10.1016/S01679473(02)002906
Examples
data(Wine, package="candisc")
Wine.mod < lm(as.matrix(Wine[, 1]) ~ Cultivar, data=Wine)
Wine.can < candisc(Wine.mod)
plot(Wine.can, ellipse=TRUE)
# pairs.mlm HE plot, variables in given order
pairs(Wine.mod, fill=TRUE, fill.alpha=.1, var.cex=1.5)
order < varOrder(Wine.mod)
pairs(Wine.mod, variables=order, fill=TRUE, fill.alpha=.1, var.cex=1.5)