varOrder: Order variables according to canonical structure or other...

In candisc: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

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, ...)

Arguments

x	A multivariate linear model or a numeric data frame
term	For the mlm method, one term in the model for which the canonical structure coefficients are found.
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, type="can" uses the canonical structure coefficients for the given term; type="pc" uses the principal component variable eigenvectors.
method	One of c("angles", "dim1", "dim2", "alphabet", "data", "colmean") giving the effect ordering method. "angles" Orders variables according to the angles their vectors make with dimensions 1 and 2, counter-clockwise starting from the lower-left quadrant in a 2D biplot or candisc display. "dim1" Orders variables in increasing order of their coordinates on dimension 1 "dim2" Orders variables in increasing order of their coordinates on dimension 2 "alphabet" Orders variables alphabetically "data" Uses the order of the variables in the data frame or the list of responses in the MLM "colmean" Uses the order of the column means of the variables in the data frame or the list of responses in the MLM
names	logical; if TRUE the effect ordered names of the variables are returned; otherwise, their indices in variables are returned.
descending	If TRUE, the ordered result is reversed to a descending order.
...	Arguments passed to methods

Value

A vector of integer indices of the variables or a character vector of their names.

Author(s)

Michael Friendly

References

Friendly, M. & Kwan, E. (2003). Effect Ordering for Data Displays, Computational Statistics and Data Analysis, 43, 509-539. http://dx.doi.org/10.1016/S0167-9473(02)00290-6

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)

candisc documentation built on May 2, 2019, 6:37 p.m.