mixOmics (version 2.6)

plotVar: Plot of Variables

Description

This function provides variables representation for (regularized) CCA and (sparse) PLS regression.

Usage

## S3 method for class 'rcc':
plotVar(object, comp = 1:2, rad.in = 0.5, cutoff = NULL,
        X.label = FALSE, Y.label = FALSE,
        pch = NULL, cex = NULL, col = NULL, font = NULL, ...)

## S3 method for class 'pls':
plotVar(object, comp = 1:2, rad.in = 0.5, 
        X.label = FALSE, Y.label = FALSE, keep.var = FALSE, 
        pch = NULL, cex = NULL, col = NULL, font = NULL, ...)	

## S3 method for class 'spls':
plotVar(object, comp = 1:2, rad.in = 0.5, 
        X.label = FALSE, Y.label = FALSE, keep.var = FALSE, 
        pch = NULL, cex = NULL, col = NULL, font = NULL, ...)

Arguments

object

object of class inheriting from "rcc", "pls" or "spls".

comp

integer vector of length two. The components that will be used on the horizontal and the vertical axis respectively to project the variables.

rad.in

numeric between 0 and 1, the radius of the inner circle. Defaults to 0.5.

cutoff

numeric between 0 and 1. Variables with correlations below this cutoff in absolute value are not plotted (see Details).

X.label, Y.label

either a character vector of names for the $X$- and $Y$-variables or FALSE for no names. If TRUE, the columns names of the matrice are used as labels.

col

character or integer vector of colors for plotted character and symbols. See Details.

pch

plot character. A vector of single characters or integers. See points for all alternatives.

cex

numeric vector of character expansion sizes for the plotted character and symbols.

font

numeric vector of font to be used. See par for details.

keep.var

boolean. If TRUE only the variables with loadings not zero are plotted (as selected by spls). Defaults to FALSE.

...

not used currently.

encoding

latin1

Details

plotVar produce a "correlation circle", i.e. the correlations between each variable and the selected components are plotted as scatter plot, with concentric circles of radius one et radius given by rad.in. Each point corresponds to a variable. For (sparse) PLS regression the components correspond to the $X$-variates components and for (regularized) CCA the components correspond to the bisector vector components between $X$- and $Y$-variates. The arguments col, pch, cex and font can be either vectors of length two or a list with two vector components of length $p$ and $q$ respectively, where $p$ is the number of $X$-variables and $q$ is the number of $Y$-variables. In the first case, the first and second component of the vector determine the graphics attributes for the $X$- and $Y$-variables respectively. Otherwise, multiple arguments values can be specified so that each point (variable) can be given its own graphic attributes. In this case, the first component of the list correspond to the $X$ attributs and the second component correspond to the $Y$ attributs. Default values exist for this arguments.

See Also

plot3dVar, cim, network, par.

Examples

## variable representation for objects of class 'rcc'
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
nutri.res <- rcc(X, Y, lambda1 = 0.064, lambda2 = 0.008)

plotVar(nutri.res) #(default)

plotVar(nutri.res, comp = 1:2, cutoff = 0.5, 
        X.label = TRUE, Y.label = TRUE)

## variable representation for objects of class 'pls' or 'spls'
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50), 
                      keepY = c(10, 10, 10))
	
plotVar(toxicity.spls, keep.var = TRUE, Y.label = TRUE)