var.rdf {bpca} R Documentation

## Diagnostic of Projected Correlations

### Description

Computes the diagnostic of poor graphical correlations projected by biplot according to an arbitrary ‘⁠limit⁠’.

### Usage

  var.rdf(x,
var.rb,
limit)


### Arguments

 x A given object of the classe data.frame or matrix. var.rb A given object of the class matrix with the projected correlations by biplot. limit A vector giving the percentual limit to define poor representation of variables.

### Value

A data.frame of poor graphical correlations projected by biplot.

### Note

This function is mainly for internal use in the bpca package, and may not remain available (unless we see a good reason).

### Author(s)

Faria, J. C.
Allaman, I. B.
Demétrio C. G. B.

### See Also

bpca.

### Examples

##
## Example 1
## Diagnostic of gabriel1971 dataset representation
##

oask <- devAskNewPage(dev.interactive(orNone=TRUE))

bp1 <- bpca(gabriel1971,
meth='hj',
var.rb=TRUE)

(res <- var.rdf(gabriel1971,
bp1$var.rb, lim=3)) class(res) ## ## Example 2 ## Diagnostic of gabriel1971 dataset representation with var.rd parameter ## bp2 <- bpca(gabriel1971, meth='hj', var.rb=TRUE, var.rd=TRUE, limit=3) plot(bp2, var.factor=2) bp2$var.rd

bp2$eigenvectors # Graphical visualization of the importance of the variables not contemplated # in the reduction plot(bpca(gabriel1971, meth='hj', d=3:4), main='hj', xlim=c(-1,1), ylim=c(-1,1)) # Interpretation: # RUR followed by CRISTIAN contains information dimensions that # wasn't contemplated by the biplot reduction (PC3). # Between all, RUR followed by CRISTIAN, variables are the most poor represented # by a 2d biplot. ## Not run: ## ## Example 3 ## Diagnostic of iris dataset representation with var.rd parameter ## bp3 <- bpca(iris[-5], var.rb=TRUE, var.rd=TRUE, limit=3) plot(bp3, obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)],
var.factor=.3)

bp3$var.rd bp3$eigenvectors

# Graphical diagnostic
plot(bpca(iris[-5],
d=3:4),
obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)], obj.names=FALSE, var.factor=.6, xlim=c(-2,3), ylim=c(-1,1)) # Interpretation: # Sepal.length followed by Petal.Width contains information in dimensions # (PC3 - the PC3 is, essentially, a contrast among both) that wasn't fully # contemplated by the biplot reduction (PC1 and PC2) . # Therefore, between all variables, they have the most poor representation by a # 2d biplot. bp4 <- bpca(iris[-5], d=1:3, var.rb=TRUE, var.rd=TRUE, limit=2) plot(bp4, obj.names=FALSE, obj.pch=c('+', '-', '*')[unclass(iris$Species)],
obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)], obj.cex=1, xlim=c(-5,5), ylim=c(-5,5), zlim=c(-5,5), var.factor=.5) bp4$var.rd
bp4$eigenvectors round(bp3$var.rb, 2)

round(cor(iris[-5]), 2)

# Good representation of all variables with a 3d biplot!

## End(Not run)

devAskNewPage(oask)


[Package bpca version 1.3-6 Index]