| var.rdf {bpca} | R Documentation |
Computes the diagnostic of poor graphical correlations projected by biplot
according to an arbitrary limit.
var.rdf(x, var.rb, limit)
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. |
A data.frame of poor graphical correlations projected by biplot.
This function is mainly for internal use in the bpca package, and may not remain available (unless we see a good reason).
Jose Claudio Faria (joseclaudio.faria@gmail.com)
and
Clarice Garcia Borges Demetrio (clarice@esalq.usp.br)
bpca.
##
## Example 1
## Diagnostic of gabriel1971 dataset representation
##
library(bpca)
bp1 <- bpca(gabriel1971, meth='hj', var.rb=TRUE)
res <- var.rdf(gabriel1971, bp1$var.rb, lim=3)
res
class(res)
##
## Example 2
## Diagnostic of gabriel1971 dataset representation with var.rd parameter
##
bp2 <- bpca(gabriel1971, meth='hj', lambda.end=2,
var.rb=TRUE, var.rd=TRUE, limit=3)
plot(bp2, var.factor=2)
bp2$var.rd
bp2$eigenvectors
# Graphical visualization of the importance of the variables not contemplated
# in the reduction
plot(bpca(gabriel1971, meth='hj', lambda.ini=3, lambda.end=4), main='hj')
# Interpretation:
# RUR followed by CRISTIAN contains information dimensions that
# wasn't contemplated by the biplot reduction (PC3).
# Between all, RUR followed by CRISTIAN, variables are the most poor represented
# by a 2d biplot.
##
## Example 3
## Diagnostic of iris dataset representation with var.rd parameter
##
bp3 <- bpca(iris[-5], var.rb=TRUE, var.rd=TRUE, limit=3)
plot(bp3, obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)], var.factor=.3)
bp3$var.rd
bp3$eigenvectors
# Graphical diagnostic
plot(bpca(iris[-5], lambda.ini=3, lambda.end=4),
obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)], var.factor=.6)
# Interpretation:
# Sepal.length followed by Petal.Width contains information in dimensions
# (PC3 - the PC3 is, essentially, a contrast among both) that wasn't fully
# contemplated by the biplot reduction (PC1 and PC2) .
# Therefore, between all variables, they have the most poor representation by a
# 2d biplot.
bp4 <- bpca(iris[-5], lambda.end=3, var.rb=TRUE, var.rd=TRUE, limit=2)
plot(bp4, obj.names=FALSE,
obj.pch=c('+', '-', '*')[unclass(iris$Species)],
obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)], obj.cex=1)
bp4$var.rd
bp4$eigenvectors
round(bp3$var.rb, 2)
round(cor(iris[-5]), 2)
# Good representation of all variables with a 3d biplot!