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)

Jose Claudio Faria (joseclaudio.faria@gmail.com)
and
Clarice Garcia Borges Demetrio (clarice@esalq.usp.br)

See Also

bpca.

Examples

  ##
  ## 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!

[Package bpca version 1.0.2 Index]