sym.quantiles.PCA.plot: sym.quantiles.PCA.plot

In RSDA: R to Symbolic Data Analysis

sym.quantiles.PCA.plot

Description

sym.quantiles.PCA.plot

Usage

sym.quantiles.PCA.plot(
  histogram.PCA.r,
  concept.names,
  var.names,
  Title,
  axes.x.label,
  axes.y.label,
  label.name
)

Arguments

histogram.PCA.r	A quantil matrix
concept.names	Concept Name
var.names	Variables to plot
Title	Plot title
axes.x.label	Label of axis X
axes.y.label	Label of axis Y
label.name	Concept Variable

Value

3D plot

Author(s)

Jorge Arce Garro

Examples

## Not run: 
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
 M<-length(Hardwood.cols)
 N<-length(Hardwood.names)
 BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "PC 1 (84.83%)"
axes.y.label<- "PC 2 (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
plot.3D.HW<-sym.quantiles.PCA.plot(Hardwood.quantiles.PCA,
                                     concept.names,
                                     var.names,
                                     Title,
                                     axes.x.label,
                                     axes.y.label,
                                     label.name)

plot.3D.HW

## End(Not run)

RSDA documentation built on Nov. 10, 2023, 5:06 p.m.