Sym.PCA.Hist.PCA.k.plot: Sym.PCA.Hist.PCA.k.plot

In RSDA: R to Symbolic Data Analysis

Sym.PCA.Hist.PCA.k.plot

Description

Sym.PCA.Hist.PCA.k.plot

Usage

Sym.PCA.Hist.PCA.k.plot(
  data.sym.df,
  title.graph,
  concepts.name,
  title.x,
  title.y,
  pca.axes
)

Arguments

data.sym.df	Bins's projections
title.graph	Plot title
concepts.name	Concepts names
title.x	Label of axis X
title.y	Label of axis Y
pca.axes	Principal Component

Value

Concepts projected onto the Principal component chosen

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$sym.hist.matrix.PCA,3)
ACER.p1<-Sym.PCA.Hist.PCA.k.plot(data.sym.df = pca.hist$Bins.df,
                                    title.graph = " ",
                                    concepts.name = c("ACER"),
                                    title.x = "First Principal Component (84.83%)",
                                    title.y = "Frequency",
                                    pca.axes = 1)

ACER.p1

## End(Not run)

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