R: Plots for Hierarchical Classification on Principle Components...
plot.HCPC
R Documentation
Plots for Hierarchical Classification on Principle Components (HCPC) results
Description
Plots graphs from a HCPC result: tree, barplot of inertia gains and
first factor map with or without the tree, in 2 or 3 dimensions.
Usage
## S3 method for class 'HCPC'
plot(x, axes=c(1,2), choice="3D.map", rect=TRUE,
draw.tree=TRUE, ind.names=TRUE, t.level="all", title=NULL,
new.plot=FALSE, max.plot=15, tree.barplot=TRUE,
centers.plot=FALSE, ...)
Arguments
x
A HCPC object, see HCPC for details.
axes
a two integers vector.Defines the axes of the factor map
to plot.
choice
A string. "tree" plots the tree. "bar" plots bars of
inertia gains. "map" plots a factor map, individuals colored
by cluster. "3D.map" plots the same factor map, individuals colored by cluster,
the tree above.
rect
a boolean. If TRUE, rectangles are drawn around clusters
if choice ="tree".
tree.barplot
a boolean. If TRUE, the barplot of intra inertia
losses is added on the tree graph.
draw.tree
A boolean. If TRUE, the tree is projected on the
factor map if choice ="map".
ind.names
A boolean. If TRUE, the individuals names are added
on the factor map when choice="3D.map"
t.level
Either a positive integer or a string. A positive
integer indicates the starting level to plot the tree on the map when
draw.tree=TRUE. If "all", the whole tree is ploted. If "centers", it
draws the tree starting t the centers of the clusters.
title
a string. Title of the graph. NULL by default and a title is automatically defined
centers.plot
a boolean. If TRUE, the centers of clusters are
drawn on the 3D factor maps.
new.plot
a boolean. If TRUE, the plot is done in a new window.
data(iris)
# Clustering, auto nb of clusters:
res.hcpc=HCPC(iris[1:4], nb.clust=3)
# 3D graph from a different point of view:
plot(res.hcpc, choice="3D.map", angle=60)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(FactoMineR)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FactoMineR/plot.HCPC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.HCPC
> ### Title: Plots for Hierarchical Classification on Principle Components
> ### (HCPC) results
> ### Aliases: plot.HCPC
> ### Keywords: dplot
>
> ### ** Examples
>
> data(iris)
> # Clustering, auto nb of clusters:
> res.hcpc=HCPC(iris[1:4], nb.clust=3)
dev.new(): using pdf(file="Rplots925.pdf")
dev.new(): using pdf(file="Rplots926.pdf")
dev.new(): using pdf(file="Rplots927.pdf")
> # 3D graph from a different point of view:
> plot(res.hcpc, choice="3D.map", angle=60)
>
>
>
>
>
> dev.off()
png
2
>