## Chapter 12: Multivariate Data Exploration and Discrimination

## Sec 12.1: Multivariate Exploratory Data Analysis
## ss 12.1.1: Scatterplot matrices
## Scatterplot matrix, columns 9-11 of possum (DAAG).
## Colors distinguish sexes; symbols distinguish sites
library(DAAG)
## Loading required package: lattice
library(lattice)
colr <- c("red", "blue")
pchr <- c(3,4,0,8,2,10,1)
ss <- expand.grid(site=1:7, sex=1:2)           # Site varies fastest
parset <- with(ss, simpleTheme(pch=pchr[site], col=colr[sex]))
sitenames <- c("Cambarville","Bellbird","Whian Whian", "Byrangery",
"Conondale ","Allyn River", "Bulburin")
## Add column sexsite to possum; will be used again below
possum$sexsite <- paste(possum$sex, possum$site, sep="-") splom(possum[, c(9:11)], groups = possum$sexsite,
col = colr[ss$sex], par.settings=parset, varnames=c("tail\nlength","foot\nlength","ear conch\nlength"), key = list(text=list(sitenames), points=list(pch=pchr), columns=3))  ## Cloud plot of earconch, taill and footlgth cloud(earconch~taill+footlgth, data=possum, pch=pchr, groups=site, par.settings=simpleTheme(pch=c(3,4,0,8,2,10,1)), auto.key = list(space="top", corner=c(0,1), columns=3, between=1, text=sitenames, between.columns=2))   # auto.key takes its symbols (pch) from par.settings ## ss 12.1.2: Principal components analysis ## Preliminary data scrutiny ## Principal components calculations: possum[, 6:14] (DAAG) possum.prc <- princomp(na.omit(possum[, 6:14])) ## Footnote Code ## Plot of principal components: possum[, 6:14] here<- complete.cases(possum[, 6:14]) colr <- c("red", "blue") pchr <- c(3,4,0,8,2,10,1) ss <- expand.grid(site=1:7, sex=1:2) # Site varies fastest xyplot(possum.prc$scores[, 2] ~ possum.prc$scores[, 1], aspect="iso", groups = possum$sexsite[here], col = colr[ss$sex], pch = pchr[ss$site],
xlab="1st Principal Component", ylab="2nd Principal Component",
key=list(points = list(pch=pchr),
text=list(c("Cambarville", "Bellbird", "Whian Whian",
"Byrangery", "Conondale", "Allyn River",
"Bulburin" )), columns=4))