Nothing
##
## Computing and plotting a bpca object with 'graphics' package - 2d
##
oask <- devAskNewPage(dev.interactive(orNone=TRUE))
bp <- bpca(gabriel1971)
plot(bp,
var.factor=2)
# Exploring the object 'bp' created by the function 'bpca'
class(bp)
names(bp)
str(bp)
summary(bp)
bp$call
bp$eigenval
bp$eigenvec
bp$numb
bp$import
bp$coord
bp$coord$obj
bp$coord$var
bp$var.rb
bp$var.rd
# Additional graphical parameters (nonsense)
plot(bpca(gabriel1971,
meth='sqrt'),
main='gabriel1971 - sqrt',
sub='The graphical parameters are working fine!',
var.factor=2,
var.cex=.6,
var.col=rainbow(9),
var.pch='v',
obj.pch='o',
obj.cex=.5,
obj.col=rainbow(8),
obj.pos=1,
obj.offset=.5)
##
## Computing and plotting a bpca object with 'scatterplot3d' package - 3d
##
bp <- bpca(gabriel1971,
d=1:3)
plot(bp,
var.factor=3)
# Exploring the object 'bp' created by the function 'bpca'
class(bp)
names(bp)
str(bp)
summary(bp)
bp$call
bp$eigenval
bp$eigenvec
bp$numb
bp$import
bp$coord
bp$coord$obj
bp$coord$var
bp$var.rb
bp$var.rd
# Additional graphical parameters (nonsense)
plot(bpca(gabriel1971,
d=1:3,
meth='jk'),
main='gabriel1971 - jk',
sub='The graphical parameters are working fine!',
var.factor=6,
var.pch='+',
var.cex=.6,
var.col='green4',
obj.pch='*',
obj.cex=.8,
obj.col=1:8,
ref.lty='solid',
ref.col='red',
angle=70)
##
## Computing and plotting a bpca object with 'obj.identify=TRUE' parameter - 2d
##
bp <- bpca(gabriel1971)
# Normal labels
if(dev.interactive()) {
plot(bp,
obj.names=FALSE,
obj.identify=TRUE)
}
# Alternative labels
if(dev.interactive()) {
plot(bp,
obj.names=FALSE,
obj.labels=c('toi', 'kit', 'bat', 'ele', 'wat', 'rad', 'tv', 'ref'),
obj.identify=TRUE)
}
##
## Computing and plotting a bpca object with 'obj.identify=TRUE' parameter - 3d
##
bp <- bpca(gabriel1971,
d=1:3)
# Normal labels
if(dev.interactive()) {
plot(bp,
obj.names=FALSE,
obj.identify=TRUE)
}
# Alternative labels
if(dev.interactive()) {
plot(bp,
obj.names=FALSE,
obj.labels=c('toi', 'kit', 'bat', 'ele', 'wat', 'rad', 'tv', 'ref'),
obj.identify=T)
}
##
## Computes: vector variable lengths, angles between vector variables and
## variable correlations from data.frame or matrix objects (n x p)
## n = rows (objects)
## p = columns (variables)
##
dt <- dt.tools(iris,
var.pos=2) # No numeric columns are removed in 'dt.tools'
# Exploring the object 'bp' created by the function 'var.tools'
class(dt)
names(dt)
str(dt)
dt$length
dt$angle
dt$r
dt
# Checking the determinations
(iris.tools <- round(dt.tools(iris[-5],
center=2)$r,
5))
(iris.obsv <- round(cor(iris[-5]),
5))
all(iris.tools == iris.obsv)
##
## Grouping objects with different symbols and colors - 2d and 3d
##
# 2d
plot(bpca(iris[-5]),
var.factor=.3,
var.cex=.7,
obj.names=FALSE,
obj.cex=1.5,
obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)],
obj.pch=c('+', '*', '-')[unclass(iris$Species)])
# 3d static
plot(bpca(iris[-5],
d=1:3),
var.factor=.2,
var.color=c('blue', 'red'),
var.cex=1,
obj.names=FALSE,
obj.cex=1,
obj.col=c('red', 'green3', 'blue')[unclass(iris$Species)],
obj.pch=c('+', '*', '-')[unclass(iris$Species)])
##
## Example of 'var.rb=TRUE' parameter as a measure of the quality of the biplot - 2d
##
## Differences between methods of factorization
# SQRT
bp1 <- bpca(gabriel1971,
meth='sqrt',
var.rb=TRUE)
qbp1 <- qbpca(gabriel1971,
bp1)
plot(qbp1, main='sqrt - 2d \n (poor)')
# JK
bp2 <- bpca(gabriel1971,
meth='jk',
var.rb=TRUE)
qbp2 <- qbpca(gabriel1971, bp2)
plot(qbp2,
main='jk - 2d \n (very poor)')
# GH
bp3 <- bpca(gabriel1971,
meth='gh',
var.rb=TRUE)
qbp3 <- qbpca(gabriel1971,
bp3)
plot(qbp3,
main='gh - 2d \n (good)')
# HJ
bp4 <- bpca(gabriel1971,
meth='hj',
var.rb=TRUE)
qbp4 <- qbpca(gabriel1971,
bp4)
plot(qbp4,
main='hj - 2d \n (good)')
##
## Example of 'var.rb=TRUE' parameter as a measure of the quality of the biplot - 3d
##
## Differences between methods of factorization
# SQRT
bp1 <- bpca(gabriel1971,
meth='sqrt',
d=1:3,
var.rb=TRUE)
qbp1 <- qbpca(gabriel1971,
bp1)
plot(qbp1,
main='sqrt - 3d \n (poor)')
# JK
bp2 <- bpca(gabriel1971,
meth='jk',
d=1:3,
var.rb=TRUE)
qbp2 <- qbpca(gabriel1971,
bp2)
plot(qbp2,
main='jk - 3d \n (very poor)')
# GH
bp3 <- bpca(gabriel1971,
meth='gh',
d=1:3,
var.rb=TRUE)
qbp3 <- qbpca(gabriel1971,
bp3)
plot(qbp3,
main='gh - 3d \n (whow!)')
# HJ
bp4 <- bpca(gabriel1971,
meth='hj',
d=1:3,
var.rb=TRUE)
qbp4 <- qbpca(gabriel1971,
bp4)
plot(qbp4,
main='hj - 3d \n (whow!)')
##
## Example of 'var.rd=TRUE' parameter as a measure of the quality of the biplot - 2d
## Mainly recommended for large datasets.
##
bp <- bpca(gabriel1971,
meth='hj',
var.rb=TRUE,
var.rd=TRUE,
limit=3)
bp$var.rd
# RUR followed by CRISTIAN contains information in dimensions that
# wasn't contemplated by the biplot reduction (PC3).
# Between all, RUR followed by CRISTIAN, variables are bad represented by a 2d
# biplot.
# Graphical visualization of the importance of the variables not contemplated
# in the reduction
plot(bpca(gabriel1971,
meth='hj',
d=3:4),
main='hj',
xlim=c(-1,1),
ylim=c(-1,1),
zlim=c(-1,1))
##
## New options plotting
##
data(ontario)
plot(bpca(ontario))
## Labels for all objects
(obj.lab <- paste('g',
1:18,
sep=''))
# Giving obj.labels
plot(bpca(ontario),
obj.labels=obj.lab)
# Evaluate an object (1 is the default)
plot(bpca(ontario),
type='eo',
obj.cex=1)
plot(bpca(ontario),
type='eo',
obj.id=7,
obj.cex=1)
# Giving obj.labels
plot(bpca(ontario),
type='eo',
obj.labels=obj.lab,
obj.id=7,
obj.cex=1)
# The same as above
plot(bpca(ontario),
type='eo',
obj.labels=obj.lab,
obj.id='g7',
obj.cex=1)
# Evaluate a variable (1 is the default)
plot(bpca(ontario),
type='ev',
var.pos=2,
var.cex=1)
plot(bpca(ontario),
type='ev',
var.id='E7',
obj.labels=obj.lab,
var.pos=1,
var.cex=1)
# A complete plot
cl <- 1:3
plot(bpca(iris[-5]),
type='ev',
var.id=1,
var.fac=.3,
obj.names=FALSE,
obj.col=cl[unclass(iris$Species)])
legend('topleft',
legend=levels(iris$Species),
text.col=cl,
pch=19,
col=cl,
cex=.9,
box.lty=0)
# Compare two objects (1 and 2 are the default)
plot(bpca(ontario),
type='co')
plot(bpca(ontario),
type='co',
obj.labels=obj.lab)
plot(bpca(ontario),
type='co',
obj.labels=obj.lab,
obj.id=13:14)
plot(bpca(ontario),
type='co',
obj.labels=obj.lab,
obj.id=c('g7', 'g13'))
# Compare two variables
plot(bpca(ontario),
type='cv')
# Which won where/what
plot(bpca(ontario),
type='ww')
# Discrimitiveness vs. representativeness
plot(bpca(ontario),
type='dv')
# Means vs. stability
plot(bpca(ontario),
type='ms')
# Rank objects with ref. to the ideal variable
plot(bpca(ontario),
type='ro')
# Rank variables with ref. to the ideal object
plot(bpca(ontario),
type='rv')
plot(bpca(iris[-5]),
type='eo',
obj.id=42,
obj.cex=1)
plot(bpca(iris[-5]),
type='ev',
var.id='Sepal.Width')
plot(bpca(iris[-5]),
type='ev',
var.id='Sepal.Width',
var.factor=.3)
devAskNewPage(oask)
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