bpca-package {bpca} R Documentation

## Biplot of Multivariate Data Based on Principal Components Analysis

### Description

Implements biplot (2d and 3d) and diagnostic tools of the quality of the reduction.

### Author(s)

Faria, J. C.
Allaman, I. B.
Demétrio C. G. B.

### References

Gabriel, K. R. (1971) The biplot graphical display of matrices with application to principal component analysis. Biometrika 58, 453-467.

Galindo Vilardón, M. P. (1986) Una alternativa de representación simultánea: HJ-Biplot. Qüestiió, 10(1):13-23, 1986.

Johnson, R. A. and Wichern, D. W. (1988) Applied multivariate statistical analysis. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 6 ed.

Gower, J.C. and Hand, D. J. (1996) Biplots. Chapman & Hall.

Yan, B. W. and Kang, M. S. (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, New York, 288p.

### Examples

##
## Grouping objects with different symbols and colors - 2d and 3d
##

dev.new(w=6, h=6)

## Not run:
# 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)])

# 3d dynamic
plot(bpca(iris[-5],
method='hj',
d=1:3),
rgl.use=TRUE,
var.col='brown',
var.factor=.3,
var.cex=1.2,
obj.names=FALSE,
obj.cex=.8,
obj.col=c('red', 'green3', 'orange')[unclass(iris$Species)], simple.axes=FALSE, box=TRUE) ## End(Not run) ## ## New options plotting ## plot(bpca(ontario)) # Labels for all objects (obj.lab <- paste('g', 1:18, sep='')) # Giving obj.labels plot(bpca(ontario), obj.labels=obj.lab) # Evaluate an object (1 is the default) plot(bpca(ontario), type='eo', obj.cex=1) plot(bpca(ontario), type='eo', obj.id=7, obj.cex=1) # Giving obj.labels plot(bpca(ontario), type='eo', obj.labels=obj.lab, obj.id=7, obj.cex=1) # The same as above plot(bpca(ontario), type='eo', obj.labels=obj.lab, obj.id='g7', obj.cex=1) # Evaluate a variable (1 is the default) plot(bpca(ontario), type='ev', var.pos=2, var.cex=1) plot(bpca(ontario), type='ev', var.id='E7', obj.labels=obj.lab, var.pos=1, var.cex=1) # A complete plot cl <- 1:3 plot(bpca(iris[-5]), type='ev', var.id=1, var.fac=.3, obj.names=FALSE, obj.col=cl[unclass(iris$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')

## Not run:
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)

## End(Not run)