bpca (version 1.3-6)

bpca-package: 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

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)
oask <- devAskNewPage(dev.interactive(orNone=TRUE))

if (FALSE) {
# 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)
}

##
## 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')

if (FALSE) {
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)