bpca-package {bpca} | R Documentation |
Implements biplot (2d and 3d) and diagnostic tools of the quality of the reduction.
Package: | bpca |
Type: | Package |
Version: | 1.0.1 |
Date: | 2008-08-08 |
License: | GPL (>= 2) |
Jose Claudio Faria (joseclaudio.faria@gmail.com)
and
Clarice Garcia Borges Demetrio (clarice@esalq.usp.br)
Gabriel, K. R. (1971) The biplot graphical display of matrices with application to principal component analysis. Biometrika 58, 453-467.
Gower, J.C. and Hand, D. J. (1996) Biplots. Chapman & Hall.
Galindo, M. P. (1986) Una alternativa de representacion simultanea: HJ-Biplot. Questiio, 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.
Yan, B. W. and Kang, M. S. (2003) GGE biplot analysis : a graphical tool for breeders, geneticists, and agronomists. CRC Press, New York, 288p.
## ## Example 1 ## Basic presentation and comparision with 'biplot' function ('stats' package) ## library(bpca) # Opening e configuring a graphical device x11(w=8, h=4) op <- par(no.readonly=TRUE) par(mfrow=c(1, 2)) # Biplot of package stats (left) and bpca of package biplot (right) # variables in columns (represented as red vectors) # biplot biplot(prcomp(caith, scale=FALSE), main='biplot (stats) (scale=FALSE)') # bpca plot(bpca(caith, var.scale=FALSE), main='bpca - hj (var.scale=FALSE)', var.factor=2, var.cex=1, obj.cex=1) # Variables in rows (represented as red vectors) biplot(prcomp(t(caith), scale=TRUE), main='biplot (stats) (scale=TRUE)') plot(bpca(caith, var.scale=TRUE, var.pos=1), main='bpca - hj (var.scale=TRUE)', var.factor=2, var.cex=1, obj.cex=1) par(op) # Summarizing bpca summary(bpca(caith, var.scale=FALSE)) bpca(caith, var.scale=FALSE)$coord bpca(caith, var.scale=FALSE)$eigenvec ## ## Example 2 ## Grouping objects with different symbols and colors - 2d and 3d ## library(bpca) x11(w=6, h=6) # 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], lambda.end=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 dinamic plot(bpca(iris[-5], method='hj', lambda.end=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)