PCAmixdata (version 2.1)

plot.PCAmix: Graphs of a PCAmix analysis before or after rotation

Description

Graphs of PCAmix analysis before or after rotation: plot of the observations (the scores or the standardized scores), plot of the variables (squared loadings) correlation circle of the available quantitative variables, plot of the categories of the available qualitative variables.

Usage

## S3 method for class 'PCAmix':
plot(x,axes = c(1, 2), choice = "ind", stand=FALSE,label=TRUE,
quali=NULL,posleg="topleft",xlim=NULL,ylim=NULL,cex=1,col.var=NULL,...)

Arguments

x

an object of class PCAmix obtained with the function PCAmix or PCArot.

axes

a length 2 vector specifying the components to plot.

choice

the graph to plot ("ind" for the observations, "var" for the variables (plot of the squared loadings), "cor" for the correlation circle if quantitative variables are available, "categ" for the categories if qualitative variables are available.

stand

if 'TRUE' the standardized scores are used in the plot of the observations.

label

if 'FALSE' the labels of the points are not plotted

quali

a qualitative variable such as a character vector or a factor of size n (the number of observation). The observations are colored according to the categories of this variable.

posleg

position of the legend if quali is not 'NULL'.

xlim

numeric vectors of length 2, giving the x coordinates range.

ylim

numeric vectors of length 2, giving the y coordinates range.

cex

cf. function par in the graphics package

col.var

a vector of size p (the total n umber of variables) which allows to colorate each variable with a different color

...

further arguments passed to or from other methods.

References

Chavent, M., Kuentz, V., Saracco, J. (2011), Orthogonal Rotation in PCAMIX Kiers, H.A.L., (1991), Simple structure in Component Analysis Techniques for mixtures of qualitative and quantitative variables, Psychometrika, 56, 197-212.

See Also

summary.PCAmix,PCAmix,PCArot,

Examples

#PCAMIX:
data(wine)
X.quanti <- wine[,c(3:29)] 
X.quali <- wine[,c(1,2)] 
pca<-PCAmix(X.quanti,X.quali,ndim=4, graph=FALSE)

#Scores on dim 1-2
plot(pca,choice="ind",quali=wine[,1],
	posleg="bottomleft",main="Scores") 
#Scores on dim 2-3
plot(pca,choice="ind",axes=c(2,3),quali=wine[,1],
	posleg="bottomleft",main="Scores") 
#Other graphics	
plot(pca,choice="var",main="Squared loadings")
plot(pca,choice="categ",main="Categories")
plot(pca,choice="cor",xlim=c(-1.5,2.5),
	main="Correlation circle")

rot<-PCArot(pca,3,graph=FALSE)
plot(rot,choice="ind", main="Rotated scores",label=FALSE)
plot(rot,choice="var",main="Squared loadings after rotation")
plot(rot,choice="categ",main="Categories after rotation")
plot(rot,choice="cor",main="Correlation circle after rotation")


#PCA:
data(decathlon)
quali<-decathlon[,13]
pca<-PCAmix(decathlon[,1:10], graph=FALSE)
plot(pca,choice="ind",quali=quali,stand=TRUE,cex=0.8,
	posleg="topright",main="Standardized scores")
plot(pca, choice="cor", stand=TRUE,main="Correlation circle")