Description Usage Arguments Details Author(s) References See Also Examples
Displays the graphical outputs of PCAmix and PCArot. The individuals (observations), the quantitative variables and the levels of the qualitative variables are plotted as points using their factor coordinates (scores). All the variables (quantitative and qualitative) are plotted as points on the same graph using their squared loadings.
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x 
an object of class PCAmix obtained with the function 
axes 
a length 2 vector specifying the components to plot. 
choice 
the graph to plot:

label 
boolean, if FALSE the labels of the points are not plotted. 
coloring.ind 
a qualitative variable such as a character vector or a factor of size n (the number of individuals). The individuals are colored according to the levels of this variable. If NULL, the individuals are not colored. 
col.ind 
a vector of colors, of size the number of levels of

coloring.var 
boolean, if TRUE, the variables in the plot of the squared loadings are colored according to their type (quantitative or qualitative). 
lim.cos2.plot 
a value between 0 and 1. Points with squared cosinus below this value are not plotted. 
lim.contrib.plot 
a value between 0 and 100. Points with relative contributions (in percentage) below this value are not plotted. 
posleg 
position of the legend. 
xlim 
a numeric vectors of length 2, giving the x coordinates range. If NULL (by default) the range is defined automatically (recommended). 
ylim 
a numeric vectors of length 2, giving the y coordinates range. If NULL (by default) the range is defined automatically (recommended). 
cex 
cf. function 
leg 
boolean, if TRUE, a legend is displayed. 
main 
a string corresponding to the title of the graph to draw. 
cex.leg 
a numerical value giving the amount by which the legend should be magnified. Default is 0.8. 
... 
arguments to be passed to methods, such as graphical parameters. 
The observations can be colored according to the levels of a qualitative variable. The observations, the quantitative variables and the levels can be selected according to their squared cosine (lim.cos2.plot) or their relative contribution (lim.contrib.plot) to the component map. Only points with squared cosine or relative contribution greater than a given threshold are plotted. Note that the relative contribution of a point to the component map (a plan) is the sum of the absolute contributions to each dimension, divided by the sum of the corresponding eigenvalues.
Marie Chavent [email protected], Amaury Labenne
Chavent M., KuentzSimonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38  data(gironde)
base < gironde$housing[1:20,]
X.quanti <splitmix(base)$X.quanti
X.quali < splitmix(base)$X.quali
res<PCAmix(X.quanti, X.quali, rename.level=TRUE, ndim=3,graph=FALSE)
#quantitative variables on the correlation circle
plot(res,choice="cor",cex=0.8)
#individuals component map
plot(res,choice="ind",cex=0.8)
#individuals colored with the qualitative variable "houses"
houses < X.quali$houses
plot(res,choice="ind",cex=0.6,coloring.ind=houses)
#individuals selected according to their cos2
plot(res,choice="ind",cex=0.6,lim.cos2.plot=0.8)
#all the variables plotted with the squared loadings
plot(res,choice="sqload",cex=0.8)
#variables colored according to their type (quanti or quali)
plot(res,choice="sqload",cex=0.8,coloring.var=TRUE)
#levels component map
plot(res,choice="levels",cex=0.8)
#example with supplementary variables
data(wine)
X.quanti < splitmix(wine)$X.quanti[,1:5]
X.quali < splitmix(wine)$X.quali[,1,drop=FALSE]
X.quanti.sup <splitmix(wine)$X.quanti[,28:29]
X.quali.sup <splitmix(wine)$X.quali[,2,drop=FALSE]
pca<PCAmix(X.quanti,X.quali,ndim=4,graph=FALSE)
pca2 < supvar(pca,X.quanti.sup,X.quali.sup)
plot(pca2,choice="levels")
plot(pca2,choice="cor")
plot(pca2,choice="sqload")

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