predict.PCAmix: Prediction of new scores in PCAmix or PCArot

Description

This function performs the scores of new observations on the principal components of PCAmix. If the components have been rotated, this function performs the scores of the new observations on the rotated principal components. In other words, this function is projecting the new observations onto the principal components of PCAmix (or PCArot) obtained previoulsy on a separated dataset. Note that the new observations must be described with the same variables than those used in PCAmix (or PCArot).

Usage

# S3 method for PCAmix
predict(object, X.quanti = NULL, X.quali = NULL,
  rename.level = FALSE, ...)

Arguments

object

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

X.quanti

a numeric data matrix or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).

X.quali

a categorical matrix of data, or an object that can be coerced to such a matrix (such as a character vector, a factor or a data frame with all factor columns).

rename.level

boolean, if TRUE all the levels of the qualitative variables are renamed as follows: "variable_name=level_name". This prevents to have identical names for the levels.

…

urther arguments passed to or from other methods. They are ignored in this function.

Value

Returns the matrix of the scores of the new observations on the principal components or on the rotated principal components of PCAmix.

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

Examples

Run this code

# NOT RUN {
# quantitative data
data(decathlon)
n <- nrow(decathlon)
sub <- sample(1:n,20)
pca<-PCAmix(decathlon[sub,1:10], graph=FALSE)
predict(pca,decathlon[-sub,1:10])
rot <- PCArot(pca,dim=4)
predict(rot,decathlon[-sub,1:10])

# quantitative and qualitative data
data(wine)
str(wine)
X.quanti <- splitmix(wine)$X.quanti
X.quali <- splitmix(wine)$X.quali
pca<-PCAmix(X.quanti[,1:27],X.quali,ndim=4,graph=FALSE)
n <- nrow(wine)
sub <- sample(1:n,10)
pca<-PCAmix(X.quanti[sub,1:27],X.quali[sub,],ndim=4)
pred <- predict(pca,X.quanti[-sub,1:27],X.quali[-sub,])
plot(pca,axes=c(1,2))
points(pred[,c(1,2)],col=2,pch=16)
text(pred[,c(1,2)], labels = rownames(X.quanti[-sub,1:27]), col=2,pos=3)
# }