predict.pcaRes {pcaMethods} | R Documentation |
This function extracts the predict values from a pcaRes object for the PCA methods SVD, Nipals, PPCA and BPCA
Newdata is first centered if the PCA model was and then scores (T) and data (X) is 'predicted' according to :
That=XnewP
Xhat=ThatP'
Missing values are set to zero before matrix multiplication to achieve NIPALS like treatment of missing values.
predict.pcaRes(object, newdata, pcs=nPcs(object), ...)
object |
pcaRes the pcaRes object of interest. |
newdata |
matrix new data with same number of columns as
the used to compute object . |
pcs |
numeric The number of PC's to consider |
... |
Not passed on anywhere, included for S3 consistency. |
A list with the following components:
scores |
The predicted scores |
x |
The predicted data |
Henning Redestig <henning[at]psc.riken.jp>
data(iris) hidden <- sample(nrow(iris), 50) pcIr <- pca(iris[-hidden,1:4]) pcFull <- pca(iris[,1:4]) irisHat <- predict(pcIr, iris[hidden,1:4]) cor(irisHat$scores[,1], scores(pcFull)[hidden,1])