Predict values from PCA.
Predict data using PCA model
## S3 method for class 'pcaRes' predict(object, newdata, pcs = nP(object), pre = TRUE, post = TRUE, ...) ## S4 method for signature 'pcaRes' predict(object, newdata, pcs = nP(object), pre = TRUE, post = TRUE, ...)
object |
|
newdata |
|
pcs |
|
pre |
pre-process |
post |
unpre-process the final data (add the center back etc) |
... |
Not passed on anywhere, included for S3 consistency. |
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.
A list with the following components:
scores |
The predicted scores |
x |
The predicted data |
Henning Redestig
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])
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