Extract fitted values from PCA.
Fitted values of a PCA model
## S3 method for class 'pcaRes' fitted(object, data = NULL, nPcs = nP(object), pre = TRUE, post = TRUE, ...) ## S4 method for signature 'pcaRes' fitted(object, data = NULL, nPcs = nP(object), pre = TRUE, post = TRUE, ...)
object |
the |
data |
For standard PCA methods this can safely be left null to get scores x loadings but if set, then the scores are obtained by projecting provided data onto the loadings. If data contains missing values the result will be all NA. Non-linear PCA is an exception, here if data is NULL then data is set to the completeObs and propaged through the network. |
nPcs |
The number of PC's to consider |
pre |
pre-process |
post |
unpre-process the final data (add the center back etc to get the final estimate) |
... |
Not used |
This function extracts the fitted values from a pcaResobject. For PCA methods like SVD, Nipals, PPCA etc this is basically just the scores multipled by the loadings and adjusted for pre-processing. for non-linear PCA the original data is propagated through the network to obtain the approximated data.
A matrix representing the fitted data
Henning Redestig
pc <- pca(iris[,1:4], nPcs=4, center=TRUE, scale="uv") sum( (fitted(pc) - iris[,1:4])^2 )
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