Principal component analysis with reference spectra
Description
A PCA model is calculated after a multiple of the reference matrix is added to the data matrix.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## S4 method for signature 'matrix,matrix'
pcaadd(x, reference, ...,
ref.factor = 10, refcomps = seq_len(nrow(reference)))
## S4 method for signature 'hyperSpec,hyperSpec'
pcaadd(x, reference, ...)
## S4 method for signature 'hyperSpec,matrix'
pcaadd(x, reference, ...)
## S4 method for signature 'matrix,hyperSpec'
pcaadd(x, reference, ...)
## S3 method for class 'pcaadd'
predict(object, newdata, ...)

Arguments
x 
data matrix 
reference 
reference data points 
... 
further arguments are handed to prcomp, but

ref.factor 
reference is multiplied by

refcomps 
the principal components that are attributed to the reference 
object 
pcaadd model 
newdata 
matrix with new observations. 
Value
object of class "pcaadd"