This is a simple estimator for the optimal number of componets
when applying PCA or LLSimpute for missing value estimation. No
cross validation is performed, instead the estimation quality is
defined as Matrix[!missing]  Estimate[!missing]. This will give a
relatively rough estimate, but the number of iterations equals the
length of the parameter evalPcs.
Does not work with LLSimpute!!
As error measure the NRMSEP (see Feten et. al, 2005) or the Q2
distance is used. The NRMSEP basically normalises the RMSD
between original data and estimate by the variablewise
variance. The reason for this is that a higher variance will
generally lead to a higher estimation error. If the number of
samples is small, the gene  wise variance may become an unstable
criterion and the Q2 distance should be used instead. Also if
variance normalisation was applied previously.
1 2  kEstimateFast(Matrix, method = "ppca", evalPcs = 1:3, em = "nrmsep",
allVariables = FALSE, verbose = interactive(), ...)

Matrix 

method 

evalPcs 

em 

allVariables 

verbose 

... 
Further arguments to 
list 
Returns a list with the elements:

Wolfram Stacklies
1 2 3 4 5 6  data(metaboliteData)
# Estimate best number of PCs with ppca for component 2:4
esti < kEstimateFast(t(metaboliteData), method = "ppca", evalPcs = 2:4, em="nrmsep")
barplot(drop(esti$eError), xlab = "Components",ylab = "NRMSEP (1 iterations)")
# The best k value is:
print(esti$minNPcs)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.