If training set too small, augment it with parametric
bootstrap
Usage
sfaPBootstrap(realclass, x, sfaList)
Arguments
realclass
true class of training data (can be
vector, numerics, integers, factors)
x
matrix containing the training data
sfaList
list with several parameter settings, e.g.
as created by sfa2CreatesfaList$xpDimFun (=xpDim by default) calculated
dimension of expaned SFA space
sfaList$deg
degree of
Value
a list list containing:
xtraining set
extended to minimu number of recors1.5*(xpdim+nclass), if
necessary
realclasstraining class labels,
extended analogously