This function is used when you have a huge number of packets where you want to identify which ones are, individually, candidates for the good prediction of a response
bestm(w2mobj, y, percentage = 50)A list of class w2m with the following components:
A matrix containing the select packets (as columns), reordered so that the best packets come first
A vector which indexes the best packets into the original supplied matrix
The original wavelet packet indices corresponding to each packet
As pktix but for the wavelet packet levels
The number of resolution levels in the original wavelet packet object
The ordered correlations
The w2m object that contains the packets you wish to preselect
The response time series
The percentage of the w2m packets that you wish to select
G P Nason
This function naively addresses a very common problem. The object
w2mobj contains a huge number of variables which might shed some light
on the response object y. The problem is that the dimensionality
of w2mobj is larger than that of the length of the series y.
The solution here is to choose a large, but not huge, subset of the variables
that might be potentially useful in correlating with y, discard the
rest, and return the "best" or preselected variables. Then the dimensionality
is reduced and more sophisticated methods can be used to perform better
quality modelling of the response y on the packets in w2mobj.
makewpstRO