tdmPreSFA.train: SFA (Slow Feature Analysis) for numeric columns in a data...
In TDMR: Tuned Data Mining in R

Description Usage Arguments Value Author(s) See Also

tdmPreSFA.train uses package rSFA-package. It is assumed that classification for the variable contained in column response.var is done. SFA seeks features in an expanded function space for which the intra-class variation w.r.t. response.var is as low as possible.

1	tdmPreSFA.train(dset, response.var, opts)

dset

the data frame with training (and test) data.

response.var

the response variable for classification.

opts

a list from which we need here the following entries:

PRE.SFA: [ "linear" | "2nd" | "none" ] which stands for [ 1st | 2nd degree monomial SFA | no SFA ]
PRE.SFA.REPLACE: [T] =T: replace the original numerical columns with the SFA columns; =F: add the SFA columns
PRE.SFA.npc: if >0, then add for the first PRE.SFA.npc PCs the monomials of degree 2 (see tdmPreAddMonomials)
PRE.SFA.PPRANGE: [11] number of inputs after preprocessing, they enter into expansion
PRE.SFA.ODIM: [5] number of SFA output dimensions (slowest signals) to return
PRE.SFA.numericV vector with all column names in dset which are input for SFA. These columns may contain *numeric* values only.

sfa, a list with entries:

`dset`	the input data frame dset with columns numeric.variables replaced or extended (depending on `opts$PRE.SFA.REPLACE`) by the SFA components with names SF1, SF2, ... and with optional monomial columns added, if PRE.SFA.npc>0
`numeric.variables`	the new numeric column names of `dset`, i.e. SFA components, monomials (and optionally PRE.SFA.numericV, if `opts$PRE.SFA.REPLACE==F`)
`sfaList`	a list with the items `opts (sfaOpts)`, matrices DSF and SF and many others, as returned from `sfaStep`