tdmPrePCA.apply

dset

a value returned from a prior call to <code><a rd-options="" href="/link/tdmPrePCA.train?package=TDMR&version=2.2" data-mini-rdoc="TDMR::tdmPrePCA.train">tdmPrePCA.train</a></code>

pcaList

a list from which we need here the following entries:<ul>
<li>PRE.knum: if &gt;0 and if PRE.PCA="kernel", take only a subset of PRE.knum records from dset</li>
<li>PRE.PCA.npc: if &gt;0, then add for the first PRE.PCA.npc PCs the monomials of
 degree 2 (see tdmPreAddMonomials)</li>
<li>PRE.PCA.numericV vector with all column names in dset for which PCA is performed.
 These columns may contain *numeric* values only.</li>
</ul>

opts

[NULL] optional, only needed in case that dset is a 0-row-data frame: then we 'borrow' the columns from dtrain,
the data set returned from <code><a rd-options="" href="/link/tdmPrePCA.train?package=TDMR&version=2.2" data-mini-rdoc="TDMR::tdmPrePCA.train">tdmPrePCA.train</a></code> in <code>pca$dset</code>.

dtrain

The PCA rotation is taken from <code>pcaList</code>, a value returned from a prior call to <code><a rd-options="" href="/link/tdmPrePCA.train?package=TDMR&version=2.2" data-mini-rdoc="TDMR::tdmPrePCA.train">tdmPrePCA.train</a></code>.

Tuned Data Mining in R ('TDMR') performs the complete tuning of a
data mining task (predictive analytics, that is classification and regression).
Preprocessing parameters and modeling parameters can be tuned simultaneously.
It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and
allows integration of additional tuners. Noise handling in the data mining optimization
process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.

Wolfgang Konen

TDMR

Tuned Data Mining in R

tdmPrePCA.apply function

a value returned from a prior call to <code><a rd-options='' href='tdmPrePCA.train'>tdmPrePCA.train</a></code>

[NULL] optional, only needed in case that dset is a 0-row-data frame: then we 'borrow' the columns from dtrain,
the data set returned from <code><a rd-options='' href='tdmPrePCA.train'>tdmPrePCA.train</a></code> in <code>pca$dset</code>.

The PCA rotation is taken from <code>pcaList</code>, a value returned from a prior call to <code><a rd-options='' href='tdmPrePCA.train'>tdmPrePCA.train</a></code>.

tdmPrePCA.apply: Apply PCA (Principal Component Analysis) to new data.

Description

Usage

Arguments

Value

See Also