pimp.mat.bin

Internal function called by <code>pimp.import</code> to construct a binary/logical matrix
representing which predictors (columns) are used in each interaction (rows) of a sample.

internal

Logic Forest is an ensemble machine learning method that identifies important and interpretable combinations of binary predictors using logic regression trees to model complex relationships with an outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

Melica Nikahd

LogicForest

Logic Forest

Bethany Wolf

Andrew Gothard

Madison Hyer

pimp.mat.bin function

<dl><dt>pimps.out</dt>
<dd>R object containing <code>vec.primes</code>, <code>tmp.mat</code>, <code>vec.pimpvars</code>, and <code>list.pimps</code>.</dd>
<dt>testdata</dt>
<dd>Data frame or matrix of out-of-bag (OOB) samples.</dd></dl>

Arguments

Bethany J. Wolf <a href="/link/wolfb%40musc.edu?package=LogicForest&version=2.1.3" data-mini-rdoc="LogicForest::wolfb@musc.edu">wolfb@musc.edu</a>

Author

Predictor Importance Matrix – Classification — pimp.mat.bin

<dl>

<dt>pimps.out</dt>
<dd>R object containing <code>vec.primes</code>, <code>tmp.mat</code>, <code>vec.pimpvars</code>, and <code>list.pimps</code>.</dd>


<dt>testdata</dt>
<dd>Data frame or matrix of out-of-bag (OOB) samples.</dd>

</dl>

Bethany J. Wolf <a href='mailto:wolfb@musc.edu'>wolfb@musc.edu</a>

pimp.mat.bin: Predictor Importance Matrix – Classification

Description

Usage

Value

Arguments

Author

Details

References

See Also