TTab

Internal function to evaluate importance of predictor combinations

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

TTab function

<dl><dt>data</dt>
<dd>Input data.</dd>
<dt>tree</dt>
<dd>Logic tree constructed for a sample.</dd>
<dt>Xs</dt>
<dd>Input predictors.</dd>
<dt>mtype</dt>
<dd>Model type: classification or linear or survival regression.</dd></dl>

Arguments

Bethany J. Wolf <a href="/link/wolfb%40musc.edu?package=LogicForest&version=2.1.2" data-mini-rdoc="LogicForest::wolfb@musc.edu">wolfb@musc.edu</a>

Author

Truth table — TTab

<dl>

<dt>data</dt>
<dd>Input data.</dd>


<dt>tree</dt>
<dd>Logic tree constructed for a sample.</dd>


<dt>Xs</dt>
<dd>Input predictors.</dd>


<dt>mtype</dt>
<dd>Model type: classification or linear or survival regression.</dd>

</dl>

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

TTab: Truth table

Description

Usage

Value

Arguments

Author

Details

References

See Also