predict.logreg2

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

predict.logreg2 function

<dl><dt>object</dt>
<dd>An object of class <code>"logreg"</code></dd>
<dt>msz</dt>
<dd>Max number of leaves on a tree</dd>
<dt>ntr</dt>
<dd>Number of trees in object</dd>
<dt>newbin</dt>
<dd>matrix containing new datapoints for prediction</dd>
<dt>newsep</dt>
<dd>new number of separate predictors</dd>
<dt>newcens</dt>
<dd>censoring indicator</dd></dl>

Arguments

Internal Logic Forest Functions — predict.logreg2

<dl>

<dt>object</dt>
<dd>An object of class <code>"logreg"</code></dd>


<dt>msz</dt>
<dd>Max number of leaves on a tree</dd>


<dt>ntr</dt>
<dd>Number of trees in object</dd>


<dt>newbin</dt>
<dd>matrix containing new datapoints for prediction</dd>


<dt>newsep</dt>
<dd>new number of separate predictors</dd>


<dt>newcens</dt>
<dd>censoring indicator</dd>

</dl>

predict.logreg2: Internal Logic Forest Functions

Description

Usage

Value

Arguments