frame.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

frame.logreg2 function

<dl><dt>fit</dt>
<dd>An object of type <code>"logreg"</code> fit to data</dd>
<dt>msz</dt>
<dd>Max number of leaves on a tree</dd>
<dt>ntr</dt>
<dd>Number of trees of fit</dd>
<dt>newbin</dt>
<dd>new binary variables</dd>
<dt>newresp</dt>
<dd>new response variable</dd>
<dt>newsep</dt>
<dd>new number of separate predictors</dd>
<dt>newcens</dt>
<dd>new censoring indicators</dd>
<dt>newweight</dt>
<dd>new weightings</dd></dl>

Arguments

INTERNAL FUNCTION TO EVALUATE IMPORTANCE OF PREDICTOR COMBINATIONS — frame.logreg2

<dl>

<dt>fit</dt>
<dd>An object of type <code>"logreg"</code> fit to data</dd>


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


<dt>ntr</dt>
<dd>Number of trees of fit</dd>


<dt>newbin</dt>
<dd>new binary variables</dd>


<dt>newresp</dt>
<dd>new response variable</dd>


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


<dt>newcens</dt>
<dd>new censoring indicators</dd>


<dt>newweight</dt>
<dd>new weightings</dd>

</dl>

frame.logreg2: INTERNAL FUNCTION TO EVALUATE IMPORTANCE OF PREDICTOR COMBINATIONS

Description

Usage

Value

Arguments

Details