mRMR.classic_FRESA

Returns the positive MI-scored set of maximum relevance minimum redundancy (mRMR) features returned by the mMRM.classic function

Feature_Filtering

Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.

Jose Tamez-Pena

FRESA.CAD

Feature Selection Algorithms for Computer Aided Diagnosis

mRMR.classic_FRESA function

<dl>	<dt>data</dt>
<dd>The data frame</dd>	<dt>Outcome</dt>
<dd>The outcome feature</dd>	<dt>feature_count</dt>
<dd>The number of features to return</dd>	<dt>...</dt>
<dd>Extra parameters to be passed to the <code>mRMRe::mMRM.classic</code> function</dd></dl>

Arguments

Author

FRESA.CAD wrapper of mRMRe::mRMR.classic — mRMR.classic_FRESA

<dl>

	<dt>data</dt>
<dd>The data frame</dd>

	<dt>Outcome</dt>
<dd>The outcome feature</dd>

	<dt>feature_count</dt>
<dd>The number of features to return</dd>

	<dt>...</dt>
<dd>Extra parameters to be passed to the <code>mRMRe::mMRM.classic</code> function</dd>

</dl>

mRMR.classic_FRESA: FRESA.CAD wrapper of mRMRe::mRMR.classic

Description

Usage

Value

Arguments

Author

See Also