topTable-methods

topTable

topTable,nlcv-method

object resulting from a classification or regression procedure

number of features that one wants to extract from a table that
ranks all features according to their importance in the classification or
regression model

method used to rank the features; one of <code>percentage</code>
(percentage of runs the feature is selected in the top n), <code>meanrank</code>
(mean rank of the feature across runs) or <code>medianrank</code> (median rank of
the feature across runs); <code>percentage</code> is the default method

method

Methods for topTable. topTable extracts the top n most important features
for a given classification or regression procedure.

manip

methods

Nested loop cross validation for classification purposes for misclassification error rate estimation.
The package supports several methodologies for feature selection: random forest, Student t-test, limma,
and provides an interface to the following classification methods in the 'MLInterfaces' package: linear,
quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized
linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
the classifier are included: plot of the ranks of the features, scores plot for a specific
classification algorithm and number of features, misclassification rate
for the different number of features and classification algorithms tested and ROC plot.
For further details about the methodology, please check:
Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004)
<doi:10.2202/1544-6115.1078>.

Laure Cougnaud

nlcv

Nested Loop Cross Validation

topTable-methods function

<dl class="dl-horizontal">nlcv<dt>fit = "nlcv"</dt><dd>nlcv objects are produced by <code>nlcv</code></dd> </dl>

topTable-methods: Methods for topTable

Description

Usage

Arguments

Value

Methods

Details

Examples