confusionMatrix.nlcv

object for which a confusionMatrix should be produced, e.g. one
produced by the <code>nlcv</code> function; for the print method, it is the object
to be printed

string indicating the classification technique for which the
confusion matrix should be returned

tech

logical indicating whether the cells of the matrix should
contain proportions (<code>TRUE</code>) or raw counts (<code>FALSE</code>)

proportions

Dots argument to pass additional parameters to the
<code>confusionMatrix</code> or <code>print</code> methods

&#8230;

The observed and predicted classes are cross-tabulated for a given
classification technique used in the nested loop cross validation. The
predicted class that is used to construct the confusion matrix is the class
that was predicted most of the time (\(&gt;= 50\%\)) across all runs
of the nested loop.

manip

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>.

confusionMatrix.nlcv: compute a confusion matrix for the optimal number of features for a given technique used in the nested loop cross validation

Description

Usage

Arguments

Value