rocPlot

object of class 'nlcv' as produced by the nlcv function

nlcvObj

technique; character of length one; one of 'dlda', 'lda',
'nlda', 'qda', 'glm', 'randomForest', 'bagg', 'pam', 'svm' or 'ksvm'

tech

number of features used in the classification model; numeric of
length one

nfeat

main title to be used for the ROC plot

main

color for the global AUC (defaults to '#FF9900')

globalAUCcol

further arguments for the plot call (such as sub e.g.)

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Produce a ROC plot for a classification model belonging to a given technique
and with a given number of features.

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>.

rocPlot: Produce a ROC plot for a classification model belonging to a given technique and with a given number of features.

Description

Usage

Arguments

Value