Nested Loop Cross Validation
function to compare the original matrix of correct classes to each com...
compute a confusion matrix for the optimal number of features for a gi...
Function to define a learning sample based on balanced sampling
Wrapper around limma for the comparison of two groups
Misclassification Rate Plot
Nested Loop Cross-Validation
new MLInterfaces schema for lda from MASS
Instance of a learnerSchema for pamr models
convert from pamrML to classifierOutput
Wrapper function around the pamr.* functions
Function providing a formula interface to pamr.train
predict pamrML object
print object nlcvConfusionMatrix
print pamrML object
print function for summary.mcrPlot object
Plot the Distribution of Ranks of Features Across nlcv Runs
Produce a ROC plot for a classification model belonging to a given tec...
Function to Plot a Scores Plot
summary function for mcrPlot object
Methods for topTable
xtable method for confusionMatrix objects
xtable method for summary.mcrPlot objects
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>.