calcStats calculates classifier performance based on the class predictions
and actual class labels stored in an ExprsPredict object.
calcStats(object, aucSkip = FALSE, plotSkip = FALSE)# S4 method for ExprsPredict
calcStats(object, aucSkip = FALSE,
plotSkip = FALSE)
An ExprsPredict object.
A logical scalar. Toggles whether to calculate area under the receiver operating characteristic curve. See details.
A logical scalar. Toggles whether to plot the receiver operating characteristic curve.
Returns a data.frame of performance metrics.
ExprsPredict: Method to calculate the performance of a deployed classifier.
This function calculates classifier performance based on the predicted
class labels and the actual class labels in one of two ways. If the argument
aucSkip = FALSE AND the ExprsArray object was an ExprsBinary
object with at least one case and one control AND ExprsPredict contains
a coherent @probability slot, calcStats will calculate classifier
performance using the area under the receiver operating characteristic (ROC) curve
via the ROCR package. Otherwise, calcStats will calculate classifier
performance traditionally using a confusion matrix. Note that accuracies calculated
using ROCR may differ from accuracies calculated using a confusion
matrix because the former may adjust the discrimination threshold to optimize
sensitivity and specificity. The discrimination threshold is automatically chosen
as the point along the ROC which minimizes the Euclidean distance from (0, 1).