A method to visualize the performance in the classification of synthesis, degradation
and processing rates based on the comparison of the original simulated rates and the one
obtained by the function modelRates. For each rate, classification performance is measured
in terms of sensitivity and specificity using a ROC curve analysis. False negatives (FN) represent cases
where the rate is identified as constant while it was simulated as varying. False positives (FP) represent
cases where INSPEcT identified a rate as varying while it was simulated as constant. On the contrary,
true positives (TP) and negatives (TN) are cases of correct classification of varying and constant rates, respectively.
Consequently, sensitivity and specificity are computed using increasing thresholds for the brown p-values,
and the ability of correctly classifying a rate is measured through the area under the curve (AUC) for each rate.
rocCurve(object, object2, cTsh = NULL, plot = TRUE)
"rocCurve"(object, object2, cTsh = NULL, plot = TRUE)
"rocCurve"(object, object2, cTsh = NULL, plot = TRUE)makeSimModel, makeSimDataset, rocThresholds
data('simRates', package='INSPEcT')
data('simData3rep', package='INSPEcT')
rocCurve(simRates, simData3rep)
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