ROCR (version 1.0-1)

performance: Function to create performance objects

Description

All kinds of predictor evaluations are performed using this function.

Usage

performance(prediction.obj, measure, x.measure="cutoff", ...)

Arguments

prediction.obj
An object of class prediction.
measure
Performance measure to use for the evaluation. A complete list of the performance measures that are available for measure and x.measure is given in the 'Details' section.
x.measure
A second performance measure. If different from the default, a two-dimensional curve, with x.measure taken to be the unit in direction of the x axis, and measure to be the unit in direction of the y axis, is created.
...
Optional arguments (specific to individual performance measures).

Value

  • An S4 object of class performance.

Details

Here is the list of available performance measures. Let Y and $\hat{Y}$ be random variables representing the class and the prediction for a randomly drawn sample, respectively. We denote by $\oplus$ and $\ominus$ the positive and negative class, respectively. Further, we use the following abbreviations for empirical quantities: P (# positive samples), N (# negative samples), TP (# true positives), TN (# true negatives), FP (# false positives), FN (# false negatives). [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

A detailed list of references can be found on the ROCR homepage at http://rocr.bioinf.mpi-sb.mpg.de.

See Also

prediction, prediction-class, performance-class, plot.performance

Examples

Run this code
## computing a simple ROC curve (x-axis: fpr, y-axis: tpr)
library(ROCR)
data(ROCR.simple)
pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels)
perf <- performance(pred,"tpr","fpr")
plot(perf)

## precision/recall curve (x-axis: recall, y-axis: precision)
perf1 <- performance(pred, "prec", "rec")
plot(perf1)

## sensitivity/specificity curve (x-axis: specificity,
## y-axis: sensitivity)
perf1 <- performance(pred, "sens", "spec")
plot(perf1)

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