| performance-class {ROCR} | R Documentation |
Class performance
Description
Object to capture the result of a performance evaluation, optionally collecting evaluations from several cross-validation or bootstrapping runs.
Details
A performance object can capture information from four
different evaluation scenarios:
The behaviour of a cutoff-dependent performance measure across the range of all cutoffs (e.g.
performance( predObj, 'acc' )). Here,x.valuescontains the cutoffs,y.valuesthe corresponding values of the performance measure, andalpha.valuesis empty.
The trade-off between two performance measures across the range of all cutoffs (e.g.
performance( predObj, 'tpr', 'fpr' )). In this case, the cutoffs are stored inalpha.values, whilex.valuesandy.valuescontain the corresponding values of the two performance measures.
A performance measure that comes along with an obligatory second axis (e.g.
performance( predObj, 'ecost' )). Here, the measure values are stored iny.values, while the corresponding values of the obligatory axis are stored inx.values, andalpha.valuesis empty.
A performance measure whose value is just a scalar (e.g.
performance( predObj, 'auc' )). The value is then stored iny.values, whilex.valuesandalpha.valuesare empty.
Slots
x.namePerformance measure used for the x axis.
y.namePerformance measure used for the y axis.
alpha.nameName of the unit that is used to create the parametrized curve. Currently, curves can only be parametrized by cutoff, so
alpha.nameis eithernoneorcutoff.x.valuesA list in which each entry contains the x values of the curve of this particular cross-validation run.
x.values[[i]],y.values[[i]], andalpha.values[[i]]correspond to each other.y.valuesA list in which each entry contains the y values of the curve of this particular cross-validation run.
alpha.valuesA list in which each entry contains the cutoff values of the curve of this particular cross-validation run.
Objects from the Class
Objects can be created by using the performance function.
Author(s)
Tobias Sing tobias.sing@gmail.com, Oliver Sander osander@gmail.com
References
A detailed list of references can be found on the ROCR homepage at http://rocr.bioinf.mpi-sb.mpg.de.
See Also
prediction
performance,
prediction-class,
plot.performance