performanceMeasure: Performance metrics to evaluate classification
In dcanr: Differential co-expression/association network analysis

Description Usage Arguments Details Value See Also Examples

Quantify the performance of a classification algorithm. Predictions and truth both have to be binary.

1	performanceMeasure(pred, obs, perf.method = "f.measure", ...)

`pred`	a logical or numeric, where 0 and FALSE represent control, and, 1 and TRUE represent cases
`obs`	a logical or numeric, where 0 and FALSE represent control, and, 1 and TRUE represent cases
`perf.method`	a character, specifying the method to use. Available methods can be accessed using `perfMethods`
`...`	additional parameters to methods. see details

The F-measure requires the beta parameter which can be specified using f.beta which defaults to 1 thereby computing the F1-measure.

a numeric, representing the performance

perfMethods

pred <- sample(0:1, 100, replace = TRUE, prob = c(0.75, 0.25))
obs <- sample(0:1, 100, replace = TRUE, prob = c(0.75, 0.25))

#compute the F1 and F2 scores
f1 <- performanceMeasure(pred, obs)
f2 <- performanceMeasure(pred, obs, f.beta = 2)