dcEvaluate: Evaluate performance of DC methods on simulations
In dcanr: Differential co-expression/association network analysis

Description Usage Arguments Value See Also Examples

Quantify the performance of a differential co-expression pipeline on simulated data.

1 2	dcEvaluate(simulation, dclist, truth.type = c("association", "influence", "direct"), perf.method = "f.measure", combine = TRUE, ...)

`simulation`	a list, storing data and results generated from simulations
`dclist`	a list of igraphs, produced using `dcPipeline`
`truth.type`	a character, specifying which level of the true network to retrieve: 'association' (default), 'influence' or 'direct'
`perf.method`	a character, specifying the method to use. Available methods can be accessed using `perfMethods`
`combine`	a logical, indicating whether differential networks from independent knock-outs should be treated as a single inference or independent inferences (defaults to `TRUE`)
`...`	additional parameters to be passed on to the performance metric method (see `performanceMeasure`)

a numeric, representing the performance metric. A single value if combine = TRUE and a named vector otherwise.

dcPipeline, performanceMeasure, perfMethods

data(sim102)

#run a standard pipeline
resStd <- dcPipeline(sim102, dc.func = 'zscore')
dcEvaluate(sim102, resStd)
dcEvaluate(sim102, resStd, combine = FALSE)