dcEvaluate {dcanr} | R Documentation

## Evaluate performance of DC methods on simulations

### Description

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

### Usage

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

### Arguments

`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`)

### Value

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

### See Also

`dcPipeline`, `performanceMeasure`, `perfMethods`

### Examples

    data(sim102)

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

[Package dcanr version 1.0.0 Index]