noise.bench: Noise sensitivity test
In netbenchmark: Benchmarking of several gene network inference methods

Description Usage Arguments Details Value Author(s) See Also Examples

For a given vector of character of the names of wrapper functions that compute a network inference methods, noise.bench performs a noise sensitivity test. It makes use of different big gene datasets adding Gaussian noise with different intensity to evaluate the performance of the methods.

    noise.bench(methods = "all.fast", datasources.names = "all",
        experiments = 150, eval = "AUPR", no.topedges = 20,
        datasets.num = 3, local.noise = seq(0, 100, len = 3),
        global.noise = 0, noiseType = "normal", sym = TRUE,
        seed = NULL)

`methods`	A vector of characters containing the names of network inference algorithms wrappers to be compared (default: "all.fast").
`datasources.names`	A vector of characters containing the names of network datasets to be included in the benchmark (default: "all").
`eval`	The name of the evaluation metric among the following ones: "no.truepos", "AUROC" or "AUPR" (default : "AUPR") - see `evaluate`.
`datasets.num`	Number of repetitions in the noise evaluation, for each method and each dataset and each noise intensity (default: 5).
`experiments`	Integer specifying the number of experiments to generate the subsampled datasets (default: 150) - see `datasource.subsample`.
`no.topedges`	Float specifying the percentage number of links to be considered in the evaluation (default: 20).
`local.noise`	Vector specifying the desired percentage of local noise to be added at each of the subsampled datasets (default: seq(0, 100, len = 3)).
`global.noise`	Vector specifying the desired percentage of global noise to be added at each of the subsampled datasets (default: 0).
`noiseType`	Character specifying the type of the noise to be added: "normal" (default: "normal").
`sym`	Logical specifying if the evaluation is symmetric (default: TRUE) - see `evaluate`.
`seed`	A single value, interpreted as an integer to specify seeds, useful for creating simulations that can be reproduced (default: `NULL`) - see `set.seed`.

The argument methods accepts "all.fast" and "all" (case insensitive) as a parameters:

"all.fast" performs network inference with "aracne", "c3net", "clr", "GeneNet", "mutual ranking", "mrnetb", "pcit"
"all" performs network inference with "aracne", "c3net", "clr", "GeneNet", "Genie3", "mutual ranking", "mrnet", "mrnetb", "pcit"

It evaluates the first no.topedges % of the possible links inferred by each algorithm at each dataset.

noise.bench returns a list with three elements:

A data.frame which is the result table containing the number of true positives as an evaluation measure. It evaluates each algorithm specified at methods at each one of the specified datasources.names with the local.noise and global.noise specified. For each combination the algorithms are evaluated datasets.num times and their results are averaged.
A data.frame which is the corresponding pvalue table of the corresponding statistical test for each one of the datasets.num between the best algorithm and the others.
The seed of the random number generators that allows the replication of the results.