bn_tabu_gen {GmicR} | R Documentation

## Uses tabu search algorithm to learn the structure of discretized data.

### Description

Uses tabu search algorithm to learn the structure of discretized data.

### Usage

    bn_tabu_gen(
      Auto_WGCNA_OUTPUT,
      whitelist = NULL,
      blacklist = NULL,
      score = "bde",
      tabu = 50,
      iss = 10,
      cluster = NULL,
      debug = TRUE,
      bootstraps_replicates = 500
    )

### Arguments

`Auto_WGCNA_OUTPUT` | an R object generated by Auto_WGCNA and discretized using the Data_Prep function.
`whitelist` | a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.
`blacklist` | a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.
`score` | character string indicating the score used for structure learning. If "bde" (default), prior is set to "uniform". If bds is used, the prior is set to "marginal".
`tabu` | a positive integer number, the length of the tabu list used in the `tabu` function.
`iss` | the imaginary sample size, used by the Bayesian Dirichlet scores (bde and bds) It is also known as “equivalent sample size”. The default value is equal to 10.
`cluster` | an optional cluster object from package parallel.
`debug` | a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.
`bootstraps_replicates` | an integer for the number of bootstraps_replicates used for structure learning. Default value is 500

### Value

The learned bayesian network

### See Also

`arc.strength`

`hc`

`score`

### Examples

    GMIC_Builder_disc_dir<-system.file("extdata", "GMIC_Builder_disc.Rdata",
    package = "GmicR", mustWork = TRUE)
    load(GMIC_Builder_disc_dir)

    no_cores<-1
    cl<-parallel::makeCluster(no_cores)

    GMIC_net<-bn_tabu_gen(GMIC_Builder_disc,
    cluster = cl,
    bootstraps_replicates = 50, score = "bds")
    parallel::stopCluster(cl)

[Package GmicR version 1.6.0 Index]