bn_tabu_gen {GmicR} | R Documentation |
Uses tabu search algorithm to learn the structure of discretized data.
bn_tabu_gen( Auto_WGCNA_OUTPUT, whitelist = NULL, blacklist = NULL, score = "bde", tabu = 50, iss = 10, cluster = NULL, debug = TRUE, bootstraps_replicates = 500 )
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
|
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 |
bootstraps_replicates |
an integer for the number of bootstraps_replicates used for structure learning. Default value is 500 |
The learned bayesian network
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)