squared_jumping: Squared jumping of adaptive MCMC algorithm

In anna-pacinkova/intomics_package: Integrative analysis of multi-omics data to infer regulatory networks

Squared jumping of adaptive MCMC algorithm

Description

squared_jumping Squared jumping of adaptive MCMC algorithm is used to tune the variance of the beta parameter.

Usage

squared_jumping(
  second.adapt.phase_net,
  constant,
  fin,
  beta_sd,
  B_prior_mat,
  omics,
  parent_set_combinations,
  BGe_score_all_configs_node,
  layers_def,
  prob_mbr,
  annot
)

Arguments

second.adapt.phase_net	list output of the variance_target or squared_jumping function.
constant	numeric vector used to multiply the beta_sd to determine the variance of the distribution of the hyperparameter beta.
fin	numeric vector iteration to stop.
beta_sd	numeric vector used to determine the variance of the distribution of the hyperparameter beta.
B_prior_mat	a biological prior matrix.
omics	named list containing the gene expression (possibly copy number variation and methylation data). Each component of the list is a matrix with samples in rows and features in columns.
parent_set_combinations	list of all possible parent set configuration for all nodes available.
BGe_score_all_configs_node	list of nodes BGe score for all possible parent set configurations.
layers_def	data.frame containing the modality ID, corresponding layer in BN and maximal number of parents from given layer to GE nodes.
prob_mbr	numeric vector probability of the MBR step.
annot	named list containing the associated methylation probes of given gene.

Value

List of 1 element: second adaptive phase result with stopped MCMC mixing

anna-pacinkova/intomics_package documentation built on Aug. 13, 2022, 11:38 a.m.