var_mat: Estimate variance-covariance matrixes for any parameters of...

In BNrich: Pathway Enrichment Analysis Based on Bayesian Network

Description

Estimate variance-covariance matrixes for any parameters of

Usage

var_mat(Data_h, coef_H, BNs_H, Data_d, coef_D, BNs_D)

Arguments

Data_h	A list contains data frames related to control objects for any BN
coef_H	A lists of parameters of BN_h achieved
BNs_H	A list of BNs learned by control objects data
Data_d	A list contains data frames related to disease objects for any BN
coef_D	A lists of parameters of BN_d
BNs_D	A list of BNs learned by disease objects data

Value

A listcontains two lists var_mat_Bh and var_mat_Bd

Examples

#All the 187 preprocessed signaling pathways can be entered in analysis by fetch_data_file().
#But here you enter a subset of those pathways to see how this package works.
files <- system.file("extdata", "test_files_to_start.RData", package = "BNrich", mustWork = TRUE)
load(files)
Data <- system.file("extdata", "Test_DATA.RData", package = "BNrich", mustWork = TRUE)
load(Data)
uni_Result <- unify_path(dataH, dataD, MapkG = sub_mapkG, Pathway.id = path.id)
M1 <- uni_Result$mapkG1
BN <- BN_struct(M1)
data_h1 <- uni_Result$data_h
data_d1 <- uni_Result$data_d
LASSO_Result <- LASSO_BN(BN = BN , data_h = data_h1 , data_d = data_d1)
BN_h1 <- LASSO_Result$BN_h
BN_d1 <- LASSO_Result$BN_d
esti_result <- esti_par(BN_H = BN_h1, BN_D = BN_d1, data_h = data_h1, data_d = data_d1)
BNs_H <- esti_result$BNs_h
BNs_D <- esti_result$BNs_d
coef_h <- esti_result$coef_h
coef_d <- esti_result$coef_d
var_result <- var_mat(data_h1, coef_h,  BNs_H, data_d1, coef_d, BNs_D)

BNrich documentation built on April 14, 2020, 7:08 p.m.