BI: Objective function of Beyesion information criterion (BIC)
In xzheng-ac/scPADGRN: A PADMM approach for reconstructing DGRN using scRNA-seq

Description Usage Arguments Value References Examples

This function returns the object function of Beyesion information criterion (BIC) under designated parameters.

1	BI(Y, A, alpha, beta, eps)

`Y`	Cluster-level data, a list of matrix, stand for gene expression data at different time points.
`A`	Dynamic gene regulatory network, a list of numerical matrix, obtained by scPADGRN.
`alpha`	Turing parameter in scPADGRN model, controls the weight of sparsity.
`beta`	Turing parameter in scPADGRN model, controls the weight of continuity.
`eps`	Threshold, controls "Dim" function in objective function of BIC optimization problem.

`Bi`	objective function of BIC optimization problem.

Li Y, Jin S, Lei L, Pan Z, Zou X. Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis. Scientific Reports. 2015;5:9283.

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (Y, A, alpha, beta, eps)
{
    of <- objFunc(Y, A, alpha, beta)
    dim <- dimList(A, eps)
    Bi <- log(of) - log(dim)
    return(Bi)
  }