ccdr_custom: Estimates an adjacencey matrix for a DAG based on l1...

In shr264/partitionDAG: ESTIMATION OF GAUSSIAN DIRECTED ACYCLICGRAPHS USING PARTIAL ORDERING INFORMATION

Description

Estimates an adjacencey matrix for a DAG based on l1 penalized negative likelihood minimization

Usage

ccdr_custom(
  X,
  l,
  a = NULL,
  m1 = NULL,
  m2 = NULL,
  m3 = NULL,
  m4 = NULL,
  m5 = NULL,
  m6 = NULL,
  m7 = NULL,
  m8 = NULL,
  m9 = NULL,
  eps = 10^(-4),
  maxitr = 100,
  init = NULL
)

Arguments

X	a matrix of size n by p containing n observations an p variables
l	penalization parameter
m1	not relevant
m2	not relevant
m3	not relevant
m4	not relevant
m5	not relevant
m6	not relevant
m7	not relevant
m8	not relevant
m9	not relevant
eps	tolerance parameter to decide whether algorithm has converved or not
maxitr	maximum number of iterations to run before returning output
init	initial estimate of graph adjacency B

Value

graph adjacency B

Examples

ccdr_custom(X = X, l = 2)

shr264/partitionDAG documentation built on Nov. 23, 2021, 10:28 p.m.