partial9_inc: Estimates an adjacencey matrix for a DAG based on l1...

Description Usage Arguments Value Examples

View source: R/partialDAGs.R

Description

Estimates an adjacencey matrix for a DAG based on l1 penalized negative likelihood minimization given a partitioning of the nodes into two groups

Usage

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partial9_inc(
  X,
  l,
  a = NULL,
  m1 = NULL,
  m2 = NULL,
  m3 = NULL,
  m4 = NULL,
  m5 = NULL,
  m6 = NULL,
  m7 = NULL,
  m8 = NULL,
  m9 = NULL,
  l1 = NULL,
  l2 = NULL,
  l3 = NULL,
  l4 = NULL,
  l5 = NULL,
  l6 = NULL,
  l7 = NULL,
  l8 = NULL,
  l9 = 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

the node at which the first partition occurs

m2

the node at which the second partition occurs

m3

the node at which the third partition occurs

m4

the node at which the fourth partition occurs

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

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partial5_inc(X = X, l = 2, m1 = 12, m2 = 24, m3 = 36, m4 = 48)

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