partial9: 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(
  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

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

the node at which the fifth partition occurs

m6

the node at which the sixth partition occurs

m7

the node at which the seventh partition occurs

m8

the node at which the eighth partition occurs

m9

the node at which the ninth partition occurs

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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partial9(X = X, l = 2, m1 = 12, m2 = 24, m3 = 36, m4 = 48, m5 = 60, m6 = 72, m7 = 84, m8 = 96)

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