covarianceSelectionBisection: Covariance Selection with Bisection Optimization

In Sage-Bionetworks/metanetwork: Network meta analysis

Covariance Selection with Bisection Optimization

Description

Selects an optimal covariance matrix through BIC convergence.

Usage

covarianceSelectionBisection(
  S,
  rankedEdges,
  numberObservations,
  lowerBoundEdge,
  upperBoundEdge
)

Arguments

S	Required. A symetric p-by-p covariance matrix.
rankedEdges	Required. A list of ranked edges to be constrained by zero.
numberObservations	Required. Number of observations used to calculate BIC estimates.
lowerBoundEdge	Required. Numeric specifying the lower bound number of parameters (d) in BIC calculation: 'BIC = -2 * loglikelihood + d * log(N)'
upperBoundEdge	Required. Numeric specifying the upper bound number of parameters (d) in BIC calculation: 'BIC = -2 * loglikelihood + d * log(N)'

Value

A list containg

• 'w' Estimated inverse covariance matrix.

• 'resMiddle' The converged sparse inverse covariance matrix.

• 'bicMiddle' The converged BIC estimate.

• 'middleEdge' The converged estimate of parameters.

Sage-Bionetworks/metanetwork documentation built on April 27, 2022, 7:42 a.m.