subsample_multiJGL: Function for running multiJGL algorithm across subsamples and...

In KontioJuho/multiJGL: What the Package Does (One Line, Title Case)

Function for running multiJGL algorithm across subsamples and different sparsity levels

Description

Function for running multiJGL algorithm across subsamples and different sparsity levels

Usage

subsample_multiJGL(
  node.covariates = node.covariates,
  grouping.factor = grouping.factor,
  lambda.seq,
  by_value,
  num_repetitions = 10,
  penalty.lin = "fused",
  penalty.nonlin = "fused",
  lin_lambda1 = lambda.seq1[j],
  lin_lambda2 = 0.025,
  nonlin_lambda1 = lambda.seq1[j],
  nonlin_lambda2 = 0.025,
  tol.linear = 1e-05,
  tol.nonlinear = 1e-05,
  subsample.pseudo_obs = FALSE,
  omit.rate = 2L
)

Arguments

node.covariates	An nxp dimensional matrix of p covariates measured over n samples.
grouping.factor	A grouping factor for creating observational classes.
lambda.seq	The range of different lambda values
by_value	Specifies the density of lambda grid
num_repetitions	The number of subsample analyses
penalty.lin	Specify "fused" or "group" penalty type for the linear JGL algorithm.
penalty.nonlin	Specify "fused" or "group" penalty type for the nonlinear JGL algorithm.
lin_lambda1	The l1-penalty parameter for the linear JGL to regulate within group network densities
lin_lambda2	The l1-penalty parameter for the nonlinear JGL.
nonlin_lambda1	The fusion penalty parameter for the linear JGL.
nonlin_lambda2	The fusion penalty parameter for the nonlinear JGL.
tol.linear	Convergence criterion for the linear part (see the JGL package for details).
tol.nonlinear	Convergence criterion for the nonlinear part.
subsample.pseudo_obs	Should the subsampling procedure be used over the pseudo-observations.
omit.rate	An integer: Omit rate for the subsampling pcocedure between 2L and 5L

Examples

print("")

KontioJuho/multiJGL documentation built on Oct. 30, 2022, 3:42 a.m.