nrm_selection

0th

Percentile

Perform AIC forward selection for nrm.

Perform AIC forward selection for nrm.

Usage
nrm_selection(
  adj,
  predictors,
  directed,
  selfloops,
  pval = 0.05,
  xi = NULL,
  init = NULL,
  ncores = NULL,
  ...
)

# S3 method for default nrm_selection( adj, predictors, directed, selfloops, pval = 0.05, xi = NULL, init = NULL, ncores = NULL, ... )

# S3 method for nrmpredictor nrm_selection( adj, predictors, directed, selfloops, pval = 0.05, xi = NULL, init = NULL, ncores = NULL, ... )

# S3 method for nrm_selection print(x, ...)

Arguments
adj

the adjacency matrix of the response network

predictors

list containing the set of predictors as sublists.

directed

logical, is the response network directed?

selfloops

logical, do the response network allows selfloops?

pval

the significance at which computing confidence intervals.

xi

optional, the possibility matrix \(\Xi\).

init

optional, initial values passed to the solver to estimate the MLE.

ncores

optional, number of cores over which parallelise the task.

optional arguments to print or plot methods.

x

object of class 'nrm_selection'.

Value

A nrm object

Methods (by class)

  • default: Default method for the nrm stepwise selection.

  • nrmpredictor: Method for the nrm stepwise selection when nrmpredictors are passed.

  • nrm_selection: Print method for elements of class 'nrm_selection'.

See Also

nrm

nrm_selection

Aliases
  • nrm_selection
  • nrm_selection.default
  • nrm_selection.nrmpredictor
  • print.nrm_selection
Examples
# NOT RUN {
data('highschool.predictors')
nrm_selection(adj=contacts.adj,predictors=createPredictors(highschool.predictors),
  ncores=1,directed=FALSE,selfloops=FALSE)
 
# }
Documentation reproduced from package ghypernet, version 1.0.0, License: AGPL-3

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