nrmSelection {ghypernet}

Perform AIC forward selection for nrm.

Description

Perform AIC forward selection for nrm.

Usage

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

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

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

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

## S3 method for class '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'.

Author(s)

Giona Casiraghi

Giona Casiraghi

See Also

nrm

nrm_selection

Examples

data('highschool.predictors')
models <- nrm_selection(adj=contacts.adj,predictors=create_predictors(highschool.predictors),
  ncores=1,directed=FALSE,selfloops=FALSE)
texreg::screenreg(models$models, digits=3)