nrmSelection | R Documentation |
Perform AIC forward selection for nrm.
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, ...)
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 |
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 |
A nrm object
nrm_selection(default)
: Default method for the nrm stepwise selection.
nrm_selection(nrmpredictor)
: Method for the nrm stepwise selection when nrmpredictors are passed.
print(nrm_selection)
: Print method for elements of class 'nrm_selection'
.
Giona Casiraghi
nrm
nrm_selection
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)
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