bnc | R Documentation |
A convenience function to learn the structure and parameters in a single
call. Must provide the name of the structure learning algorithm function;
see bnclassify
for the list.
bnc( dag_learner, class, dataset, smooth, dag_args = NULL, awnb_trees = NULL, awnb_bootstrap = NULL, manb_prior = NULL, wanbia = NULL )
dag_learner |
A character. Name of the structure learning function. |
class |
A character. Name of the class variable. |
dataset |
The data frame from which to learn network structure and parameters. |
smooth |
A numeric. The smoothing value (α) for Bayesian parameter estimation. Nonnegative. |
dag_args |
A list. Optional additional arguments to |
awnb_trees |
An integer. The number (M) of bootstrap samples to generate. |
awnb_bootstrap |
A numeric. The size of the bootstrap subsample,
relative to the size of |
manb_prior |
A numeric. The prior probability for an arc between the class and any feature. |
wanbia |
A logical. If |
data(car) nb <- bnc('nb', 'class', car, smooth = 1) nb_manb <- bnc('nb', 'class', car, smooth = 1, manb_prior = 0.3) ode_cl_aic <- bnc('tan_cl', 'class', car, smooth = 1, dag_args = list(score = 'aic'))
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