tune.knn.impute

data

<p>vector containing the categorical variables</p>

cat.var

k.min

k.max

frac.miss

n.iter

seed

<p>tune the parameter k of the knn algorithm used in imputation.</p>

Bayesian Network Structure Learning from Data with Missing Values.
The package implements the Silander-Myllymaki complete search,
the Max-Min Parents-and-Children, the Hill-Climbing, the
Max-Min Hill-climbing heuristic searches, and the Structural
Expectation-Maximization algorithm. Available scoring functions are
BDeu, AIC, BIC. The package also implements methods for generating and using
bootstrap samples, imputed data, inference.

Alberto Franzin

bnstruct

Bayesian Network Structure Learning from Data with Missing
Values

Francesco Sambo

tune.knn.impute function

tune the parameter k of the knn algorithm used in imputation. — tune.knn.impute

tune the parameter k of the knn algorithm used in imputation.

tune.knn.impute: tune the parameter k of the knn algorithm used in imputation.

Description

Usage

Arguments

Value