tune.knn.impute

data

vector containing the categorical variables

cat.var

k.min

k.max

frac.miss

n.iter

seed


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


Bayesian Network Structure Learning from Data with Missing Values.
The package implements the Silander-Myllymaki complete search,
the Max-Min Hill-climbing heuristic search, 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.

Francesco Sambo

bnstruct

Bayesian Network Structure Learning from Data with Missing
Values

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.

Description

Usage

Arguments

Value