knn.impute

data

number of neighbours to be used; for categorical variables
the mode of the neighbours is used, for continuous variables
the median value is used instead. Default: 10.

vector containing the indices of the variables to be
considered as categorical. Default: all variables.

cat.var

vector indicating which rows of the dataset are to be imputed.
Default: impute all rows.

to.impute

vector indicating which rows of the dataset are to be used
       to search for neighbours. Default: use all rows.

using


Perform imputation of missing data in a data frame using the k-Nearest Neighbour algorithm.
For discrete variables we use the mode, for continuous variables the median value is instead taken.


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.

knn.impute: Perform imputation of a data frame using k-NN.

Description

Usage

Arguments

Value