dprep (version 2.1)

ec.knnimp: KNN Imputation

Description

Function to carry out KNN imputation of missing values.

Usage

ec.knnimp(data, nomatr, k = 10)

Arguments

data

Original dataset with missing values

nomatr

Vector containing the indices of nominal attributes

k

Numeric value representing the number of neighbors to use for imputation

Value

• data2
  contains values belonging to one class (of a larger matrix) for which missing values in relevant variables have been imputed.

Details

This function is called by the function ce.knn.imp which is part of this library, to impute values by class. If called alone the function will impute values based on information in the entire matrix and not the classes. Needs also the function: nnmiss.

References

Acuna, E. and Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy. In D. Banks, L. House, F.R. McMorris, P. Arabie, W. Gaul (Eds). Classification, Clustering and Data Mining Applications. Springer-Verlag Berlin-Heidelberg, 639-648.

Examples

#---- Performing knn imputation----
data(hepatitis)
hepa.knnimp=ec.knnimp(hepatitis,k=10)