ce.impute: Imputation in supervised classification
In dprep: Data Pre-Processing and Visualization Functions for Classification

Description Usage Arguments Value Note Author(s) References See Also Examples

This function performs data imputation in datasets for supervised classification by using mean, median or knn imputation methods. The mode is used when the attribute is nominal

1 2	ce.impute(data, method = c("mean", "median", "knn"), atr, nomatr = rep(0, 0), k1 = 10)

`data`	the name of the dataset
`method`	the name of the method to be used
`atr`	a vector identifying the attributes where imputations will be performed
`nomatr`	a vector identifying the nominal attributes
`k1`	the number of neighbors to be used for the knn imputation

Returns a matrix without missing values.

A description of all the imputations carried out may be stored in a report that is later saved to the current workspace. To produce the report, lines at the end of the code must be uncommented. The report objects name starts with Imput.rep.

Caroline Rodriguez

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.

clean

data(hepatitis)
#--------Median Imputation-----------
#ce.impute(hepatitis,"median",1:19)
#--------knn Imputation--------------
hepa.imputed=ce.impute(hepatitis,"knn",k1=10)