ce.impute {dprep} | R Documentation

## Imputation in supervised classification

### Description

This function performs imputation by mean, median and knn in datasets for supervised classification.

### Usage

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

### Arguments

`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

### Value

Returns a matrix without missing values.

### Note

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.

### Author(s)

Caroline Rodriguez

### 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.

### See Also

`clean`

### Examples

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

[Package dprep version 1.0 Index]