Unified cellwise-robust imputation dispatcher

Convenience wrapper that dispatches to one of the three cellwise-robust imputation methods: imputeCellIRMI, imputeCellM, or imputeCellEM.

imputeCellwise(data, method = c("cellIRMI", "cellM", "cellEM"), ...)

Arguments

data

data.frame with missing values (mixed continuous + categorical).

method

imputation method: "cellIRMI" (default), "cellM", or "cellEM".

...

additional arguments passed to the chosen method.

Value

The return value of the dispatched function. See the documentation of the individual methods for details.

Note

Model uncertainty via bootstrap (Rubin's combining rules for multiple imputation) is not yet implemented. The current version provides single imputation with stochastic uncertainty (PMM or residual draw). For valid multiple imputation, call the function repeatedly with different seeds and combine using Rubin's rules.

See also

imputeCellIRMI, imputeCellM, imputeCellEM

Other imputation methods: hotdeck(), impPCA(), imputeCellEM(), imputeCellIRMI(), imputeCellM(), imputeCellMCD(), imputeRobust(), imputeRobustChain(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), regressionImp(), sampleCat(), vimmi, vimpute(), xgboostImpute()

Author

Matthias Templ

Examples

# \donttest{
data(sleep, package = "VIM")
result <- imputeCellwise(sleep, method = "cellIRMI")
head(result$data_imputed)
#>   BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
#> 1   0.785      6.4  8.6   1.9  10.6 38.6   60    3   2      3
#> 2   1.000      6.6  6.3   2.0   8.3  4.5   42    3   1      3
#> 3   3.385     44.5  8.4   1.9  10.3 14.0   60    1   1      1
#> 4   0.920      5.7  8.4   2.1  10.6  3.9   25    5   2      3
#> 5   2.500     15.5  2.1   1.8   3.9 34.0   31    3   1      4
#> 6   1.700     25.6  9.1   0.7   9.8 27.0  180    4   4      4
# }