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Imputation by classification and regression trees

Source: R/imputations.R
impute.mice_cart.Rd

Apply imputation to the dataset by classification and regression trees (Breiman et al. 1984; Doove et al. 2014; van Buuren 2018) .

Usage

impute.mice_cart(dataSet, reportImputing = FALSE, m = 5, seed = 362436069)

Arguments

dataSet

The 2d dataset of experimental values.

reportImputing

A boolean (default = FALSE) specifying whether to provide a shadow data frame with imputed data labels, where 1 indicates the corresponding entries have been imputed, and 0 indicates otherwise. Alters the return structure.

m

An integer (default = 5) specifying the number of multiple imputations.

seed

An integer (default = 362436069) specifying the seed used for the random number generator for reproducibility.

Value

  • If reportImputing = FALSE, the function returns the imputed 2d dataframe.

  • If reportImputing = TRUE, the function returns a list of the imputed 2d dataframe and a shadow matrix showing which proteins by replicate were imputed.

References

Breiman L, Friedman J, Olshen RA, Stone CJ (1984). Classification and Regression Trees. Routledge, New York, NY, USA. ISBN 9780412048418.

Doove LL, van Buuren S, Dusseldorp E (2014). “Recursive Partitioning for Missing Data Imputation in the Presence of Interaction Effects.” Computational Statistics & Data Analysis, 72, 92–104. doi:10.1016/j.csda.2013.10.025 .

van Buuren S (2018). Flexible Imputation of Missing Data. Chapman \& Hall/CRC, New York, NY, USA. ISBN 9781032178639.