| Title: | Multivariate Imputation by 'Mahalanobis' Distance Optimization |
|---|---|
| Description: | Imputes missing values of an incomplete data matrix by minimizing the 'Mahalanobis' distance of each sample from the overall mean. |
| Authors: | Geovert John Labita [aut, cre] |
| Maintainer: | Geovert John Labita <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-02-21 05:45:09 UTC |
| Source: | https://github.com/gjlabita/mimdo |
Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean. By utilizing Mahalanobis distance, this imputation method is preferable to be used on datasets with highly correlated variables.
mimdo(incomplete_data, inverse, iterations = 30)mimdo(incomplete_data, inverse, iterations = 30)
incomplete_data |
A data frame with missing values. |
inverse |
If TRUE, the inverse covariance matrix will be used for distance calculation. If the covariance matrix is non-invertible, use inverse = FALSE. |
iterations |
Number of iterations. It can be adjusted to avoid long running time. |
The output is a complete imputed data matrix.
Geovert John D. Labita
Labita, GJ.D. and Tubo, B.F. (2024). Missing data imputation via optimization approach: An application to K-means clustering of extreme temperature. Reliability: Theory and Applications, 2(78), 115-123. DOI: https://doi.org/10.24412/1932-2321-2024-278-115-123
Bertsimas, D., Pawlowski, C., and Zhou, Y.D. (2018). From predictive methods to missing data imputation: An optimization approach. Journal of Machine Learning Research, 18(196), 1-39.
incomplete_data<-as.data.frame(matrix(c(5.1,NA,4.7,NA,3.0,3.2,1.4,1.4,NA,0.2,0.2,NA),nrow=3)) mimdo(incomplete_data, inverse=FALSE)incomplete_data<-as.data.frame(matrix(c(5.1,NA,4.7,NA,3.0,3.2,1.4,1.4,NA,0.2,0.2,NA),nrow=3)) mimdo(incomplete_data, inverse=FALSE)