Package:
VIM
Category:
Single Imputation, Visualisations for Missing Data
Use-Cases:
Single imputation for numerical, categorical, ordered and semi-continous variables, Missing data visualizations
Popularity:
Description:
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
Last update:
Algorithms:
- irmi Iterative robust model-based imputation (IRMI)
- kNN k-Nearest Neighbour Imputation
- hotdeck Hot-Deck Imputation
- regressionImp Regression Imputation
Datasets:
- tao Tropical (Atmosphere Ocean (TAO) project data)
- testdata (Simulated data set for testing purpose)
- sleep (Mammal sleep data)
Further Information:
Alexander Kowarik, Matthias Templ (2016). Imputation with the R Package VIM. Journal of Statistical Software, 74(7), 1-16. doi:10.18637/jss.v074.i07
https://datascienceplus.com/graphical-presentation-of-missing-data-vim-package/
https://datasciencebeginners.com/2018/11/07/visualization-of-imputed-values-using-vim/
Input:
data.frame, matrix
Example:
library("VIM")
# load dataset sleep, whcih contains NAs
data(sleep, package = "VIM")
print("before imputation")
summary(sleep)
# Perfom imputation with kNN
x <- kNN(sleep)
print("after imputation")
summary(x)
Here you can have a interactive look at the example:
https://rdrr.io/snippets/embedding/