Imputation with the R Package VIM
By Alexander Kowarik, Matthias Templ. 2016
634 citations recorded by the source corpus.
The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the imputation process using visualization tools, as well as to produce high-quality graphics for publications. This article focuses on the different imputation techniques available in the package. Four different imputation methods are currently implemented in VIM, namely hot-deck imputation, k-nearest neighbor imputation, regression imputation and iterative robust model-based imputation (Templ, Kowarik, and Filzmoser 2011). All of these methods are implemented in a flexible manner with many options for customization. Furthermore in this article practical examples are provided to highlight the use of the implemented methods on real-world applications. In addition, the graphical user interface of VIM has been re-implemented from scratch resulting in the package VIMGUI (Schopfhauser, Templ, Alfons, Kowarik, and Prantner 2016) to enable users without extensive R skills to access these imputation and visualization methods.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/422e89dd66613c3abbc9c6562e0533ff05ae4edc
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Citations in the corpus
- Comparison of Performance of Data Imputation Methods for Numeric Dataset (2019, neutral): “The details about VIM package is described by Kowarik and Templ (2016).”
- MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets (2019, methodology): “…we chose the mean imputation, which is an often inaccurate baseline, the kNN approach of Troyanskaya et al. (2001) fit using the VIM R package (Kowarik & Templ, 2016), with the number of neighbours chosen in {5, ..., 15} using 5-fold cross validation; the missForest approach of Stekhoven &…”
- imputeTS: Time Series Missing Value Imputation in R (2017, neutral): “Most popular and mature (among others) are AMELIA (Honaker et al., 2011), mice (van Buuren and Groothuis-Oudshoorn, 2011), VIM (Kowarik and Templ, 2016) and missMDA (Josse and Husson, 2016).”