Imputation with the R Package VIM
Imputation with the R Package VIM
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DOI：
10.18637/jss.v074.i07
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发表时间：
2016-10-01
影响因子：
5.8
通讯作者：
Templ, Matthias
中科院分区：
文献类型：
--
作者：
Kowarik, Alexander;Templ, Matthias
开发了软件包vim（Templ，Alfons，Kowarik和Prantner 2016），以探索和分析数据中数据中缺少值的结构，以将这些缺失的值用内置的插图方法归咎于这些丢失的值并使用使用插入过程来验证这些缺失的值可视化工具以及为出版物生成高质量的图形。本文重点介绍包装中可用的不同插补技术。目前在VIM中实施了四种不同的插补方法，即热甲板插补，k-near最邻居插补，回归插补和基于迭代稳健模型的插补（Templ，Kowarik和Filzmoser 2011）。所有这些方法都是以灵活的方式实现的，并具有许多自定义选项。此外，在本文中，还提供了实践示例，以突出在现实世界应用程序上使用实现方法的使用。此外，VIM的图形用户界面已从SCRATCH中重新实现，从而导致vimgui（Schopfhauser，templ，templ，alfons，alfons，alfons，alfons，alfons ,, Kowarik和Prantner 2016）使用户无需大量的R技能即可访问这些插补和可视化方法。
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.