VIM-package(VIM)
VIM-package()所属R语言包：VIM
Visualization and Imputation of Missing Values
可视化和插补遗漏值
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This package introduces new tools for the visualization of missing or imputed values in R, which can be used for exploring the data and the structure of the missing or imputed values. Depending on this structure, they may help to identify the mechanism generating the missing values or errors, which may have happened in the imputation process. This knowledge is necessary for selecting an appropriate imputation method in order to reliably estimate the missing values. Thus the visualization tools should be applied before imputation and the diagnostic tools afterwards.
这个包丢失或估算值R，可以用来研究数据和结构的丢失或估算值的可视化，引入了新的工具。根据这种结构，它们可能有助于识别机制产生的遗漏值或错误，这可能发生在插补过程中。这方面的知识是必要的，选择适当的插补方法，以可靠地估计缺失值的。因此前归集和诊断工具后，应采用可视化工具。
Detecting missing values mechanisms is usually done by statistical tests or models. Visualization of missing and imputed values can support the test decision, but also reveals more details about the data structure. Most notably, statistical requirements for a test can be checked graphically, and problems like outliers or skewed data distributions can be discovered. Furthermore, the included plot methods may also be able to detect missing values mechanisms in the first place.
遗漏值的检测机制通常是通过统计检验或模型。和估算值的可视化支持测试的决定，但也揭示了更多的关于数据结构的细节。最值得注意的是，统计要求的测试可以检查图形化的问题，像离群或倾斜的数据分布可以发现。此外，包括图的方法，也可能是能够检测遗漏值的机制摆在首位。
A graphical user interface allows an easy handling of the plot methods. In addition, VIM can be used for data from essentially any field.
图形用户界面允许一个简单的图处理方法。此外，VIM可以从基本上任何字段用于数据。
Details
详细信息----------Details----------
（作者）----------Author(s)----------
Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner
Maintainer: Matthias Templ <templ@tuwien.ac.at>
参考文献----------References----------
Exploring incomplete data using visualization tools. Journal of Advances in Data Analysis and Classification, Online first. DOI: 10.1007/s11634-011-0102-y.
Iterative stepwise regression imputation using standard and robust methods. Journal of Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 16:08:28
