Fast Pure R Implementation of GEE: Application of the Matrix Package.

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2013-06-01
Lee S McDaniel, Nicholas C Henderson, Paul J Rathouz
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引用次数: 0

Abstract

Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.

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GEE的快速纯R实现:矩阵包的应用。
R中的广义估计方程求解器只允许为链接函数和方差函数提供一些预先确定的选项。我们提供了一个包geeM,它完全用R实现,允许用户指定链接和方差函数。matrix包中提供的稀疏矩阵表示支持快速实现。为了提高速度,我们尽可能使用工作相关的解析逆,并在无法使用解析逆时使用快速求数值逆的技巧。通过三个示例,我们演示了geeM的速度,它在小数据集上并不比gepack和gee等C实现差多少,在大数据集上更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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