哪些 R 包可用于关联和聚类的二进制数据？
我现在正在做一个项目，它与我以前做过的任何事情都不同。我有两个具有二进制结果的测试，这些测试将针对同一样本进行，该样本是从聚集的人群中抽取的（即，一些受试者来自同一家庭）。我想比较阳性测试结果的比例，但聚类使得麦克尼马尔的测试不合适，所以我一直在阅读替代方法。两条主要路线似乎是 1) Rao 和 Scott (1992)、Eliasziw 和 Donner (1991) 以及 Obuchowski (1998) 的聚类调整 McNemar 替代方案，以及 2) GEE。
您知道 Rao-Obuchowski 谱系在 R（或者我想是 SAS）中的任何实现吗？ GEE 很容易找到，但您对任何特定套餐有过正面或负面的体验吗？是否还有另一种我完全缺失的分析这些数据的途径？
I'm working on a project now that's rather unlike anything I've done before. I have two tests with binary results that will be administered to the same sample, which is drawn from a clustered population (i.e., some subjects will be from the same family). I'd like to compare proportions of positive test results, but the clustering makes McNemar's test inappropriate so I've been reading up on alternative approaches. The two main routes seem to be 1) the clustering-adjusted McNemar alternatives by Rao and Scott (1992), Eliasziw and Donner (1991), and Obuchowski (1998), and 2) GEE.
Do you know of any implementations of the Rao-Obuchowski lineage in R (or, I suppose, SAS)? GEE is easy to find, but have you had a positive or negative experience with any particular packages? Is there another route to analyzing these data that I'm completely missing?
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您始终可以只使用集群引导程序。跨家庭重新抽样，您认为这些家庭是独立的。也就是说，当您重新采样时，让家人在一起。计算每个样本的
p2 - p1。大约 1000 次迭代后，计算上限和下限 2.5% 分位数。这将为您提供自举的 95% 置信区间。或者计算高于零的样本比例，或者无论你的假设是什么。除非家庭数量很少，否则该过程应该具有相当好的性能。在 R 中手动完成此操作可能比依赖任何包最简单。
You could always just use a clustered bootstrap. Resample across families, which you believe are independent. That is, keep families together when you resample. Compute
p2 - p1for each sample. After 1000 iterations or so, compute the upper and bottom 2.5% quantiles. This will give you a bootstrapped 95% confidence interval. Alternatively compute the fraction of samples above zero, or whatever your hypothesis is. The procedure should have good pretty good properties unless the number of families is small.It's probably easiest to do this by hand in R rather than relying on any package.
查看
survey包：它的设计目的是考虑集群抽样引起的相关性。Check out the
surveypackage: it is designed to take into account correlations induced by clustered sampling.您是否已经检查过 R 中的 CorrBin 包？
它用于分析相关的二进制数据，有一篇论文名为： 使用 CorrBin 包进行非参数分析
Szabo 的相关二进制数据，它包括 Rao-Scott、随机排序和基于 GEE 的测试的三个版本。
Have you already checked the CorrBin package in R?
It is for analysis of correlated binary data, there is a paper named: Using the CorrBin package for nonparametric analysis of
correlated binary data by Szabo, it includes the Rao-Scott, stochastic ordering and three versions of a GEE-based test.
用于集群二进制匹配的 clust.bin.pair 包配对数据最近发布到 CRAN。
它包含 Eliasziw 和 Donner (1991) 和 Obuchowski (1998) 的实现，以及同一家族 Durkalski (2003) 和 Yang (2010) 中最近的两个测试。
The clust.bin.pair package for clustered binary matched-pair data was recently published to CRAN.
It contains implementations of Eliasziw and Donner (1991) and Obuchowski (1998), as well as two more recent tests in the same family Durkalski (2003) and Yang (2010).