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Activity Number:
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279
- Nonparametric Tests and Estimations for Clustered Data: It Is Essential for Non-Normal Data
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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International Chinese Statistical Association
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Abstract #322761
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View Presentation
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Title:
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Wilcoxon Rank-Based Tests for Clustered Data with R Package Clusrank
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Author(s):
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Jun Yan* and Yujing Jiang and Mei-Ling Ting Lee and Xin He and Bernard Rosner
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Companies:
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University of Connecticut and University of Connecticut and University of Maryland and University of Maryland, College Park and Harvard Medical School
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Keywords:
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rank-sum test ;
signed-rank test
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Abstract:
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Wilcoxon Rank-based tests are distribution-free alternatives to the popular two-sample and paired t-tests. For independent data, they are available in several R packages such as stats and coin.. For clustered data, in spite of the recent methodological developments, there did not exist an R package that makes them available at one place. We present a package clusrank where the latest developments are implemented and wrapped under a unified user-friendly interface. With different methods dispatched based on the inputs, this package offers great flexibility in rank-based tests for various clustered data. Exact tests based on permutations are also provided for some methods. Details of the major schools of different methods are briefly reviewed. Usages of the package clusrank are illustrated with simulated data as well as a real dataset from an ophthalmological study. The package also enables convenient comparison between selected methods under settings that have not been studied before and the results are discussed.
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Authors who are presenting talks have a * after their name.
