Format

Send to

Choose Destination
J Stat Softw. 2014;59(13):1-21. Epub 2014 Sep 12.

structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data.

Author information

1
Department of Statistics, Stanford University, P.O. Box 14869, Stanford CA, 94309, United States of America, krissankaran@stanford.edu URL: http://www.stanford.edu/~kriss1.
2
Department of Statistics, Stanford University, Sequoia Hall, Stanford, CA 94305, United States of America.

Abstract

The 𝖱 package structSSI provides an accessible implementation of two recently developed simultaneous and selective inference techniques: the group Benjamini-Hochberg and hierarchical false discovery rate procedures. Unlike many multiple testing schemes, these methods specifically incorporate existing information about the grouped or hierarchical dependence between hypotheses under consideration while controlling the false discovery rate. Doing so increases statistical power and interpretability. Furthermore, these procedures provide novel approaches to the central problem of encoding complex dependency between hypotheses. We briefly describe the group Benjamini-Hochberg and hierarchical false discovery rate procedures and then illustrate them using two examples, one a measure of ecological microbial abundances and the other a global temperature time series. For both procedures, we detail the steps associated with the analysis of these particular data sets, including establishing the dependence structures, performing the test, and interpreting the results. These steps are encapsulated by 𝖱 functions, and we explain their applicability to general data sets.

KEYWORDS:

false discovery rate; hierarchical data; multiple testing; selective inference; simultaneous inference

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center