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Journal of Statistical Theory and Practice

, Volume 11, Issue 3, pp 402–406 | Cite as

seqtest: An R package for sequential triangular tests

Article

Abstract

The R package seqtest provides functions to perform sample size determination and a sequential triangular test for the expectation in one and two samples, probabilities in one and two samples, and the product–moment correlation coefficient. The main characteristic of the sequential triangular test is that there is no fixed sample size given in advance. That is, for the most recent sampling point, one has to decide whether (1) sampling has to be continued, (2) the null hypothesis is accepted, or (3) the alternative hypothesis is accepted, given specified precision requirements (i.e., type-I risk, type-II risk, and an effect size). In general, sequential triangular tests have the advantage that in many cases the average sample size is smaller than that of the corresponding fixed sample size tests. The use of seqtest is illustrated by testing a correlation coefficient’s null hypothesis: 0 < ρρ0 using a sequential triangular test.

Keywords

Correlation coefficient R program sequential triangular test test of hypothesis 

AMS Subject Classification

62 

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References

  1. R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://doi.org/www.R-project.org/.Google Scholar
  2. Rasch, D., K. D. Kubinger, and T. Yanagida. 2011. Statistics in psychology—Using R and SPSS. Chichester, UK: Wiley.CrossRefGoogle Scholar
  3. Rasch, D., T. Yanagida, K. D. Kubinger, and B. Schneider. 2017. Determination of the optimal size of subsamples for testing a correlation coefficient by a sequential triangular test. In Statistics and simulation, ed. K. Moder, V. Melas, J. Pilz, and D. Rasch. Heidelberg, Germany: Springer.Google Scholar
  4. Schneider, B. 1992. An interactive computer program for design and monitoring of sequential clinical trials. Proceedings of the XVIth International Biometric Conference, 237–50, Hamilton, New Zealand.Google Scholar
  5. Schneider, B., D. Rasch, K. D. Kubinger, and T. Yanagida. 2015. A sequential triangular test of a correlation coefficient’s null-hypothesis: 0 < ρρ 0. Statistical Papers 56:689–600.MathSciNetCrossRefGoogle Scholar
  6. Wald, A. 1947. Sequential analysis. New York, NY: Wiley.MATHGoogle Scholar
  7. Whitehead, J. 1992. The design and analysis of sequential clinical trials, 2nd ed. Chichester, UK: Ellis Horwood.MATHGoogle Scholar
  8. Yanagida, T. 2016. seqtest: Sequential triangular test. R package version 0.1-0. https://doi.org/CRAN.R-project.org/package=seqtest

Copyright information

© Grace Scientific Publishing, 20 Middlefield Ct, Greensboro, NC 27455 2017

Authors and Affiliations

  1. 1.School of Medical Engineering and Applied Social SciencesUniversity of Applied Sciences Upper AustriaLinzAustria

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