Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function computes the test and key test quantities for the two one-sided test for equivalence, as documented in Schuirmann (1981) and Westlake (1981). This function computes the test from the statistics of a sample of paired differences of a normally-distributed population.

1 |

`mean` |
sample mean |

`std` |
sample standard deviation |

`n` |
sample size |

`null` |
the value of the parameter in the null hypothesis |

`alpha` |
test size |

`Epsilon` |
magnitude of region of similarity |

This test requires the assumption of normality of the population.

A list with the following components

`Dissimilarity ` |
the outcome of the test of the null hypothesis of dissimilarity |

`Mean ` |
the mean of the sample |

`StdDev ` |
the standard deviation of the sample |

`n ` |
the non-missing sample size |

`alpha ` |
the size of the test |

`Epsilon ` |
the magnitude of the region of similarity |

`Interval ` |
the half-length of the two one-sided interval |

This test requires the assumption of normality of the population. The components of the test are t-based confidence intervals, so the Central Limit Theorem and Slutsky's Theorem may be relevant to its application in large samples.

Andrew Robinson A.Robinson@ms.unimelb.edu.au

Schuirmann, D.L. 1981. On hypothesis testing to determine if the mean of a normal distribution is contained in a known interval. Biometrics 37 617.

Wellek, S. 2003. Testing statistical hypotheses of equivalence. Chapman and Hall/CRC. 284 pp.

Westlake, W.J. 1981. Response to T.B.L. Kirkwood: bioequivalence testing - a need to rethink. Biometrics 37, 589-594.

1 2 3 4 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.