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npboottprmFBar Release Party

December 13, 2023

The 'npboottprmFBar' package is now available on CRAN. The name, stands for the nonparametric bootstrap test with pooled resampling method (npbp) via the FBar distribution. The goal is to combine two methods:



In Dwivedi et al 2007, the npbp is impemented for the independent sample t-test, the paired t-test, and the F-test; on the other hand, the npboottprmFBar package adapts the npbp to work with the Fbar statistic (as implemented in 'restriktor') rather than the usual F statistic used in ANOVA.


Unlike most Mighty Metrika packages released on CRAN, npboottprmFBar does not (yet) have an associated 'shiny' application. This is because I want to get a better understanding of how the method functions. As such, the npboottprmFBar::persimon() function can be used to run simulations which fill out table shells similar to Dwivedi et al 2017 tables S2 and S3 (see snippets below) which focus on Type I error and statistical power. These simulations will be able to compare the performance of the npboottprmFBar method to the methods already present in the aforementioned tables S2 and S3 as well as to the restriktor::iht() default method and restriktor::iht() with boot = "parametric".

Snippets of Table S2 and Table S3 from Dwivedi et al 2017

In the npboottprmFBar "persimon" simulations (performance simulation on type I error or statistical power), all of the methods associated with informative hypothesis testing will be run under three different constraint scenarios:


  • 0 inequality constraints (grp 1 = grp2 = grp3)
  • 1 inequality constraints (grp1 < grp3)
  • 2 inequality constraints (grp1 < grp2 < grp3)


Another caveat is that for each model in the simulation, "the usual handling of the intercept" is employed. This means that for the methods which do not take constraints, the intercept is included in the model while for the methods which do take constraints, the intercept is not included in the model. While this distinction will make it harder to understand why a particular method is performing better or worse than a method which handles the intercept differently, the results may be more useful in that the results reflect how one would actually employ each of the models in practice.

Who Said It Best Episode 1: Daniel McNeish

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Mighty Metrika focuses on statistical methods and mathematics for the analysis of small sample size data. As such, the project runs the risk of people with small sample sizes using tools and methods from mightymetrika.com and becoming over confident in their results because they used "small sample size methods." The long term rigorous goal to combat this disservice is to host citizen science projects, include simulation function in R packages, and share simulation results from the literature and from mightymetrika.com tools through blogs. A short and quick way to combat misuse is through the Who Said It Best series. The series will share some of the best warnings from the small sample size statistical literature. In the Conclusion section of Daniel McNeish's paper Challenging Conventional Wisdom for Multivariate Statistical Models With Small Samples he shares a clear and wonderfully worded warning:

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BFfe: Test Update

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In 'mmibain' v0.2.0, the unit tests are passing at the moment, but on r-devel-linux-x86_64-debian-clang it really seems to be hit or miss. I believe that when the test fails it is do to the new BFfe function which is a case-by-case type implementation of ' bain ' for linear models; however, I used a unit test which relies on a synthetic data set where I generated random numbers and then just used the rep() function to group observations by participants. As such, the data generating process does fit the statistical model and sometimes the random data set that is generated does not make it through bain::bain() without error. I have already changed the unit test and corresponding Roxygen2 documentation example on the Mighty Metrika GitHub and this blog post will walk through the new data and model. But just for further context, here is the original code that sometimes runs through and sometimes throws and error.
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