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Description

Informative Nonparametric Bootstrap Test with Pooled Resampling.

Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power.

npboottprmFBar

The goal of ‘npboottprmFBar’ is to implement the nonparametric bootstrap test with pooled resampling method (as presented in Dwivedi, Mallawaarachchi, and Alvarado (2017)) for informative hypothesis testing (as implemented in ‘restriktor’ and outlined in Vanbrabant and Rosseel (2020)).

Installation

You can install the released version of ‘npboottprmFBar’ from CRAN:

install.packages("npboottprmFBar")

To install the development version of ‘npboottprmFBar’ from GitHub, use the devtools package:

# install.packages("devtools")
devtools::install_github("mightymetrika/npboottprmFBar")

An iris example

The following example demonstrates how to use the bootFbar() function to conduct an informative hypothesis test.

library(npboottprmFBar)

res <- bootFbar(data = iris, formula = Sepal.Length ~ -1 + Species,
                grp = "Species",
                constraints = 'Speciessetosa < Speciesversicolor < Speciesvirginica',
                nboot = 10, conf.level = 0.95, seed = NULL, na_rm = FALSE)

paste0("Type B Test: ", res$pvalueB)
#> [1] "Type B Test: 1"
paste0("Type A Test: ", res$pvalueA)
#> [1] "Type A Test: 0"

The non-significant Type B test followed by the significant Type A test is evidence in favor the order-constrained hypothesis

References

Dwivedi AK, Mallawaarachchi I, Alvarado LA (2017). “Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.” Statistics in Medicine, 36 (14), 2187-2205.

Vanbrabant, L., & Rosseel, Y. (2020). An Introduction to Restriktor: Evaluating informative hypotheses for linear models. In R. van de Schoot & M. Miocevic (Eds.), Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners (1st ed., pp. 157 -172). Routledge. https://doi.org/10.4324/9780429273872-14

Metadata

Version

0.1.1

License

Unknown

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