Description
Alternative Bootstrap-Based t-Test Aiming to Reduce Type-I Error for Non-Negative, Zero-Inflated D….
Description
Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.
README.md
rbtt (Robust bootstrapped t-test)
In datasets whose data-generating distributions are non-negative with excess zero observations, it can be difficult to find general-purpose statistical tests for comparing sample means while controlling type-I error rates. This R package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these particular datasets.
To download and use this package, run the following:
install.packages("devtools") # Unless you already have it
library(devtools)
devtools::install_github("WannabeSmith/rbtt")
To obtain details on how to use the package, run:
help(rbtt)