The package terminaldigits implements simulated tests of
uniformity and independence for terminal digits. For certain parameters,
terminaldigits also implements Monte Carlo simulations for
type I errors and power for the test of independence. Simulations are
run in C++ utilizing Rcpp.
You can install the development version of
terminaldigits from GitHub with:
# install.packages("devtools")
devtools::install_github("josh-mc/terminaldigits")In many cases, terminal digits can be assumed to be uniformly distributed and independent of preceding digits. A violation of either of these assumptions may point to a data quality issue.
The following examples are based on a data set taken from the third
round of a decoy experiment involving hand-washing purportedly carried
out in a number of factories in China. For details, see
decoy and Yu, Nelson, and Simonsohn (2018).
The td_uniformity function tests the assumption of
uniformity using Pearson’s chi-squared statistic for
goodness-of-fit.
library(terminaldigits)
td_uniformity(decoy$weight, decimals = 2, reps = 1000)
#>
#> Pearson's chi-squared GOF test for uniformity of terminal digits
#>
#> data: decoy$weight
#> Chi-squared = 539.67, p-value = 0.000999The td_independence function tests the assumption of
independence. The default statistic is again Pearson’s chi-squared
statistic but the log-likelihood ratio statistic, the Freeman-Tukey
statistic, and the root-mean-square statistic are also available.
td_independence(decoy$weight, decimals = 2, reps = 1000)
#>
#> Chisq test for independence of terminal digits
#>
#> data: decoy$weight
#> Chisq = 6422.4, p-value = 0.000999The td_test function is a wrapper for the above two
functions. For more details, including a discussion of the
td_simulate function, see the package introduction
vignette.
Yu, F., Nelson, L., & Simonsohn, U. (2018, December 5). “In Press at Psychological Science: A New ‘Nudge’ Supported by Implausible Data.” DataColoda 74. http://datacolada.org/74