Implements simulated tests for the hypothesis that terminal digits are uniformly distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits are independent from preceding digits (several tests of independence for r x c contingency tables). Also, for a number of distributions, implements Monte Carlo simulations for type I errors and power for the test of independence.
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 = 0.000999
The 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 = 0.000999
The 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