Overview
This package implements some of the non-parametric tests in chapters 1-5 of Higgins (2003).
It depends on R6 for object oriented design and Rcpp for integration of R and C++.
A few examples in the book can be found here.
Installation
# install.packages("pak")
pak::pkg_install("qddyy/LearnNonparam")Usage
library(LearnNonparam)
options(LearnNonparam.pmt_progress = TRUE)-
Construct a test object
- from some R6 class directly
t <- Wilcoxon$new(n_permu = 1e6)- using the
pmt(permutation test) function (recommended)
t <- pmt("twosample.wilcoxon", n_permu = 1e6) -
Provide it with samples
-
Check the results
t$statistic
t$p_value
t$print()
t$plot(style = "ggplot2", binwidth = 1)
-
Modify some active bindings and see how the results change
t$type <- "asymp" t$p_value
See pmts() for tests implemented in this package.
| key | class | test |
|---|---|---|
| onesample.quantile | Quantile | Quantile Test |
| onesample.cdf | CDF | Inference on Cumulative Distribution Function |
| twosample.difference | Difference | Two-Sample Test Based on Mean or Median |
| twosample.wilcoxon | Wilcoxon | Two-Sample Wilcoxon Test |
| twosample.scoresum | ScoreSum | Two-Sample Test Based on Sum of Scores |
| twosample.ansari | AnsariBradley | Ansari-Bradley Test |
| twosample.siegel | SiegelTukey | Siegel-Tukey Test |
| twosample.rmd | RatioMeanDeviance | Ratio Mean Deviance Test |
| twosample.ks | KolmogorovSmirnov | Two-Sample Kolmogorov-Smirnov Test |
| ksample.f | KSampleF | K-Sample Test Based on F Statistic |
| ksample.kw | KruskalWallis | Kruskal-Wallis Test |
| ksample.jt | JonckheereTerpstra | Jonckheere-Terpstra Test |
| multcomp.studentized | Studentized | Multiple Comparison Based on Studentized Statistic |
| paired.sign | Sign | Two-Sample Sign Test |
| paired.difference | PairedDifference | Paired Comparison Based on Differences |
| rcbd.f | RCBDF | Test for RCBD Based on F Statistic |
| rcbd.friedman | Friedman | Friedman Test |
| rcbd.page | Page | Page Test |
| association.corr | Correlation | Test for Association Between Paired Samples |
| table.chisq | ChiSquare | Chi-Square Test on Contingency Table |
The define_pmt function allows users to define new permutation tests. Take Cramér-von Mises test as an example:
t <- define_pmt(
# this is a two-sample permutation test
inherit = "twosample",
statistic = function(x, y) {
# pre-calculate certain constants that remain invariant during permutation
n_x <- length(x)
n_y <- length(y)
F_x <- seq_len(n_x) / n_x
G_y <- seq_len(n_y) / n_y
# return another function to calculate the test statistic
function(x, y) {
x <- sort(x)
y <- sort(y)
F <- approxfun(x, F_x, "constant", 0, 1, ties = "ordered")
G <- approxfun(y, G_y, "constant", 0, 1, ties = "ordered")
sum(c(F_x - G(x), G_y - F(y))^2)
}
},
# reject the null hypothesis when the test statistic is large
rejection = "r",
scoring = "none", n_permu = 1e4,
name = "Cramér-von Mises Test",
alternative = "samples are from different distributions"
)
t$test(rnorm(20, 1), rnorm(20, 0))$print()