README

Overview

This R package implements several non-parametric tests in chapters 1-5 of Higgins (2004), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with Rcpp for efficiency and R6 for flexible, object-oriented design, it provides a unified framework for performing or creating custom permutation tests.

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) wrapper

# recommended for a unified API
t <- pmt("twosample.wilcoxon", n_permu = 1e6)

Provide it with samples

set.seed(-1)

t$test(rnorm(10, 1), rnorm(10, 0))

Check the results

t$statistic

t$p_value

options(digits = 3)

t$print()

ggplot2::theme_set(ggplot2::theme_minimal())

t$plot(style = "ggplot2", binwidth = 1)

Modify some settings and observe the 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.oneway	OneWay	One-Way Test for Equal Means
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.oneway	RCBDOneWay	One-Way Test for Equal Means in RCBD
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

define_pmt allows users to define new permutation tests. Take the two-sample Cramér-Von Mises test as an example:

t <- define_pmt(
    # this is a two-sample permutation test
    inherit = "twosample",
    statistic = function(x, y) {
        # (optional) 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 a closure to calculate the test statistic
        function(x, y) {
            x <- sort.int(x)
            y <- sort.int(y)
            F <- approxfun(x, F_x, "constant", 0, 1)
            G <- approxfun(y, G_y, "constant", 0, 1)
            sum(c(F_x - G(x), G_y - F(y))^2)
        }
    },
    # reject the null hypothesis when the test statistic is large
    rejection = "r",
    name = "Two-Sample Cramér-Von Mises Test",
    alternative = "samples are from different continuous distributions"
)

t$test(rnorm(10), runif(10))$print()

LearnNonparam

Overview

Installation

Usage

References