| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| #------------------------------------------------------------------------------ |
| # R source file to validate KolmogorovSmirnov tests in |
| # org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest |
| # |
| # To run the test, install R, put this file and testFunctions |
| # into the same directory, launch R from this directory and then enter |
| # source("<name-of-this-file>") |
| # |
| # NOTE: the 2-sample bootstrap test requires the "Matching" library |
| ## https://cran.r-project.org/web/packages/Matching/index.html |
| ## See http://sekhon.berkeley.edu/matching for additional documentation. |
| ## Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching |
| ## Software with Automated Balance Optimization: The Matching package for R.'' |
| ## Journal of Statistical Software, 42(7): 1-52. |
| # |
| #------------------------------------------------------------------------------ |
| tol <- 1E-14 # error tolerance for tests |
| #------------------------------------------------------------------------------ |
| # Function definitions |
| |
| source("testFunctions") # utility test functions |
| require("Matching") # for ks.boot |
| |
| verifyOneSampleGaussian <- function(data, expectedP, expectedD, mean, sigma, exact, tol, desc) { |
| results <- ks.test(data, "pnorm", mean, sigma, exact = exact) |
| if (assertEquals(expectedP, results$p.value, tol, "p-value")) { |
| displayPadded(c(desc," p-value test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " p-value test"), FAILED, WIDTH) |
| } |
| if (assertEquals(expectedD, results$statistic, tol, "D statistic value")) { |
| displayPadded(c(desc," D statistic test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " D statistic test"), FAILED, WIDTH) |
| } |
| } |
| |
| verifyOneSampleUniform <- function(data, expectedP, expectedD, min, max, exact, tol, desc) { |
| results <- ks.test(data, "punif", min, max, exact = exact) |
| if (assertEquals(expectedP, results$p.value, tol, "p-value")) { |
| displayPadded(c(desc," p-value test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " p-value test"), FAILED, WIDTH) |
| } |
| if (assertEquals(expectedD, results$statistic, tol, "D statistic value")) { |
| displayPadded(c(desc," D statistic test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " D statistic test"), FAILED, WIDTH) |
| } |
| } |
| |
| verifyTwoSampleLargeSamples <- function(sample1, sample2, expectedP, expectedD, tol, desc) { |
| results <- ks.test(sample1, sample2) |
| if (assertEquals(expectedP, results$p.value, tol, "p-value")) { |
| displayPadded(c(desc," p-value test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " p-value test"), FAILED, WIDTH) |
| } |
| if (assertEquals(expectedD, results$statistic, tol, "D statistic value")) { |
| displayPadded(c(desc," D statistic test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " D statistic test"), FAILED, WIDTH) |
| } |
| } |
| |
| verifyTwoSampleSmallSamplesExact <- function(sample1, sample2, expectedP, expectedD, tol, desc) { |
| results <- ks.test(sample1, sample2, exact = TRUE) |
| if (assertEquals(expectedP, results$p.value, tol, "p-value")) { |
| displayPadded(c(desc," p-value test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " p-value test"), FAILED, WIDTH) |
| } |
| if (assertEquals(expectedD, results$statistic, tol, "D statistic value")) { |
| displayPadded(c(desc," D statistic test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " D statistic test"), FAILED, WIDTH) |
| } |
| } |
| |
| verifyTwoSampleBootstrap <- function(sample1, sample2, expectedP, tol, desc) { |
| results <- ks.boot(sample1, sample2,nboots=10000 ) |
| if (assertEquals(expectedP, results$ks.boot.pvalue, tol, "p-value")) { |
| displayPadded(c(desc, " p-value test"), SUCCEEDED, WIDTH) |
| } else { |
| displayPadded(c(desc, " p-value test"), FAILED, WIDTH) |
| } |
| } |
| |
| cat("KolmogorovSmirnovTest test cases\n") |
| |
| gaussian <- c(0.26055895, -0.63665233, 1.51221323, 0.61246988, -0.03013003, -1.73025682, |
| -0.51435805, 0.70494168, 0.18242945, 0.94734336, -0.04286604, -0.37931719, -1.07026403, -2.05861425, |
| 0.11201862, 0.71400136, -0.52122185, -0.02478725, -1.86811649, -1.79907688, 0.15046279, 1.32390193, |
| 1.55889719, 1.83149171, -0.03948003, -0.98579207, -0.76790540, 0.89080682, 0.19532153, 0.40692841, |
| 0.15047336, -0.58546562, -0.39865469, 0.77604271, -0.65188221, -1.80368554, 0.65273365, -0.75283102, |
| -1.91022150, -0.07640869, -1.08681188, -0.89270600, 2.09017508, 0.43907981, 0.10744033, -0.70961218, |
| 1.15707300, 0.44560525, -2.04593349, 0.53816843, -0.08366640, 0.24652218, 1.80549401, -0.99220707, |
| -1.14589408, -0.27170290, -0.49696855, 0.00968353, -1.87113545, -1.91116529, 0.97151891, -0.73576115, |
| -0.59437029, 0.72148436, 0.01747695, -0.62601157, -1.00971538, -1.42691397, 1.03250131, -0.30672627, |
| -0.15353992, -1.19976069, -0.68364218, 0.37525652, -0.46592881, -0.52116168, -0.17162202, 1.04679215, |
| 0.25165971, -0.04125231, -0.23756244, -0.93389975, 0.75551407, 0.08347445, -0.27482228, -0.4717632, |
| -0.1867746, -0.1166976, 0.5763333, 0.1307952, 0.7630584, -0.3616248, 2.1383790,-0.7946630, |
| 0.0231885, 0.7919195, 1.6057144, -0.3802508, 0.1229078, 1.5252901, -0.8543149, 0.3025040) |
| |
| shortGaussian <- gaussian[1:50] |
| |
| gaussian2 <- c(2.88041498038308, -0.632349445671017, 0.402121295225571, 0.692626364613243, 1.30693446815426, |
| -0.714176317131286, -0.233169206599583, 1.09113298322107, -1.53149079994305, 1.23259966205809, |
| 1.01389927412503, 0.0143898711497477, -0.512813545447559, 2.79364360835469, 0.662008875538092, |
| 1.04861546834788, -0.321280099931466, 0.250296656278743, 1.75820367603736, -2.31433523590905, |
| -0.462694696086403, 0.187725700950191, -2.24410950019152, 2.83473751105445, 0.252460174391016, |
| 1.39051945380281, -1.56270144203134, 0.998522814471644, -1.50147469080896, 0.145307533554146, |
| 0.469089457043406, -0.0914780723809334, -0.123446939266548, -0.610513388160565, -3.71548343891957, |
| -0.329577317349478, -0.312973794075871, 2.02051909758923, 2.85214308266271, 0.0193222002327237, |
| -0.0322422268266562, 0.514736012106768, 0.231484953375887, -2.22468798953629, 1.42197716075595, |
| 2.69988043856357, 0.0443757119128293, 0.721536984407798, -0.0445688839903234, -0.294372724550705, |
| 0.234041580912698, -0.868973119365727, 1.3524893453845, -0.931054600134503, -0.263514296006792, |
| 0.540949457402918, -0.882544288773685, -0.34148675747989, 1.56664494810034, 2.19850536566584, |
| -0.667972122928022, -0.70889669526203, -0.00251758193079668, 2.39527162977682, -2.7559594317269, |
| -0.547393502656671, -2.62144031572617, 2.81504147017922, -1.02036850201042, -1.00713927602786, |
| -0.520197775122254, 1.00625480138649, 2.46756916531313, 1.64364743727799, 0.704545210648595, |
| -0.425885789416992, -1.78387854908546, -0.286783886710481, 0.404183648369076, -0.369324280845769, |
| -0.0391185138840443, 2.41257787857293, 2.49744281317859, -0.826964496939021, -0.792555379958975, |
| 1.81097685787403, -0.475014580016638, 1.23387615291805, 0.646615294802053, 1.88496377454523, 1.20390698380814, |
| -0.27812153371728, 2.50149494533101, 0.406964323253817, -1.72253451309982, 1.98432494184332, 2.2223658560333, |
| 0.393086362404685, -0.504073151377089, -0.0484610869883821) |
| |
| uniform <- c(0.7930305, 0.6424382, 0.8747699, 0.7156518, 0.1845909, 0.2022326, |
| 0.4877206, 0.8928752, 0.2293062, 0.4222006, 0.1610459, 0.2830535, 0.9946345, 0.7329499, |
| 0.26411126, 0.87958133, 0.29827437, 0.39185988, 0.38351185, 0.36359611, 0.48646472, 0.05577866, |
| 0.56152250, 0.52672013, 0.13171783, 0.95864085, 0.03060207, 0.33514887, 0.72508148, 0.38901437, |
| 0.9978665, 0.5981300, 0.1065388, 0.7036991, 0.1071584, 0.4423963, 0.1107071, 0.6437221, |
| 0.58523872, 0.05044634, 0.65999539, 0.37367260, 0.73270024, 0.47473755, 0.74661163, 0.50765549, |
| 0.05377347, 0.40998009, 0.55235182, 0.21361998, 0.63117971, 0.18109222, 0.89153510, 0.23203248, |
| 0.6177106, 0.6856418, 0.2158557, 0.9870501, 0.2036914, 0.2100311, 0.9065020, 0.7459159, |
| 0.56631790, 0.06753629, 0.39684629, 0.52504615, 0.14199103, 0.78551120, 0.90503321, 0.80452362, |
| 0.9960115, 0.8172592, 0.5831134, 0.8794187, 0.2021501, 0.2923505, 0.9561824, 0.8792248, |
| 0.85201008, 0.02945562, 0.26200374, 0.11382818, 0.17238856, 0.36449473, 0.69688273, 0.96216330, |
| 0.4859432,0.4503438, 0.1917656, 0.8357845, 0.9957812, 0.4633570, 0.8654599, 0.4597996, |
| 0.68190289, 0.58887855, 0.09359396, 0.98081979, 0.73659533, 0.89344777, 0.18903099, 0.97660425) |
| |
| smallSample1 <- c(6, 7, 9, 13, 19, 21, 22, 23, 24) |
| smallSample2 <- c(10, 11, 12, 16, 20, 27, 28, 32, 44, 54) |
| smallSample3 <- c(6, 7, 9, 13, 19, 21, 22, 23, 24, 29, 30, 34, 36, 41, 45, 47, 51, 63, 33, 91) |
| smallSample4 <- c(10, 11, 12, 16, 20, 27, 28, 32, 44, 54, 56, 57, 64, 69, 71, 80, 81, 88, 90) |
| smallSample5 <- c(-10, -5, 17, 21, 22, 23, 24, 30, 44, 50, 56, 57, 59, 67, 73, 75, 77, 78, 79, 80, 81, 83, 84, 85, 88, 90, |
| 92, 93, 94, 95, 98, 100, 101, 103, 105, 110) |
| smallSample6 <- c(-2, -1, 0, 10, 14, 15, 16, 20, 25, 26, 27, 31, 32, 33, 34, 45, 47, 48, 51, 52, 53, 54, 60, 61, 62, 63, |
| 74, 82, 106, 107, 109, 11, 112, 113, 114) |
| bootSample1 <- c(0, 2, 4, 6, 8, 8, 10, 15, 22, 30, 33, 36, 38) |
| bootSample2 <- c(9, 17, 20, 33, 40, 51, 60, 60, 72, 90, 101) |
| roundingSample1 <- c(2,4,6,8,9,10,11,12,13) |
| roundingSample2 <- c(0,1,3,5,7) |
| |
| shortUniform <- uniform[1:20] |
| |
| verifyOneSampleGaussian(gaussian, 0.3172069207622391, 0.0932947561266756, 0, 1, |
| TRUE, tol, "One sample gaussian - gaussian values") |
| |
| verifyOneSampleGaussian(shortGaussian, 0.683736463728347, 0.09820779969463278, 0, 1, |
| TRUE, tol, "One sample gaussian - gaussian values - small sample") |
| |
| verifyOneSampleGaussian(uniform, 8.881784197001252E-16, 0.5117493931609258, 0, 1, |
| TRUE, tol, "One sample gaussian - uniform values") |
| |
| verifyOneSampleUniform(uniform, 8.881784197001252E-16, 0.5400666982352942, -0.5, 0.5, |
| TRUE, tol, "One sample uniform - uniform values") |
| |
| verifyOneSampleUniform(shortUniform, 4.117594598618268E-9, 0.6610459, -0.5, 0.5, |
| TRUE, tol, "One sample uniform - uniform values - small sample") |
| |
| verifyOneSampleUniform(gaussian, 4.9405812774239166E-11, 0.3401058049019608, -0.5, 0.5, |
| TRUE, tol, "One sample uniform - unit normal values") |
| |
| verifyTwoSampleLargeSamples(gaussian, gaussian2, 0.0319983962391632, 0.202352941176471, tol, |
| "Two sample N(0, 1) vs N(0, 1.6)") |
| |
| verifyTwoSampleSmallSamplesExact(smallSample1, smallSample2, 0.105577085453247, .5, tol, |
| "Two sample small samples exact 1") |
| |
| verifyTwoSampleSmallSamplesExact(smallSample3, smallSample4, 0.046298660942952, 0.426315789473684, tol, |
| "Two sample small samples exact 2") |
| |
| verifyTwoSampleSmallSamplesExact(smallSample5, smallSample6, 0.00300743602233366, 0.41031746031746, tol, |
| "Two sample small samples exact 3") |
| |
| verifyTwoSampleBootstrap(bootSample1, bootSample2, 0.0059, 1E-3, "Two sample bootstrap - isolated failures possible") |
| verifyTwoSampleBootstrap(gaussian, gaussian2, 0.0237, 1E-2, "Two sample bootstrap - isolated failures possible") |
| verifyTwoSampleBootstrap(roundingSample1, roundingSample2, 0.06303, 1E-2, "Two sample bootstrap - isolated failures possible") |
| |
| displayDashes(WIDTH) |
| |
| |
