| /* |
| * 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. |
| */ |
| package org.apache.commons.math4.legacy.stat.inference; |
| |
| import java.util.ArrayList; |
| import java.util.List; |
| |
| import org.apache.commons.statistics.distribution.NormalDistribution; |
| import org.apache.commons.math4.legacy.exception.DimensionMismatchException; |
| import org.apache.commons.math4.legacy.exception.NotPositiveException; |
| import org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException; |
| import org.apache.commons.math4.legacy.exception.NullArgumentException; |
| import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException; |
| import org.apache.commons.math4.legacy.exception.OutOfRangeException; |
| import org.apache.commons.math4.legacy.stat.descriptive.SummaryStatistics; |
| import org.apache.commons.math4.legacy.core.jdkmath.AccurateMath; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| |
| /** |
| * Test cases for the TestUtils class. |
| * |
| */ |
| public class InferenceTestUtilsTest { |
| @Test |
| public void testChiSquare() { |
| |
| // Target values computed using R version 1.8.1 |
| // Some assembly required ;-) |
| // Use sum((obs - exp)^2/exp) for the chi-square statistic and |
| // 1 - pchisq(sum((obs - exp)^2/exp), length(obs) - 1) for the p-value |
| |
| long[] observed = {10, 9, 11}; |
| double[] expected = {10, 10, 10}; |
| Assert.assertEquals("chi-square statistic", 0.2, InferenceTestUtils.chiSquare(expected, observed), 10E-12); |
| Assert.assertEquals("chi-square p-value", 0.904837418036, InferenceTestUtils.chiSquareTest(expected, observed), 1E-10); |
| |
| long[] observed1 = { 500, 623, 72, 70, 31 }; |
| double[] expected1 = { 485, 541, 82, 61, 37 }; |
| Assert.assertEquals( "chi-square test statistic", 9.023307936427388, InferenceTestUtils.chiSquare(expected1, observed1), 1E-10); |
| Assert.assertEquals("chi-square p-value", 0.06051952647453607, InferenceTestUtils.chiSquareTest(expected1, observed1), 1E-9); |
| Assert.assertTrue("chi-square test reject", InferenceTestUtils.chiSquareTest(expected1, observed1, 0.07)); |
| Assert.assertFalse("chi-square test accept", InferenceTestUtils.chiSquareTest(expected1, observed1, 0.05)); |
| |
| try { |
| InferenceTestUtils.chiSquareTest(expected1, observed1, 95); |
| Assert.fail("alpha out of range, OutOfRangeException expected"); |
| } catch (OutOfRangeException ex) { |
| // expected |
| } |
| |
| long[] tooShortObs = { 0 }; |
| double[] tooShortEx = { 1 }; |
| try { |
| InferenceTestUtils.chiSquare(tooShortEx, tooShortObs); |
| Assert.fail("arguments too short, DimensionMismatchException expected"); |
| } catch (DimensionMismatchException ex) { |
| // expected |
| } |
| |
| // unmatched arrays |
| long[] unMatchedObs = { 0, 1, 2, 3 }; |
| double[] unMatchedEx = { 1, 1, 2 }; |
| try { |
| InferenceTestUtils.chiSquare(unMatchedEx, unMatchedObs); |
| Assert.fail("arrays have different lengths, DimensionMismatchException expected"); |
| } catch (DimensionMismatchException ex) { |
| // expected |
| } |
| |
| // 0 expected count |
| expected[0] = 0; |
| try { |
| InferenceTestUtils.chiSquareTest(expected, observed, .01); |
| Assert.fail("bad expected count, NotStrictlyPositiveException expected"); |
| } catch (NotStrictlyPositiveException ex) { |
| // expected |
| } |
| |
| // negative observed count |
| expected[0] = 1; |
| observed[0] = -1; |
| try { |
| InferenceTestUtils.chiSquareTest(expected, observed, .01); |
| Assert.fail("bad expected count, NotPositiveException expected"); |
| } catch (NotPositiveException ex) { |
| // expected |
| } |
| |
| } |
| |
| @Test |
| public void testChiSquareIndependence() { |
| |
| // Target values computed using R version 1.8.1 |
| |
| long[][] counts = { {40, 22, 43}, {91, 21, 28}, {60, 10, 22}}; |
| Assert.assertEquals( "chi-square test statistic", 22.709027688, InferenceTestUtils.chiSquare(counts), 1E-9); |
| Assert.assertEquals("chi-square p-value", 0.000144751460134, InferenceTestUtils.chiSquareTest(counts), 1E-9); |
| Assert.assertTrue("chi-square test reject", InferenceTestUtils.chiSquareTest(counts, 0.0002)); |
| Assert.assertFalse("chi-square test accept", InferenceTestUtils.chiSquareTest(counts, 0.0001)); |
| |
| long[][] counts2 = {{10, 15}, {30, 40}, {60, 90} }; |
| Assert.assertEquals( "chi-square test statistic", 0.168965517241, InferenceTestUtils.chiSquare(counts2), 1E-9); |
| Assert.assertEquals("chi-square p-value",0.918987499852, InferenceTestUtils.chiSquareTest(counts2), 1E-9); |
| Assert.assertFalse("chi-square test accept", InferenceTestUtils.chiSquareTest(counts2, 0.1)); |
| |
| // ragged input array |
| long[][] counts3 = { {40, 22, 43}, {91, 21, 28}, {60, 10}}; |
| try { |
| InferenceTestUtils.chiSquare(counts3); |
| Assert.fail("Expecting DimensionMismatchException"); |
| } catch (DimensionMismatchException ex) { |
| // expected |
| } |
| |
| // insufficient data |
| long[][] counts4 = {{40, 22, 43}}; |
| try { |
| InferenceTestUtils.chiSquare(counts4); |
| Assert.fail("Expecting DimensionMismatchException"); |
| } catch (DimensionMismatchException ex) { |
| // expected |
| } |
| long[][] counts5 = {{40}, {40}, {30}, {10}}; |
| try { |
| InferenceTestUtils.chiSquare(counts5); |
| Assert.fail("Expecting DimensionMismatchException"); |
| } catch (DimensionMismatchException ex) { |
| // expected |
| } |
| |
| // negative counts |
| long[][] counts6 = {{10, -2}, {30, 40}, {60, 90} }; |
| try { |
| InferenceTestUtils.chiSquare(counts6); |
| Assert.fail("Expecting NotPositiveException"); |
| } catch (NotPositiveException ex) { |
| // expected |
| } |
| |
| // bad alpha |
| try { |
| InferenceTestUtils.chiSquareTest(counts, 0); |
| Assert.fail("Expecting OutOfRangeException"); |
| } catch (OutOfRangeException ex) { |
| // expected |
| } |
| } |
| |
| @Test |
| public void testChiSquareLargeTestStatistic() { |
| double[] exp = new double[] { |
| 3389119.5, 649136.6, 285745.4, 25357364.76, 11291189.78, 543628.0, |
| 232921.0, 437665.75 |
| }; |
| |
| long[] obs = new long[] { |
| 2372383, 584222, 257170, 17750155, 7903832, 489265, 209628, 393899 |
| }; |
| org.apache.commons.math4.legacy.stat.inference.ChiSquareTest csti = |
| new org.apache.commons.math4.legacy.stat.inference.ChiSquareTest(); |
| double cst = csti.chiSquareTest(exp, obs); |
| Assert.assertEquals("chi-square p-value", 0.0, cst, 1E-3); |
| Assert.assertEquals( "chi-square test statistic", |
| 114875.90421929007, InferenceTestUtils.chiSquare(exp, obs), 1E-9); |
| } |
| |
| /** Contingency table containing zeros - PR # 32531 */ |
| @Test |
| public void testChiSquareZeroCount() { |
| // Target values computed using R version 1.8.1 |
| long[][] counts = { {40, 0, 4}, {91, 1, 2}, {60, 2, 0}}; |
| Assert.assertEquals( "chi-square test statistic", 9.67444662263, |
| InferenceTestUtils.chiSquare(counts), 1E-9); |
| Assert.assertEquals("chi-square p-value", 0.0462835770603, |
| InferenceTestUtils.chiSquareTest(counts), 1E-9); |
| } |
| |
| private double[] tooShortObs = { 1.0 }; |
| private double[] emptyObs = {}; |
| private SummaryStatistics emptyStats = new SummaryStatistics(); |
| |
| @Test |
| public void testOneSampleT() { |
| double[] observed = |
| {93.0, 103.0, 95.0, 101.0, 91.0, 105.0, 96.0, 94.0, 101.0, 88.0, 98.0, 94.0, 101.0, 92.0, 95.0 }; |
| double mu = 100.0; |
| SummaryStatistics sampleStats = null; |
| sampleStats = new SummaryStatistics(); |
| for (int i = 0; i < observed.length; i++) { |
| sampleStats.addValue(observed[i]); |
| } |
| |
| // Target comparison values computed using R version 1.8.1 (Linux version) |
| Assert.assertEquals("t statistic", -2.81976445346, |
| InferenceTestUtils.t(mu, observed), 10E-10); |
| Assert.assertEquals("t statistic", -2.81976445346, |
| InferenceTestUtils.t(mu, sampleStats), 10E-10); |
| Assert.assertEquals("p value", 0.0136390585873, |
| InferenceTestUtils.tTest(mu, observed), 10E-10); |
| Assert.assertEquals("p value", 0.0136390585873, |
| InferenceTestUtils.tTest(mu, sampleStats), 10E-10); |
| |
| try { |
| InferenceTestUtils.t(mu, (double[]) null); |
| Assert.fail("arguments too short, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(mu, (SummaryStatistics) null); |
| Assert.fail("arguments too short, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(mu, emptyObs); |
| Assert.fail("arguments too short, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(mu, emptyStats); |
| Assert.fail("arguments too short, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(mu, tooShortObs); |
| Assert.fail("insufficient data to compute t statistic, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| try { |
| InferenceTestUtils.tTest(mu, tooShortObs); |
| Assert.fail("insufficient data to perform t test, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(mu, (SummaryStatistics) null); |
| Assert.fail("insufficient data to compute t statistic, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| try { |
| InferenceTestUtils.tTest(mu, (SummaryStatistics) null); |
| Assert.fail("insufficient data to perform t test, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| } |
| |
| @Test |
| public void testOneSampleTTest() { |
| double[] oneSidedP = |
| {2d, 0d, 6d, 6d, 3d, 3d, 2d, 3d, -6d, 6d, 6d, 6d, 3d, 0d, 1d, 1d, 0d, 2d, 3d, 3d }; |
| SummaryStatistics oneSidedPStats = new SummaryStatistics(); |
| for (int i = 0; i < oneSidedP.length; i++) { |
| oneSidedPStats.addValue(oneSidedP[i]); |
| } |
| // Target comparison values computed using R version 1.8.1 (Linux version) |
| Assert.assertEquals("one sample t stat", 3.86485535541, |
| InferenceTestUtils.t(0d, oneSidedP), 10E-10); |
| Assert.assertEquals("one sample t stat", 3.86485535541, |
| InferenceTestUtils.t(0d, oneSidedPStats),1E-10); |
| Assert.assertEquals("one sample p value", 0.000521637019637, |
| InferenceTestUtils.tTest(0d, oneSidedP) / 2d, 10E-10); |
| Assert.assertEquals("one sample p value", 0.000521637019637, |
| InferenceTestUtils.tTest(0d, oneSidedPStats) / 2d, 10E-5); |
| Assert.assertTrue("one sample t-test reject", InferenceTestUtils.tTest(0d, oneSidedP, 0.01)); |
| Assert.assertTrue("one sample t-test reject", InferenceTestUtils.tTest(0d, oneSidedPStats, 0.01)); |
| Assert.assertFalse("one sample t-test accept", InferenceTestUtils.tTest(0d, oneSidedP, 0.0001)); |
| Assert.assertFalse("one sample t-test accept", InferenceTestUtils.tTest(0d, oneSidedPStats, 0.0001)); |
| |
| try { |
| InferenceTestUtils.tTest(0d, oneSidedP, 95); |
| Assert.fail("alpha out of range, OutOfRangeException expected"); |
| } catch (OutOfRangeException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.tTest(0d, oneSidedPStats, 95); |
| Assert.fail("alpha out of range, OutOfRangeException expected"); |
| } catch (OutOfRangeException ex) { |
| // expected |
| } |
| |
| } |
| |
| @Test |
| public void testTwoSampleTHeterscedastic() { |
| double[] sample1 = { 7d, -4d, 18d, 17d, -3d, -5d, 1d, 10d, 11d, -2d }; |
| double[] sample2 = { -1d, 12d, -1d, -3d, 3d, -5d, 5d, 2d, -11d, -1d, -3d }; |
| SummaryStatistics sampleStats1 = new SummaryStatistics(); |
| for (int i = 0; i < sample1.length; i++) { |
| sampleStats1.addValue(sample1[i]); |
| } |
| SummaryStatistics sampleStats2 = new SummaryStatistics(); |
| for (int i = 0; i < sample2.length; i++) { |
| sampleStats2.addValue(sample2[i]); |
| } |
| |
| // Target comparison values computed using R version 1.8.1 (Linux version) |
| Assert.assertEquals("two sample heteroscedastic t stat", 1.60371728768, |
| InferenceTestUtils.t(sample1, sample2), 1E-10); |
| Assert.assertEquals("two sample heteroscedastic t stat", 1.60371728768, |
| InferenceTestUtils.t(sampleStats1, sampleStats2), 1E-10); |
| Assert.assertEquals("two sample heteroscedastic p value", 0.128839369622, |
| InferenceTestUtils.tTest(sample1, sample2), 1E-10); |
| Assert.assertEquals("two sample heteroscedastic p value", 0.128839369622, |
| InferenceTestUtils.tTest(sampleStats1, sampleStats2), 1E-10); |
| Assert.assertTrue("two sample heteroscedastic t-test reject", |
| InferenceTestUtils.tTest(sample1, sample2, 0.2)); |
| Assert.assertTrue("two sample heteroscedastic t-test reject", |
| InferenceTestUtils.tTest(sampleStats1, sampleStats2, 0.2)); |
| Assert.assertFalse("two sample heteroscedastic t-test accept", InferenceTestUtils.tTest(sample1, sample2, 0.1)); |
| Assert.assertFalse("two sample heteroscedastic t-test accept", InferenceTestUtils.tTest(sampleStats1, sampleStats2, 0.1)); |
| |
| try { |
| InferenceTestUtils.tTest(sample1, sample2, .95); |
| Assert.fail("alpha out of range, OutOfRangeException expected"); |
| } catch (OutOfRangeException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.tTest(sampleStats1, sampleStats2, .95); |
| Assert.fail("alpha out of range, OutOfRangeException expected"); |
| } catch (OutOfRangeException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.tTest(sample1, tooShortObs, .01); |
| Assert.fail("insufficient data, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.tTest(sampleStats1, (SummaryStatistics) null, .01); |
| Assert.fail("insufficient data, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.tTest(sample1, tooShortObs); |
| Assert.fail("insufficient data, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.tTest(sampleStats1, (SummaryStatistics) null); |
| Assert.fail("insufficient data, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(sample1, tooShortObs); |
| Assert.fail("insufficient data, NumberIsTooSmallException expected"); |
| } catch (NumberIsTooSmallException ex) { |
| // expected |
| } |
| |
| try { |
| InferenceTestUtils.t(sampleStats1, (SummaryStatistics) null); |
| Assert.fail("insufficient data, NullArgumentException expected"); |
| } catch (NullArgumentException ex) { |
| // expected |
| } |
| } |
| @Test |
| public void testTwoSampleTHomoscedastic() { |
| double[] sample1 ={2, 4, 6, 8, 10, 97}; |
| double[] sample2 = {4, 6, 8, 10, 16}; |
| SummaryStatistics sampleStats1 = new SummaryStatistics(); |
| for (int i = 0; i < sample1.length; i++) { |
| sampleStats1.addValue(sample1[i]); |
| } |
| SummaryStatistics sampleStats2 = new SummaryStatistics(); |
| for (int i = 0; i < sample2.length; i++) { |
| sampleStats2.addValue(sample2[i]); |
| } |
| |
| // Target comparison values computed using R version 1.8.1 (Linux version) |
| Assert.assertEquals("two sample homoscedastic t stat", 0.73096310086, |
| InferenceTestUtils.homoscedasticT(sample1, sample2), 10E-11); |
| Assert.assertEquals("two sample homoscedastic p value", 0.4833963785, |
| InferenceTestUtils.homoscedasticTTest(sampleStats1, sampleStats2), 1E-10); |
| Assert.assertTrue("two sample homoscedastic t-test reject", |
| InferenceTestUtils.homoscedasticTTest(sample1, sample2, 0.49)); |
| Assert.assertFalse("two sample homoscedastic t-test accept", InferenceTestUtils.homoscedasticTTest(sample1, sample2, 0.48)); |
| } |
| |
| @Test |
| public void testSmallSamples() { |
| double[] sample1 = {1d, 3d}; |
| double[] sample2 = {4d, 5d}; |
| |
| // Target values computed using R, version 1.8.1 (linux version) |
| Assert.assertEquals(-2.2360679775, InferenceTestUtils.t(sample1, sample2), |
| 1E-10); |
| Assert.assertEquals(0.198727388935, InferenceTestUtils.tTest(sample1, sample2), |
| 1E-10); |
| } |
| |
| @Test |
| public void testPaired() { |
| double[] sample1 = {1d, 3d, 5d, 7d}; |
| double[] sample2 = {0d, 6d, 11d, 2d}; |
| double[] sample3 = {5d, 7d, 8d, 10d}; |
| |
| // Target values computed using R, version 1.8.1 (linux version) |
| Assert.assertEquals(-0.3133, InferenceTestUtils.pairedT(sample1, sample2), 1E-4); |
| Assert.assertEquals(0.774544295819, InferenceTestUtils.pairedTTest(sample1, sample2), 1E-10); |
| Assert.assertEquals(0.001208, InferenceTestUtils.pairedTTest(sample1, sample3), 1E-6); |
| Assert.assertFalse(InferenceTestUtils.pairedTTest(sample1, sample3, .001)); |
| Assert.assertTrue(InferenceTestUtils.pairedTTest(sample1, sample3, .002)); |
| } |
| |
| private double[] classA = |
| {93.0, 103.0, 95.0, 101.0}; |
| private double[] classB = |
| {99.0, 92.0, 102.0, 100.0, 102.0}; |
| private double[] classC = |
| {110.0, 115.0, 111.0, 117.0, 128.0}; |
| |
| private List<double[]> classes = new ArrayList<>(); |
| private OneWayAnova oneWayAnova = new OneWayAnova(); |
| |
| @Test |
| public void testOneWayAnovaUtils() { |
| classes.add(classA); |
| classes.add(classB); |
| classes.add(classC); |
| Assert.assertEquals(oneWayAnova.anovaFValue(classes), |
| InferenceTestUtils.oneWayAnovaFValue(classes), 10E-12); |
| Assert.assertEquals(oneWayAnova.anovaPValue(classes), |
| InferenceTestUtils.oneWayAnovaPValue(classes), 10E-12); |
| Assert.assertEquals(oneWayAnova.anovaTest(classes, 0.01), |
| InferenceTestUtils.oneWayAnovaTest(classes, 0.01)); |
| } |
| @Test |
| public void testGTestGoodnesOfFit() throws Exception { |
| double[] exp = new double[]{ |
| 0.54d, 0.40d, 0.05d, 0.01d |
| }; |
| |
| long[] obs = new long[]{ |
| 70, 79, 3, 4 |
| }; |
| Assert.assertEquals("G test statistic", |
| 13.144799, InferenceTestUtils.g(exp, obs), 1E-5); |
| double p_gtgf = InferenceTestUtils.gTest(exp, obs); |
| Assert.assertEquals("g-Test p-value", 0.004333, p_gtgf, 1E-5); |
| |
| Assert.assertTrue(InferenceTestUtils.gTest(exp, obs, 0.05)); |
| } |
| |
| @Test |
| public void testGTestIndependence() throws Exception { |
| long[] obs1 = new long[]{ |
| 268, 199, 42 |
| }; |
| |
| long[] obs2 = new long[]{ |
| 807, 759, 184 |
| }; |
| |
| double g = InferenceTestUtils.gDataSetsComparison(obs1, obs2); |
| |
| Assert.assertEquals("G test statistic", |
| 7.3008170, g, 1E-4); |
| double p_gti = InferenceTestUtils.gTestDataSetsComparison(obs1, obs2); |
| |
| Assert.assertEquals("g-Test p-value", 0.0259805, p_gti, 1E-4); |
| Assert.assertTrue(InferenceTestUtils.gTestDataSetsComparison(obs1, obs2, 0.05)); |
| } |
| |
| @Test |
| public void testRootLogLikelihood() { |
| // positive where k11 is bigger than expected. |
| Assert.assertTrue(InferenceTestUtils.rootLogLikelihoodRatio(904, 21060, 1144, 283012) > 0.0); |
| |
| // negative because k11 is lower than expected |
| Assert.assertTrue(InferenceTestUtils.rootLogLikelihoodRatio(36, 21928, 60280, 623876) < 0.0); |
| |
| Assert.assertEquals(AccurateMath.sqrt(2.772589), InferenceTestUtils.rootLogLikelihoodRatio(1, 0, 0, 1), 0.000001); |
| Assert.assertEquals(-AccurateMath.sqrt(2.772589), InferenceTestUtils.rootLogLikelihoodRatio(0, 1, 1, 0), 0.000001); |
| Assert.assertEquals(AccurateMath.sqrt(27.72589), InferenceTestUtils.rootLogLikelihoodRatio(10, 0, 0, 10), 0.00001); |
| |
| Assert.assertEquals(AccurateMath.sqrt(39.33052), InferenceTestUtils.rootLogLikelihoodRatio(5, 1995, 0, 100000), 0.00001); |
| Assert.assertEquals(-AccurateMath.sqrt(39.33052), InferenceTestUtils.rootLogLikelihoodRatio(0, 100000, 5, 1995), 0.00001); |
| |
| Assert.assertEquals(AccurateMath.sqrt(4730.737), InferenceTestUtils.rootLogLikelihoodRatio(1000, 1995, 1000, 100000), 0.001); |
| Assert.assertEquals(-AccurateMath.sqrt(4730.737), InferenceTestUtils.rootLogLikelihoodRatio(1000, 100000, 1000, 1995), 0.001); |
| |
| Assert.assertEquals(AccurateMath.sqrt(5734.343), InferenceTestUtils.rootLogLikelihoodRatio(1000, 1000, 1000, 100000), 0.001); |
| Assert.assertEquals(AccurateMath.sqrt(5714.932), InferenceTestUtils.rootLogLikelihoodRatio(1000, 1000, 1000, 99000), 0.001); |
| } |
| |
| @Test |
| public void testKSOneSample() throws Exception { |
| final NormalDistribution unitNormal = new NormalDistribution(0d, 1d); |
| final double[] sample = KolmogorovSmirnovTestTest.gaussian; |
| final double tol = 1e-10; |
| Assert.assertEquals(0.3172069207622391, InferenceTestUtils.kolmogorovSmirnovTest(unitNormal, sample), tol); |
| Assert.assertEquals(0.0932947561266756, InferenceTestUtils.kolmogorovSmirnovStatistic(unitNormal, sample), tol); |
| } |
| |
| @Test |
| public void testKSTwoSample() throws Exception { |
| final double tol = 1e-10; |
| final double[] smallSample1 = { |
| 6, 7, 9, 13, 19, 21, 22, 23, 24 |
| }; |
| final double[] smallSample2 = { |
| 10, 11, 12, 16, 20, 27, 28, 32, 44, 54 |
| }; |
| Assert.assertEquals(0.105577085453247, InferenceTestUtils.kolmogorovSmirnovTest(smallSample1, smallSample2, false), tol); |
| final double d = InferenceTestUtils.kolmogorovSmirnovStatistic(smallSample1, smallSample2); |
| Assert.assertEquals(0.5, d, tol); |
| Assert.assertEquals(0.105577085453247, InferenceTestUtils.exactP(d, smallSample1.length, smallSample2.length, false), tol); |
| } |
| } |