| /* |
| * 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.regression; |
| |
| import java.util.Random; |
| |
| import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException; |
| import org.apache.commons.math4.legacy.exception.OutOfRangeException; |
| import org.apache.commons.rng.UniformRandomProvider; |
| import org.apache.commons.rng.simple.RandomSource; |
| import org.apache.commons.math4.core.jdkmath.JdkMath; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| |
| /** |
| * Test cases for the TestStatistic class. |
| * |
| */ |
| |
| public final class SimpleRegressionTest { |
| |
| /* |
| * NIST "Norris" reference data set from |
| * http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Norris.dat |
| * Strangely, order is {y,x} |
| */ |
| private double[][] data = { { 0.1, 0.2 }, {338.8, 337.4 }, {118.1, 118.2 }, |
| {888.0, 884.6 }, {9.2, 10.1 }, {228.1, 226.5 }, {668.5, 666.3 }, {998.5, 996.3 }, |
| {449.1, 448.6 }, {778.9, 777.0 }, {559.2, 558.2 }, {0.3, 0.4 }, {0.1, 0.6 }, {778.1, 775.5 }, |
| {668.8, 666.9 }, {339.3, 338.0 }, {448.9, 447.5 }, {10.8, 11.6 }, {557.7, 556.0 }, |
| {228.3, 228.1 }, {998.0, 995.8 }, {888.8, 887.6 }, {119.6, 120.2 }, {0.3, 0.3 }, |
| {0.6, 0.3 }, {557.6, 556.8 }, {339.3, 339.1 }, {888.0, 887.2 }, {998.5, 999.0 }, |
| {778.9, 779.0 }, {10.2, 11.1 }, {117.6, 118.3 }, {228.9, 229.2 }, {668.4, 669.1 }, |
| {449.2, 448.9 }, {0.2, 0.5 } |
| }; |
| |
| /* |
| * Correlation example from |
| * http://www.xycoon.com/correlation.htm |
| */ |
| private double[][] corrData = { { 101.0, 99.2 }, {100.1, 99.0 }, {100.0, 100.0 }, |
| {90.6, 111.6 }, {86.5, 122.2 }, {89.7, 117.6 }, {90.6, 121.1 }, {82.8, 136.0 }, |
| {70.1, 154.2 }, {65.4, 153.6 }, {61.3, 158.5 }, {62.5, 140.6 }, {63.6, 136.2 }, |
| {52.6, 168.0 }, {59.7, 154.3 }, {59.5, 149.0 }, {61.3, 165.5 } |
| }; |
| |
| /* |
| * From Moore and Mcabe, "Introduction to the Practice of Statistics" |
| * Example 10.3 |
| */ |
| private double[][] infData = { { 15.6, 5.2 }, {26.8, 6.1 }, {37.8, 8.7 }, {36.4, 8.5 }, |
| {35.5, 8.8 }, {18.6, 4.9 }, {15.3, 4.5 }, {7.9, 2.5 }, {0.0, 1.1 } |
| }; |
| |
| /* |
| * Points to remove in the remove tests |
| */ |
| private double[][] removeSingle = {infData[1]}; |
| private double[][] removeMultiple = { infData[1], infData[2] }; |
| private double removeX = infData[0][0]; |
| private double removeY = infData[0][1]; |
| |
| |
| /* |
| * Data with bad linear fit |
| */ |
| private double[][] infData2 = { { 1, 1 }, {2, 0 }, {3, 5 }, {4, 2 }, |
| {5, -1 }, {6, 12 } |
| }; |
| |
| |
| /* |
| * Data from NIST NOINT1 |
| */ |
| private double[][] noint1 = { |
| {130.0,60.0}, |
| {131.0,61.0}, |
| {132.0,62.0}, |
| {133.0,63.0}, |
| {134.0,64.0}, |
| {135.0,65.0}, |
| {136.0,66.0}, |
| {137.0,67.0}, |
| {138.0,68.0}, |
| {139.0,69.0}, |
| {140.0,70.0} |
| }; |
| |
| /* |
| * Data from NIST NOINT2 |
| * |
| */ |
| private double[][] noint2 = { |
| {3.0,4}, |
| {4,5}, |
| {4,6} |
| }; |
| |
| |
| /** |
| * Test that the SimpleRegression objects generated from combining two |
| * SimpleRegression objects created from subsets of data are identical to |
| * SimpleRegression objects created from the combined data. |
| */ |
| @Test |
| public void testAppend() { |
| check(false); |
| check(true); |
| } |
| |
| /** |
| * Checks that adding data to a single model gives the same result |
| * as adding "parts" of the dataset to smaller models and using append |
| * to aggregate the smaller models. |
| * |
| * @param includeIntercept |
| */ |
| private void check(boolean includeIntercept) { |
| final int sets = 2; |
| final UniformRandomProvider rand = RandomSource.ISAAC.create(10L);// Seed can be changed |
| final SimpleRegression whole = new SimpleRegression(includeIntercept);// regression of the whole set |
| final SimpleRegression parts = new SimpleRegression(includeIntercept);// regression with parts. |
| |
| for (int s = 0; s < sets; s++) {// loop through each subset of data. |
| final double coef = rand.nextDouble(); |
| final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression |
| for (int i = 0; i < 5; i++) { // loop through individual samlpes. |
| final double x = rand.nextDouble(); |
| final double y = x * coef + rand.nextDouble();// some noise |
| sub.addData(x, y); |
| whole.addData(x, y); |
| } |
| parts.append(sub); |
| Assert.assertTrue(equals(parts, whole, 1E-6)); |
| } |
| } |
| |
| /** |
| * Returns true iff the statistics reported by model1 are all within tol of |
| * those reported by model2. |
| * |
| * @param model1 first model |
| * @param model2 second model |
| * @param tol tolerance |
| * @return true if the two models report the same regression stats |
| */ |
| private boolean equals(SimpleRegression model1, SimpleRegression model2, double tol) { |
| if (model1.getN() != model2.getN()) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getIntercept() - model2.getIntercept()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getInterceptStdErr() - model2.getInterceptStdErr()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getMeanSquareError() - model2.getMeanSquareError()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getR() - model2.getR()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getRegressionSumSquares() - model2.getRegressionSumSquares()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getRSquare() - model2.getRSquare()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getSignificance() - model2.getSignificance()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getSlope() - model2.getSlope()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getSlopeConfidenceInterval() - model2.getSlopeConfidenceInterval()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getSlopeStdErr() - model2.getSlopeStdErr()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getSumOfCrossProducts() - model2.getSumOfCrossProducts()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getSumSquaredErrors() - model2.getSumSquaredErrors()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getTotalSumSquares() - model2.getTotalSumSquares()) > tol) { |
| return false; |
| } |
| if (JdkMath.abs(model1.getXSumSquares() - model2.getXSumSquares()) > tol) { |
| return false; |
| } |
| return true; |
| } |
| |
| @Test |
| public void testRegressIfaceMethod(){ |
| final SimpleRegression regression = new SimpleRegression(true); |
| final UpdatingMultipleLinearRegression iface = regression; |
| final SimpleRegression regressionNoint = new SimpleRegression( false ); |
| final SimpleRegression regressionIntOnly= new SimpleRegression( false ); |
| for (int i = 0; i < data.length; i++) { |
| iface.addObservation( new double[]{data[i][1]}, data[i][0]); |
| regressionNoint.addData(data[i][1], data[i][0]); |
| regressionIntOnly.addData(1.0, data[i][0]); |
| } |
| |
| //should not be null |
| final RegressionResults fullReg = iface.regress( ); |
| Assert.assertNotNull(fullReg); |
| Assert.assertEquals("intercept", regression.getIntercept(), fullReg.getParameterEstimate(0), 1.0e-16); |
| Assert.assertEquals("intercept std err",regression.getInterceptStdErr(), fullReg.getStdErrorOfEstimate(0),1.0E-16); |
| Assert.assertEquals("slope", regression.getSlope(), fullReg.getParameterEstimate(1), 1.0e-16); |
| Assert.assertEquals("slope std err",regression.getSlopeStdErr(), fullReg.getStdErrorOfEstimate(1),1.0E-16); |
| Assert.assertEquals("number of observations",regression.getN(), fullReg.getN()); |
| Assert.assertEquals("r-square",regression.getRSquare(), fullReg.getRSquared(), 1.0E-16); |
| Assert.assertEquals("SSR", regression.getRegressionSumSquares(), fullReg.getRegressionSumSquares() ,1.0E-16); |
| Assert.assertEquals("MSE", regression.getMeanSquareError(), fullReg.getMeanSquareError() ,1.0E-16); |
| Assert.assertEquals("SSE", regression.getSumSquaredErrors(), fullReg.getErrorSumSquares() ,1.0E-16); |
| |
| |
| final RegressionResults noInt = iface.regress( new int[]{1} ); |
| Assert.assertNotNull(noInt); |
| Assert.assertEquals("slope", regressionNoint.getSlope(), noInt.getParameterEstimate(0), 1.0e-12); |
| Assert.assertEquals("slope std err",regressionNoint.getSlopeStdErr(), noInt.getStdErrorOfEstimate(0),1.0E-16); |
| Assert.assertEquals("number of observations",regressionNoint.getN(), noInt.getN()); |
| Assert.assertEquals("r-square",regressionNoint.getRSquare(), noInt.getRSquared(), 1.0E-16); |
| Assert.assertEquals("SSR", regressionNoint.getRegressionSumSquares(), noInt.getRegressionSumSquares() ,1.0E-8); |
| Assert.assertEquals("MSE", regressionNoint.getMeanSquareError(), noInt.getMeanSquareError() ,1.0E-16); |
| Assert.assertEquals("SSE", regressionNoint.getSumSquaredErrors(), noInt.getErrorSumSquares() ,1.0E-16); |
| |
| final RegressionResults onlyInt = iface.regress( new int[]{0} ); |
| Assert.assertNotNull(onlyInt); |
| Assert.assertEquals("slope", regressionIntOnly.getSlope(), onlyInt.getParameterEstimate(0), 1.0e-12); |
| Assert.assertEquals("slope std err",regressionIntOnly.getSlopeStdErr(), onlyInt.getStdErrorOfEstimate(0),1.0E-12); |
| Assert.assertEquals("number of observations",regressionIntOnly.getN(), onlyInt.getN()); |
| Assert.assertEquals("r-square",regressionIntOnly.getRSquare(), onlyInt.getRSquared(), 1.0E-14); |
| Assert.assertEquals("SSE", regressionIntOnly.getSumSquaredErrors(), onlyInt.getErrorSumSquares() ,1.0E-8); |
| Assert.assertEquals("SSR", regressionIntOnly.getRegressionSumSquares(), onlyInt.getRegressionSumSquares() ,1.0E-8); |
| Assert.assertEquals("MSE", regressionIntOnly.getMeanSquareError(), onlyInt.getMeanSquareError() ,1.0E-8); |
| |
| } |
| |
| /** |
| * Verify that regress generates exceptions as advertised for bad model specifications. |
| */ |
| @Test |
| public void testRegressExceptions() { |
| // No intercept |
| final SimpleRegression noIntRegression = new SimpleRegression(false); |
| noIntRegression.addData(noint2[0][1], noint2[0][0]); |
| noIntRegression.addData(noint2[1][1], noint2[1][0]); |
| noIntRegression.addData(noint2[2][1], noint2[2][0]); |
| try { // null array |
| noIntRegression.regress(null); |
| Assert.fail("Expecting MathIllegalArgumentException for null array"); |
| } catch (MathIllegalArgumentException ex) { |
| // Expected |
| } |
| try { // empty array |
| noIntRegression.regress(new int[] {}); |
| Assert.fail("Expecting MathIllegalArgumentException for empty array"); |
| } catch (MathIllegalArgumentException ex) { |
| // Expected |
| } |
| try { // more than 1 regressor |
| noIntRegression.regress(new int[] {0, 1}); |
| Assert.fail("Expecting ModelSpecificationException - too many regressors"); |
| } catch (ModelSpecificationException ex) { |
| // Expected |
| } |
| try { // invalid regressor |
| noIntRegression.regress(new int[] {1}); |
| Assert.fail("Expecting OutOfRangeException - invalid regression"); |
| } catch (OutOfRangeException ex) { |
| // Expected |
| } |
| |
| // With intercept |
| final SimpleRegression regression = new SimpleRegression(true); |
| regression.addData(noint2[0][1], noint2[0][0]); |
| regression.addData(noint2[1][1], noint2[1][0]); |
| regression.addData(noint2[2][1], noint2[2][0]); |
| try { // null array |
| regression.regress(null); |
| Assert.fail("Expecting MathIllegalArgumentException for null array"); |
| } catch (MathIllegalArgumentException ex) { |
| // Expected |
| } |
| try { // empty array |
| regression.regress(new int[] {}); |
| Assert.fail("Expecting MathIllegalArgumentException for empty array"); |
| } catch (MathIllegalArgumentException ex) { |
| // Expected |
| } |
| try { // more than 2 regressors |
| regression.regress(new int[] {0, 1, 2}); |
| Assert.fail("Expecting ModelSpecificationException - too many regressors"); |
| } catch (ModelSpecificationException ex) { |
| // Expected |
| } |
| try { // wrong order |
| regression.regress(new int[] {1,0}); |
| Assert.fail("Expecting ModelSpecificationException - invalid regression"); |
| } catch (ModelSpecificationException ex) { |
| // Expected |
| } |
| try { // out of range |
| regression.regress(new int[] {3,4}); |
| Assert.fail("Expecting OutOfRangeException"); |
| } catch (OutOfRangeException ex) { |
| // Expected |
| } |
| try { // out of range |
| regression.regress(new int[] {0,2}); |
| Assert.fail("Expecting OutOfRangeException"); |
| } catch (OutOfRangeException ex) { |
| // Expected |
| } |
| try { // out of range |
| regression.regress(new int[] {2}); |
| Assert.fail("Expecting OutOfRangeException"); |
| } catch (OutOfRangeException ex) { |
| // Expected |
| } |
| } |
| |
| @Test |
| public void testNoInterceot_noint2(){ |
| SimpleRegression regression = new SimpleRegression(false); |
| regression.addData(noint2[0][1], noint2[0][0]); |
| regression.addData(noint2[1][1], noint2[1][0]); |
| regression.addData(noint2[2][1], noint2[2][0]); |
| Assert.assertEquals("intercept", 0, regression.getIntercept(), 0); |
| Assert.assertEquals("slope", 0.727272727272727, |
| regression.getSlope(), 10E-12); |
| Assert.assertEquals("slope std err", 0.420827318078432E-01, |
| regression.getSlopeStdErr(),10E-12); |
| Assert.assertEquals("number of observations", 3, regression.getN()); |
| Assert.assertEquals("r-square", 0.993348115299335, |
| regression.getRSquare(), 10E-12); |
| Assert.assertEquals("SSR", 40.7272727272727, |
| regression.getRegressionSumSquares(), 10E-9); |
| Assert.assertEquals("MSE", 0.136363636363636, |
| regression.getMeanSquareError(), 10E-10); |
| Assert.assertEquals("SSE", 0.272727272727273, |
| regression.getSumSquaredErrors(),10E-9); |
| } |
| |
| @Test |
| public void testNoIntercept_noint1(){ |
| SimpleRegression regression = new SimpleRegression(false); |
| for (int i = 0; i < noint1.length; i++) { |
| regression.addData(noint1[i][1], noint1[i][0]); |
| } |
| Assert.assertEquals("intercept", 0, regression.getIntercept(), 0); |
| Assert.assertEquals("slope", 2.07438016528926, regression.getSlope(), 10E-12); |
| Assert.assertEquals("slope std err", 0.165289256198347E-01, |
| regression.getSlopeStdErr(),10E-12); |
| Assert.assertEquals("number of observations", 11, regression.getN()); |
| Assert.assertEquals("r-square", 0.999365492298663, |
| regression.getRSquare(), 10E-12); |
| Assert.assertEquals("SSR", 200457.727272727, |
| regression.getRegressionSumSquares(), 10E-9); |
| Assert.assertEquals("MSE", 12.7272727272727, |
| regression.getMeanSquareError(), 10E-10); |
| Assert.assertEquals("SSE", 127.272727272727, |
| regression.getSumSquaredErrors(),10E-9); |
| |
| } |
| |
| @Test |
| public void testNorris() { |
| SimpleRegression regression = new SimpleRegression(); |
| for (int i = 0; i < data.length; i++) { |
| regression.addData(data[i][1], data[i][0]); |
| } |
| // Tests against certified values from |
| // http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Norris.dat |
| Assert.assertEquals("slope", 1.00211681802045, regression.getSlope(), 10E-12); |
| Assert.assertEquals("slope std err", 0.429796848199937E-03, |
| regression.getSlopeStdErr(),10E-12); |
| Assert.assertEquals("number of observations", 36, regression.getN()); |
| Assert.assertEquals( "intercept", -0.262323073774029, |
| regression.getIntercept(),10E-12); |
| Assert.assertEquals("std err intercept", 0.232818234301152, |
| regression.getInterceptStdErr(),10E-12); |
| Assert.assertEquals("r-square", 0.999993745883712, |
| regression.getRSquare(), 10E-12); |
| Assert.assertEquals("SSR", 4255954.13232369, |
| regression.getRegressionSumSquares(), 10E-9); |
| Assert.assertEquals("MSE", 0.782864662630069, |
| regression.getMeanSquareError(), 10E-10); |
| Assert.assertEquals("SSE", 26.6173985294224, |
| regression.getSumSquaredErrors(),10E-9); |
| // ------------ End certified data tests |
| |
| Assert.assertEquals( "predict(0)", -0.262323073774029, |
| regression.predict(0), 10E-12); |
| Assert.assertEquals("predict(1)", 1.00211681802045 - 0.262323073774029, |
| regression.predict(1), 10E-12); |
| } |
| |
| @Test |
| public void testCorr() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(corrData); |
| Assert.assertEquals("number of observations", 17, regression.getN()); |
| Assert.assertEquals("r-square", .896123, regression.getRSquare(), 10E-6); |
| Assert.assertEquals("r", -0.94663767742, regression.getR(), 1E-10); |
| } |
| |
| @Test |
| public void testNaNs() { |
| SimpleRegression regression = new SimpleRegression(); |
| Assert.assertTrue("intercept not NaN", Double.isNaN(regression.getIntercept())); |
| Assert.assertTrue("slope not NaN", Double.isNaN(regression.getSlope())); |
| Assert.assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr())); |
| Assert.assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr())); |
| Assert.assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError())); |
| Assert.assertTrue("e not NaN", Double.isNaN(regression.getR())); |
| Assert.assertTrue("r-square not NaN", Double.isNaN(regression.getRSquare())); |
| Assert.assertTrue( "RSS not NaN", Double.isNaN(regression.getRegressionSumSquares())); |
| Assert.assertTrue("SSE not NaN",Double.isNaN(regression.getSumSquaredErrors())); |
| Assert.assertTrue("SSTO not NaN", Double.isNaN(regression.getTotalSumSquares())); |
| Assert.assertTrue("predict not NaN", Double.isNaN(regression.predict(0))); |
| |
| regression.addData(1, 2); |
| regression.addData(1, 3); |
| |
| // No x variation, so these should still blow... |
| Assert.assertTrue("intercept not NaN", Double.isNaN(regression.getIntercept())); |
| Assert.assertTrue("slope not NaN", Double.isNaN(regression.getSlope())); |
| Assert.assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr())); |
| Assert.assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr())); |
| Assert.assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError())); |
| Assert.assertTrue("e not NaN", Double.isNaN(regression.getR())); |
| Assert.assertTrue("r-square not NaN", Double.isNaN(regression.getRSquare())); |
| Assert.assertTrue("RSS not NaN", Double.isNaN(regression.getRegressionSumSquares())); |
| Assert.assertTrue("SSE not NaN", Double.isNaN(regression.getSumSquaredErrors())); |
| Assert.assertTrue("predict not NaN", Double.isNaN(regression.predict(0))); |
| |
| // but SSTO should be OK |
| Assert.assertFalse("SSTO NaN", Double.isNaN(regression.getTotalSumSquares())); |
| |
| regression = new SimpleRegression(); |
| |
| regression.addData(1, 2); |
| regression.addData(3, 3); |
| |
| // All should be OK except MSE, s(b0), s(b1) which need one more df |
| Assert.assertFalse("interceptNaN", Double.isNaN(regression.getIntercept())); |
| Assert.assertFalse("slope NaN", Double.isNaN(regression.getSlope())); |
| Assert.assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr())); |
| Assert.assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr())); |
| Assert.assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError())); |
| Assert.assertFalse("r NaN", Double.isNaN(regression.getR())); |
| Assert.assertFalse("r-square NaN", Double.isNaN(regression.getRSquare())); |
| Assert.assertFalse("RSS NaN", Double.isNaN(regression.getRegressionSumSquares())); |
| Assert.assertFalse("SSE NaN", Double.isNaN(regression.getSumSquaredErrors())); |
| Assert.assertFalse("SSTO NaN", Double.isNaN(regression.getTotalSumSquares())); |
| Assert.assertFalse("predict NaN", Double.isNaN(regression.predict(0))); |
| |
| regression.addData(1, 4); |
| |
| // MSE, MSE, s(b0), s(b1) should all be OK now |
| Assert.assertFalse("MSE NaN", Double.isNaN(regression.getMeanSquareError())); |
| Assert.assertFalse("slope std err NaN", Double.isNaN(regression.getSlopeStdErr())); |
| Assert.assertFalse("intercept std err NaN", Double.isNaN(regression.getInterceptStdErr())); |
| } |
| |
| @Test |
| public void testClear() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(corrData); |
| Assert.assertEquals("number of observations", 17, regression.getN()); |
| regression.clear(); |
| Assert.assertEquals("number of observations", 0, regression.getN()); |
| regression.addData(corrData); |
| Assert.assertEquals("r-square", .896123, regression.getRSquare(), 10E-6); |
| regression.addData(data); |
| Assert.assertEquals("number of observations", 53, regression.getN()); |
| } |
| |
| @Test |
| public void testInference() { |
| //---------- verified against R, version 1.8.1 ----- |
| // infData |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(infData); |
| Assert.assertEquals("slope std err", 0.011448491, |
| regression.getSlopeStdErr(), 1E-10); |
| Assert.assertEquals("std err intercept", 0.286036932, |
| regression.getInterceptStdErr(),1E-8); |
| Assert.assertEquals("significance", 4.596e-07, |
| regression.getSignificance(),1E-8); |
| Assert.assertEquals("slope conf interval half-width", 0.0270713794287, |
| regression.getSlopeConfidenceInterval(),1E-8); |
| // infData2 |
| regression = new SimpleRegression(); |
| regression.addData(infData2); |
| Assert.assertEquals("slope std err", 1.07260253, |
| regression.getSlopeStdErr(), 1E-8); |
| Assert.assertEquals("std err intercept",4.17718672, |
| regression.getInterceptStdErr(),1E-8); |
| Assert.assertEquals("significance", 0.261829133982, |
| regression.getSignificance(),1E-11); |
| Assert.assertEquals("slope conf interval half-width", 2.97802204827, |
| regression.getSlopeConfidenceInterval(),1E-8); |
| //------------- End R-verified tests ------------------------------- |
| |
| //FIXME: get a real example to test against with alpha = .01 |
| Assert.assertTrue("tighter means wider", |
| regression.getSlopeConfidenceInterval() < regression.getSlopeConfidenceInterval(0.01)); |
| |
| try { |
| regression.getSlopeConfidenceInterval(1); |
| Assert.fail("expecting MathIllegalArgumentException for alpha = 1"); |
| } catch (MathIllegalArgumentException ex) { |
| // ignored |
| } |
| |
| } |
| |
| @Test |
| public void testPerfect() { |
| SimpleRegression regression = new SimpleRegression(); |
| int n = 100; |
| for (int i = 0; i < n; i++) { |
| regression.addData(((double) i) / (n - 1), i); |
| } |
| Assert.assertEquals(0.0, regression.getSignificance(), 1.0e-5); |
| Assert.assertTrue(regression.getSlope() > 0.0); |
| Assert.assertTrue(regression.getSumSquaredErrors() >= 0.0); |
| } |
| |
| @Test |
| public void testPerfect2() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(0, 0); |
| regression.addData(1, 1); |
| regression.addData(2, 2); |
| Assert.assertEquals(0.0, regression.getSlopeStdErr(), 0.0); |
| Assert.assertEquals(0.0, regression.getSignificance(), Double.MIN_VALUE); |
| Assert.assertEquals(1, regression.getRSquare(), Double.MIN_VALUE); |
| } |
| |
| @Test |
| public void testPerfectNegative() { |
| SimpleRegression regression = new SimpleRegression(); |
| int n = 100; |
| for (int i = 0; i < n; i++) { |
| regression.addData(- ((double) i) / (n - 1), i); |
| } |
| |
| Assert.assertEquals(0.0, regression.getSignificance(), 1.0e-5); |
| Assert.assertTrue(regression.getSlope() < 0.0); |
| } |
| |
| @Test |
| public void testRandom() { |
| SimpleRegression regression = new SimpleRegression(); |
| Random random = new Random(1); |
| int n = 100; |
| for (int i = 0; i < n; i++) { |
| regression.addData(((double) i) / (n - 1), random.nextDouble()); |
| } |
| |
| Assert.assertTrue( 0.0 < regression.getSignificance() |
| && regression.getSignificance() < 1.0); |
| } |
| |
| |
| // Jira MATH-85 = Bugzilla 39432 |
| @Test |
| public void testSSENonNegative() { |
| double[] y = { 8915.102, 8919.302, 8923.502 }; |
| double[] x = { 1.107178495E2, 1.107264895E2, 1.107351295E2 }; |
| SimpleRegression reg = new SimpleRegression(); |
| for (int i = 0; i < x.length; i++) { |
| reg.addData(x[i], y[i]); |
| } |
| Assert.assertTrue(reg.getSumSquaredErrors() >= 0.0); |
| } |
| |
| // Test remove X,Y (single observation) |
| @Test |
| public void testRemoveXY() { |
| // Create regression with inference data then remove to test |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(infData); |
| regression.removeData(removeX, removeY); |
| regression.addData(removeX, removeY); |
| // Use the inference assertions to make sure that everything worked |
| Assert.assertEquals("slope std err", 0.011448491, |
| regression.getSlopeStdErr(), 1E-10); |
| Assert.assertEquals("std err intercept", 0.286036932, |
| regression.getInterceptStdErr(),1E-8); |
| Assert.assertEquals("significance", 4.596e-07, |
| regression.getSignificance(),1E-8); |
| Assert.assertEquals("slope conf interval half-width", 0.0270713794287, |
| regression.getSlopeConfidenceInterval(),1E-8); |
| } |
| |
| |
| // Test remove single observation in array |
| @Test |
| public void testRemoveSingle() { |
| // Create regression with inference data then remove to test |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(infData); |
| regression.removeData(removeSingle); |
| regression.addData(removeSingle); |
| // Use the inference assertions to make sure that everything worked |
| Assert.assertEquals("slope std err", 0.011448491, |
| regression.getSlopeStdErr(), 1E-10); |
| Assert.assertEquals("std err intercept", 0.286036932, |
| regression.getInterceptStdErr(),1E-8); |
| Assert.assertEquals("significance", 4.596e-07, |
| regression.getSignificance(),1E-8); |
| Assert.assertEquals("slope conf interval half-width", 0.0270713794287, |
| regression.getSlopeConfidenceInterval(),1E-8); |
| } |
| |
| // Test remove multiple observations |
| @Test |
| public void testRemoveMultiple() { |
| // Create regression with inference data then remove to test |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(infData); |
| regression.removeData(removeMultiple); |
| regression.addData(removeMultiple); |
| // Use the inference assertions to make sure that everything worked |
| Assert.assertEquals("slope std err", 0.011448491, |
| regression.getSlopeStdErr(), 1E-10); |
| Assert.assertEquals("std err intercept", 0.286036932, |
| regression.getInterceptStdErr(),1E-8); |
| Assert.assertEquals("significance", 4.596e-07, |
| regression.getSignificance(),1E-8); |
| Assert.assertEquals("slope conf interval half-width", 0.0270713794287, |
| regression.getSlopeConfidenceInterval(),1E-8); |
| } |
| |
| // Remove observation when empty |
| @Test |
| public void testRemoveObsFromEmpty() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.removeData(removeX, removeY); |
| Assert.assertEquals(regression.getN(), 0); |
| } |
| |
| // Remove single observation to empty |
| @Test |
| public void testRemoveObsFromSingle() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(removeX, removeY); |
| regression.removeData(removeX, removeY); |
| Assert.assertEquals(regression.getN(), 0); |
| } |
| |
| // Remove multiple observations to empty |
| @Test |
| public void testRemoveMultipleToEmpty() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(removeMultiple); |
| regression.removeData(removeMultiple); |
| Assert.assertEquals(regression.getN(), 0); |
| } |
| |
| // Remove multiple observations past empty (i.e. size of array > n) |
| @Test |
| public void testRemoveMultiplePastEmpty() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(removeX, removeY); |
| regression.removeData(removeMultiple); |
| Assert.assertEquals(regression.getN(), 0); |
| } |
| } |
