| /* |
| * Copyright 2003-2004 The Apache Software Foundation. |
| * |
| * Licensed 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.math.stat.regression; |
| |
| import java.util.Random; |
| |
| import junit.framework.Test; |
| import junit.framework.TestCase; |
| import junit.framework.TestSuite; |
| /** |
| * Test cases for the TestStatistic class. |
| * |
| * @version $Revision$ $Date$ |
| */ |
| |
| public final class SimpleRegressionTest extends TestCase { |
| |
| /* |
| * NIST "Norris" refernce 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 } |
| }; |
| |
| /* |
| * Data with bad linear fit |
| */ |
| private double[][] infData2 = { { 1, 1 }, {2, 0 }, {3, 5 }, {4, 2 }, |
| {5, -1 }, {6, 12 } |
| }; |
| |
| public SimpleRegressionTest(String name) { |
| super(name); |
| } |
| |
| public void setUp() { |
| } |
| |
| public static Test suite() { |
| TestSuite suite = new TestSuite(SimpleRegressionTest.class); |
| suite.setName("BivariateRegression Tests"); |
| return suite; |
| } |
| |
| 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 |
| assertEquals("slope", 1.00211681802045, regression.getSlope(), 10E-12); |
| assertEquals("slope std err", 0.429796848199937E-03, |
| regression.getSlopeStdErr(),10E-12); |
| assertEquals("number of observations", 36, regression.getN()); |
| assertEquals( "intercept", -0.262323073774029, |
| regression.getIntercept(),10E-12); |
| assertEquals("std err intercept", 0.232818234301152, |
| regression.getInterceptStdErr(),10E-12); |
| assertEquals("r-square", 0.999993745883712, |
| regression.getRSquare(), 10E-12); |
| assertEquals("SSR", 4255954.13232369, |
| regression.getRegressionSumSquares(), 10E-9); |
| assertEquals("MSE", 0.782864662630069, |
| regression.getMeanSquareError(), 10E-10); |
| assertEquals("SSE", 26.6173985294224, |
| regression.getSumSquaredErrors(),10E-9); |
| // ------------ End certified data tests |
| |
| assertEquals( "predict(0)", -0.262323073774029, |
| regression.predict(0), 10E-12); |
| assertEquals("predict(1)", 1.00211681802045 - 0.262323073774029, |
| regression.predict(1), 10E-12); |
| } |
| |
| public void testCorr() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(corrData); |
| assertEquals("number of observations", 17, regression.getN()); |
| assertEquals("r-square", .896123, regression.getRSquare(), 10E-6); |
| assertEquals("r", -0.94663767742, regression.getR(), 1E-10); |
| } |
| |
| public void testNaNs() { |
| SimpleRegression regression = new SimpleRegression(); |
| assertTrue("intercept not NaN", Double.isNaN(regression.getIntercept())); |
| assertTrue("slope not NaN", Double.isNaN(regression.getSlope())); |
| assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr())); |
| assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr())); |
| assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError())); |
| assertTrue("e not NaN", Double.isNaN(regression.getR())); |
| assertTrue("r-square not NaN", Double.isNaN(regression.getRSquare())); |
| assertTrue( "RSS not NaN", Double.isNaN(regression.getRegressionSumSquares())); |
| assertTrue("SSE not NaN",Double.isNaN(regression.getSumSquaredErrors())); |
| assertTrue("SSTO not NaN", Double.isNaN(regression.getTotalSumSquares())); |
| 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... |
| assertTrue("intercept not NaN", Double.isNaN(regression.getIntercept())); |
| assertTrue("slope not NaN", Double.isNaN(regression.getSlope())); |
| assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr())); |
| assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr())); |
| assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError())); |
| assertTrue("e not NaN", Double.isNaN(regression.getR())); |
| assertTrue("r-square not NaN", Double.isNaN(regression.getRSquare())); |
| assertTrue("RSS not NaN", Double.isNaN(regression.getRegressionSumSquares())); |
| assertTrue("SSE not NaN", Double.isNaN(regression.getSumSquaredErrors())); |
| assertTrue("predict not NaN", Double.isNaN(regression.predict(0))); |
| |
| // but SSTO should be OK |
| assertTrue("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 |
| assertTrue("interceptNaN", !Double.isNaN(regression.getIntercept())); |
| assertTrue("slope NaN", !Double.isNaN(regression.getSlope())); |
| assertTrue ("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr())); |
| assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr())); |
| assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError())); |
| assertTrue("r NaN", !Double.isNaN(regression.getR())); |
| assertTrue("r-square NaN", !Double.isNaN(regression.getRSquare())); |
| assertTrue("RSS NaN", !Double.isNaN(regression.getRegressionSumSquares())); |
| assertTrue("SSE NaN", !Double.isNaN(regression.getSumSquaredErrors())); |
| assertTrue("SSTO NaN", !Double.isNaN(regression.getTotalSumSquares())); |
| assertTrue("predict NaN", !Double.isNaN(regression.predict(0))); |
| |
| regression.addData(1, 4); |
| |
| // MSE, MSE, s(b0), s(b1) should all be OK now |
| assertTrue("MSE NaN", !Double.isNaN(regression.getMeanSquareError())); |
| assertTrue("slope std err NaN", !Double.isNaN(regression.getSlopeStdErr())); |
| assertTrue("intercept std err NaN", !Double.isNaN(regression.getInterceptStdErr())); |
| } |
| |
| public void testClear() { |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(corrData); |
| assertEquals("number of observations", 17, regression.getN()); |
| regression.clear(); |
| assertEquals("number of observations", 0, regression.getN()); |
| regression.addData(corrData); |
| assertEquals("r-square", .896123, regression.getRSquare(), 10E-6); |
| regression.addData(data); |
| assertEquals("number of observations", 53, regression.getN()); |
| } |
| |
| public void testInference() throws Exception { |
| //---------- verified against R, version 1.8.1 ----- |
| // infData |
| SimpleRegression regression = new SimpleRegression(); |
| regression.addData(infData); |
| assertEquals("slope std err", 0.011448491, |
| regression.getSlopeStdErr(), 1E-10); |
| assertEquals("std err intercept", 0.286036932, |
| regression.getInterceptStdErr(),1E-8); |
| assertEquals("significance", 4.596e-07, |
| regression.getSignificance(),1E-8); |
| assertEquals("slope conf interval half-width", 0.0270713794287, |
| regression.getSlopeConfidenceInterval(),1E-8); |
| // infData2 |
| regression = new SimpleRegression(); |
| regression.addData(infData2); |
| assertEquals("slope std err", 1.07260253, |
| regression.getSlopeStdErr(), 1E-8); |
| assertEquals("std err intercept",4.17718672, |
| regression.getInterceptStdErr(),1E-8); |
| assertEquals("significance", 0.261829133982, |
| regression.getSignificance(),1E-11); |
| 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 |
| assertTrue("tighter means wider", |
| regression.getSlopeConfidenceInterval() < regression.getSlopeConfidenceInterval(0.01)); |
| |
| try { |
| double x = regression.getSlopeConfidenceInterval(1); |
| fail("expecting IllegalArgumentException for alpha = 1"); |
| } catch (IllegalArgumentException ex) { |
| ; |
| } |
| |
| } |
| |
| public void testPerfect() throws Exception { |
| SimpleRegression regression = new SimpleRegression(); |
| int n = 100; |
| for (int i = 0; i < n; i++) { |
| regression.addData(((double) i) / (n - 1), i); |
| } |
| assertEquals(0.0, regression.getSignificance(), 1.0e-5); |
| assertTrue(regression.getSlope() > 0.0); |
| } |
| |
| public void testPerfectNegative() throws Exception { |
| SimpleRegression regression = new SimpleRegression(); |
| int n = 100; |
| for (int i = 0; i < n; i++) { |
| regression.addData(- ((double) i) / (n - 1), i); |
| } |
| |
| assertEquals(0.0, regression.getSignificance(), 1.0e-5); |
| assertTrue(regression.getSlope() < 0.0); |
| } |
| |
| public void testRandom() throws Exception { |
| 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()); |
| } |
| |
| assertTrue( 0.0 < regression.getSignificance() |
| && regression.getSignificance() < 1.0); |
| } |
| } |