Author: psteitz
Date: Wed Jul 13 17:27:41 2011
New Revision: 1146147
URL: http://svn.apache.org/viewvc?rev=1146147&view=rev
Log:
Added tests based on NIST certified data ("Wampler").
JIRA: MATH-616
Contributed by Greg Sterijevski
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
Modified:
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java?rev=1146147&r1=1146146&r2=1146147&view=diff
==============================================================================
---
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
(original)
+++
commons/proper/math/trunk/src/test/java/org/apache/commons/math/stat/regression/OLSMultipleLinearRegressionTest.java
Wed Jul 13 17:27:41 2011
@@ -509,4 +509,278 @@ public class OLSMultipleLinearRegression
public void testNewSampleDataXNull() {
createRegression().newSampleData(new double[] {}, null);
}
+
+ /*
+ * This is a test based on the Wampler1 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler1.shtml
+ */
+ @Test
+ public void testWampler1() throws Exception {
+ double[] data = new double[]{
+ 1, 0,
+ 6, 1,
+ 63, 2,
+ 364, 3,
+ 1365, 4,
+ 3906, 5,
+ 9331, 6,
+ 19608, 7,
+ 37449, 8,
+ 66430, 9,
+ 111111, 10,
+ 177156, 11,
+ 271453, 12,
+ 402234, 13,
+ 579195, 14,
+ 813616, 15,
+ 1118481, 16,
+ 1508598, 17,
+ 2000719, 18,
+ 2613660, 19,
+ 3368421, 20};
+ OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
+
+
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ model.newSampleData(tmp, nobs, nvars);
+ double[] betaHat = model.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{1.0,
+ 1.0, 1.0,
+ 1.0, 1.0,
+ 1.0}, 1E-8);
+
+ double[] se = model.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{0.0,
+ 0.0, 0.0,
+ 0.0, 0.0,
+ 0.0}, 1E-8);
+
+ TestUtils.assertEquals(1.0, model.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(0, model.estimateErrorVariance(), 1.0e-7);
+ TestUtils.assertEquals(0.00, model.calculateResidualSumOfSquares(),
1.0e-6);
+
+ return;
+ }
+
+ /*
+ * This is a test based on the Wampler2 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler2.shtml
+ */
+ @Test
+ public void testWampler2() throws Exception {
+ double[] data = new double[]{
+ 1.00000, 0,
+ 1.11111, 1,
+ 1.24992, 2,
+ 1.42753, 3,
+ 1.65984, 4,
+ 1.96875, 5,
+ 2.38336, 6,
+ 2.94117, 7,
+ 3.68928, 8,
+ 4.68559, 9,
+ 6.00000, 10,
+ 7.71561, 11,
+ 9.92992, 12,
+ 12.75603, 13,
+ 16.32384, 14,
+ 20.78125, 15,
+ 26.29536, 16,
+ 33.05367, 17,
+ 41.26528, 18,
+ 51.16209, 19,
+ 63.00000, 20};
+ OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
+
+
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ model.newSampleData(tmp, nobs, nvars);
+ double[] betaHat = model.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0e-1,
+ 1.0e-2,
+ 1.0e-3, 1.0e-4,
+ 1.0e-5}, 1E-8);
+
+ double[] se = model.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{0.0,
+ 0.0, 0.0,
+ 0.0, 0.0,
+ 0.0}, 1E-8);
+ TestUtils.assertEquals(1.0, model.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(0, model.estimateErrorVariance(), 1.0e-7);
+ TestUtils.assertEquals(0.00, model.calculateResidualSumOfSquares(),
1.0e-6);
+ return;
+ }
+
+ /*
+ * This is a test based on the Wampler3 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler3.shtml
+ */
+ @Test
+ public void testWampler3() throws Exception {
+ double[] data = new double[]{
+ 760, 0,
+ -2042, 1,
+ 2111, 2,
+ -1684, 3,
+ 3888, 4,
+ 1858, 5,
+ 11379, 6,
+ 17560, 7,
+ 39287, 8,
+ 64382, 9,
+ 113159, 10,
+ 175108, 11,
+ 273291, 12,
+ 400186, 13,
+ 581243, 14,
+ 811568, 15,
+ 1121004, 16,
+ 1506550, 17,
+ 2002767, 18,
+ 2611612, 19,
+ 3369180, 20};
+
+ OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ model.newSampleData(tmp, nobs, nvars);
+ double[] betaHat = model.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0}, 1E-8);
+
+ double[] se = model.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{2152.32624678170,
+ 2363.55173469681, 779.343524331583,
+ 101.475507550350, 5.64566512170752,
+ 0.112324854679312}, 1E-8); //
+
+ TestUtils.assertEquals(.999995559025820, model.calculateRSquared(),
1.0e-10);
+ TestUtils.assertEquals(5570284.53333333,
model.estimateErrorVariance(), 1.0e-6);
+ TestUtils.assertEquals(83554268.0000000,
model.calculateResidualSumOfSquares(), 1.0e-5);
+ return;
+ }
+
+ /*
+ * This is a test based on the Wampler4 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler4.shtml
+ */
+ @Test
+ public void testWampler4() throws Exception {
+ double[] data = new double[]{
+ 75901, 0,
+ -204794, 1,
+ 204863, 2,
+ -204436, 3,
+ 253665, 4,
+ -200894, 5,
+ 214131, 6,
+ -185192, 7,
+ 221249, 8,
+ -138370, 9,
+ 315911, 10,
+ -27644, 11,
+ 455253, 12,
+ 197434, 13,
+ 783995, 14,
+ 608816, 15,
+ 1370781, 16,
+ 1303798, 17,
+ 2205519, 18,
+ 2408860, 19,
+ 3444321, 20};
+
+ OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ model.newSampleData(tmp, nobs, nvars);
+ double[] betaHat = model.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0}, 1E-6);
+
+ double[] se = model.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{215232.624678170,
+ 236355.173469681, 77934.3524331583,
+ 10147.5507550350, 564.566512170752,
+ 11.2324854679312}, 1E-8);
+
+ TestUtils.assertEquals(.957478440825662, model.calculateRSquared(),
1.0e-10);
+ TestUtils.assertEquals(55702845333.3333,
model.estimateErrorVariance(), 1.0e-4);
+ TestUtils.assertEquals(835542680000.000,
model.calculateResidualSumOfSquares(), 1.0e-3);
+ return;
+ }
}
