* Modified multiple regression newSample methods to ensure that by default in all cases,
regression models are estimated with intercept terms. Prior to the fix for this issue, newXSampleData(double[][]), newSampleData(double[], double[][]) and newSampleData(double[], double[][], double[][]) all required columns of "1's" to be inserted into the x[][] arrays to create a model with an intercept term; while newSampleData(double[], int, int) created a model including an intercept term without requiring the unitary column. All methods have been changed to eliminate the need for users to add unitary columns to specify regression models. * Improved javadoc * Improved tests JIRA: MATH-411 git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/branches/MATH_2_X@993574 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
a7cfaf46e3
commit
995efe1ead
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@ -126,6 +126,7 @@ public enum LocalizedFormats implements Localizable {
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INVALID_INTERVAL_INITIAL_VALUE_PARAMETERS("invalid interval, initial value parameters: lower={0}, initial={1}, upper={2}"),
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INVALID_ITERATIONS_LIMITS("invalid iteration limits: min={0}, max={1}"),
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INVALID_MAX_ITERATIONS("bad value for maximum iterations number: {0}"),
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INVALID_REGRESSION_ARRAY("input data array length = {0} does not match the number of observations = {1} and the number of regressors = {2}"),
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INVALID_ROUNDING_METHOD("invalid rounding method {0}, valid methods: {1} ({2}), {3} ({4}), {5} ({6}), {7} ({8}), {9} ({10}), {11} ({12}), {13} ({14}), {15} ({16})"),
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ITERATOR_EXHAUSTED("iterator exhausted"),
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LCM_OVERFLOW_32_BITS("overflow: lcm({0}, {1}) is 2^31"),
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@ -40,14 +40,47 @@ public abstract class AbstractMultipleLinearRegression implements
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protected RealVector Y;
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/**
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* Loads model x and y sample data from a flat array of data, overriding any previous sample.
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* Assumes that rows are concatenated with y values first in each row.
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* <p>Loads model x and y sample data from a flat input array, overriding any previous sample.
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* </p>
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* <p>Assumes that rows are concatenated with y values first in each row. For example, an input
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* <code>data</code> array containing the sequence of values (1, 2, 3, 4, 5, 6, 7, 8, 9) with
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* <code>nobs = 3</code> and <code>nvars = 2</code> creates a regression dataset with two
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* independent variables, as below:
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* <pre>
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* y x[0] x[1]
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* --------------
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* 1 2 3
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* 4 5 6
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* 7 8 9
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* </pre>
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* </p>
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* <p>Note that there is no need to add an initial unitary column (column of 1's) when
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* specifying a model including an intercept term.
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* </p>
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* <p>Throws IllegalArgumentException if any of the following preconditions fail:
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* <ul><li><code>data</code> cannot be null</li>
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* <li><code>data.length = nobs * (nvars + 1)</li>
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* <li><code>nobs > nvars</code></li></ul>
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* </p>
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*
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* @param data input data array
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* @param nobs number of observations (rows)
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* @param nvars number of independent variables (columns, not counting y)
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* @throws IllegalArgumentException if the preconditions are not met
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*/
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public void newSampleData(double[] data, int nobs, int nvars) {
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if (data == null) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NULL_NOT_ALLOWED);
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}
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if (data.length != nobs * (nvars + 1)) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.INVALID_REGRESSION_ARRAY, data.length, nobs, nvars);
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}
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if (nobs <= nvars) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS);
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}
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double[] y = new double[nobs];
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double[][] x = new double[nobs][nvars + 1];
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int pointer = 0;
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@ -63,30 +96,82 @@ public abstract class AbstractMultipleLinearRegression implements
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}
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/**
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* Loads new y sample data, overriding any previous sample
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* Loads new y sample data, overriding any previous data.
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*
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* @param y the [n,1] array representing the y sample
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* @param y the array representing the y sample
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* @throws IllegalArgumentException if y is null or empty
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*/
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protected void newYSampleData(double[] y) {
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if (y == null) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NULL_NOT_ALLOWED);
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}
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if (y.length == 0) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NO_DATA);
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}
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this.Y = new ArrayRealVector(y);
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}
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/**
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* Loads new x sample data, overriding any previous sample
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*
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* @param x the [n,k] array representing the x sample
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* <p>Loads new x sample data, overriding any previous data.
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* </p>
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* The input <code>x</code> array should have one row for each sample
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* observation, with columns corresponding to independent variables.
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* For example, if <pre>
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* <code> x = new double[][] {{1, 2}, {3, 4}, {5, 6}} </code></pre>
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* then <code>setXSampleData(x) </code> results in a model with two independent
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* variables and 3 observations:
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* <pre>
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* x[0] x[1]
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* ----------
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* 1 2
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* 3 4
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* 5 6
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* </pre>
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* </p>
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* <p>Note that there is no need to add an initial unitary column (column of 1's) when
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* specifying a model including an intercept term.
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* </p>
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* @param x the rectangular array representing the x sample
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* @throws IllegalArgumentException if x is null, empty or not rectangular
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*/
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protected void newXSampleData(double[][] x) {
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this.X = new Array2DRowRealMatrix(x);
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if (x == null) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NULL_NOT_ALLOWED);
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}
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if (x.length == 0) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NO_DATA);
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}
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final int nVars = x[0].length;
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final double[][] xAug = new double[x.length][nVars + 1];
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for (int i = 0; i < x.length; i++) {
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if (x[i].length != nVars) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.DIFFERENT_ROWS_LENGTHS,
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x[i].length, nVars);
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}
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xAug[i][0] = 1.0d;
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System.arraycopy(x[i], 0, xAug[i], 1, nVars);
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}
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this.X = new Array2DRowRealMatrix(xAug, false);
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}
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/**
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* Validates sample data.
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* Validates sample data. Checks that
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* <ul><li>Neither x nor y is null or empty;</li>
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* <li>The length (i.e. number of rows) of x equals the length of y</li>
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* <li>x has at least one more row than it has columns (i.e. there is
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* sufficient data to estimate regression coefficients for each of the
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* columns in x plus an intercept.</li>
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* </ul>
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*
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* @param x the [n,k] array representing the x data
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* @param y the [n,1] array representing the y data
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* @throws IllegalArgumentException if any of the checks fail
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*
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* @param x the [n,k] array representing the x sample
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* @param y the [n,1] array representing the y sample
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* @throws IllegalArgumentException if the x and y array data are not
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* compatible for the regression
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*/
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protected void validateSampleData(double[][] x, double[] y) {
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if ((x == null) || (y == null) || (x.length != y.length)) {
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@ -94,7 +179,12 @@ public abstract class AbstractMultipleLinearRegression implements
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LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
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(x == null) ? 0 : x.length,
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(y == null) ? 0 : y.length);
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} else if ((x.length > 0) && (x[0].length > x.length)) {
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}
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if (x.length == 0) { // Must be no y data either
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NO_DATA);
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}
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if (x[0].length + 1 > x.length) {
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throw MathRuntimeException.createIllegalArgumentException(
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LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
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x.length, x[0].length);
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@ -102,12 +192,13 @@ public abstract class AbstractMultipleLinearRegression implements
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}
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/**
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* Validates sample data.
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* Validates that the x data and covariance matrix have the same
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* number of rows and that the covariance matrix is square.
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*
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* @param x the [n,k] array representing the x sample
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* @param covariance the [n,n] array representing the covariance matrix
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* @throws IllegalArgumentException if the x sample data or covariance
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* matrix are not compatible for the regression
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* @throws IllegalArgumentException if the number of rows in x is not equal
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* to the number of rows in covariance or covariance is not square.
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*/
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protected void validateCovarianceData(double[][] x, double[][] covariance) {
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if (x.length != covariance.length) {
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@ -77,8 +77,7 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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/**
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* {@inheritDoc}
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*
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* Computes and caches QR decomposition of the X matrix
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* <p>This implementation computes and caches the QR decomposition of the X matrix.</p>
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*/
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@Override
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public void newSampleData(double[] data, int nobs, int nvars) {
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@ -132,7 +131,7 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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}
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/**
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* Returns the sum of square residuals.
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* Returns the sum of squared residuals.
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*
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* @return residual sum of squares
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*/
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@ -168,22 +167,20 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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final double n = X.getRowDimension();
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return 1 - (calculateResidualSumOfSquares() * (n - 1)) /
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(calculateTotalSumOfSquares() * (n - X.getColumnDimension()));
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// return 1 - ((1 - calculateRSquare()) * (n - 1) / (n - X.getColumnDimension() - 1));
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}
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/**
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* Loads new x sample data, overriding any previous sample
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*
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* @param x the [n,k] array representing the x sample
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* {@inheritDoc}
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* <p>This implementation computes and caches the QR decomposition of the X matrix once it is successfully loaded.</p>
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*/
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@Override
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protected void newXSampleData(double[][] x) {
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this.X = new Array2DRowRealMatrix(x);
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super.newXSampleData(x);
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qr = new QRDecompositionImpl(X);
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}
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/**
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* Calculates regression coefficients using OLS.
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* Calculates the regression coefficients using OLS.
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*
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* @return beta
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*/
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@ -98,6 +98,7 @@ INVALID_BRACKETING_PARAMETERS = param\u00e8tres d''encadrement invalides : borne
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INVALID_INTERVAL_INITIAL_VALUE_PARAMETERS = param\u00e8tres de l''intervalle initial invalides : borne inf = {0}, valeur initiale = {1}, borne sup = {2}
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INVALID_ITERATIONS_LIMITS = limites d''it\u00e9rations invalides : min = {0}, max = {1}
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INVALID_MAX_ITERATIONS = valeur invalide pour le nombre maximal d''it\u00e9rations : {0}
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INVALID_REGRESSION_ARRAY= longueur du tableau de donn\u00e9es = {0} ne correspond pas au nombre d'observations = {1} et le nombre de variables explicatives = {2}
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INVALID_ROUNDING_METHOD = m\u00e9thode d''arondi {0} invalide, m\u00e9thodes valides : {1} ({2}), {3} ({4}), {5} ({6}), {7} ({8}), {9} ({10}), {11} ({12}), {13} ({14}), {15} ({16})
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ITERATOR_EXHAUSTED = it\u00e9ration achev\u00e9e
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LCM_OVERFLOW_32_BITS = d\u00e9passement de capacit\u00e9 : le MCM de {0} et {1} vaut 2^31
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@ -52,6 +52,19 @@ The <action> type attribute can be add,update,fix,remove.
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If the output is not quite correct, check for invisible trailing spaces!
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-->
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<release version="2.2" date="TBD" description="TBD">
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<action dev="psteitz" type="fix" issue="MATH-411">
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Modified multiple regression newSample methods to ensure that by default in all cases,
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regression models are estimated with intercept terms. Prior to the fix for this issue,
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newXSampleData(double[][]), newSampleData(double[], double[][]) and
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newSampleData(double[], double[][], double[][]) all required columns of "1's" to be inserted
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into the x[][] arrays to create a model with an intercept term; while newSampleData(double[], int, int)
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created a model including an intercept term without requiring the unitary column. All methods have
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been changed to eliminate the need for users to add unitary columns to specify regression models.
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<!-- uncomment when MATH-409 is resolved (noIntercept option)
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Users of OLSMultipleLinearRegression or GLSMultipleLinearRegression versions 2.0 or 2.1 should either
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verify that their code either does not use the first set of data loading methods above or set the noIntercept
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property on estimated models to get the previous behavior. -->
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</action>
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<action dev="luc" type="fix" issue="MATH-412" due-to="Bill Rossi">
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Added the dfp library providing arbitrary precision floating point computation in the spirit of
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IEEE 854-1987 (not exactly as it uses base 1000 instead of base 10). In addition to finite numbers,
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@ -16,10 +16,13 @@
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*/
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package org.apache.commons.math.stat.regression;
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import static org.junit.Assert.assertEquals;
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import org.junit.Before;
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import org.junit.Test;
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import org.apache.commons.math.TestUtils;
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import org.apache.commons.math.stat.StatUtils;
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import org.apache.commons.math.linear.MatrixUtils;
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import org.apache.commons.math.linear.RealMatrix;
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import org.apache.commons.math.linear.RealVector;
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public class GLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbstractTest {
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@ -32,12 +35,12 @@ public class GLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
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public void setUp(){
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y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
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x = new double[6][];
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x[0] = new double[]{1.0, 0, 0, 0, 0, 0};
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x[1] = new double[]{1.0, 2.0, 0, 0, 0, 0};
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x[2] = new double[]{1.0, 0, 3.0, 0, 0, 0};
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x[3] = new double[]{1.0, 0, 0, 4.0, 0, 0};
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x[4] = new double[]{1.0, 0, 0, 0, 5.0, 0};
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x[5] = new double[]{1.0, 0, 0, 0, 0, 6.0};
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x[0] = new double[]{0, 0, 0, 0, 0};
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x[1] = new double[]{2.0, 0, 0, 0, 0};
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x[2] = new double[]{0, 3.0, 0, 0, 0};
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x[3] = new double[]{0, 0, 4.0, 0, 0};
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x[4] = new double[]{0, 0, 0, 5.0, 0};
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x[5] = new double[]{0, 0, 0, 0, 6.0};
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omega = new double[6][];
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omega[0] = new double[]{1.0, 0, 0, 0, 0, 0};
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omega[1] = new double[]{0, 2.0, 0, 0, 0, 0};
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@ -115,7 +118,7 @@ public class GLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
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@Override
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protected int getNumberOfRegressors() {
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return x[0].length;
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return x[0].length + 1;
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}
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@Override
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@ -135,4 +138,69 @@ public class GLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
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model.newSampleData(y, x, omega);
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TestUtils.assertEquals(model.calculateYVariance(), 3.5, 0);
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}
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/**
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* Verifies that setting X, Y and covariance separately has the same effect as newSample(X,Y,cov).
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*/
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@Test
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public void testNewSample2() throws Exception {
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double[] y = new double[] {1, 2, 3, 4};
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double[][] x = new double[][] {
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{19, 22, 33},
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{20, 30, 40},
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{25, 35, 45},
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{27, 37, 47}
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};
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double[][] covariance = MatrixUtils.createRealIdentityMatrix(4).scalarMultiply(2).getData();
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GLSMultipleLinearRegression regression = new GLSMultipleLinearRegression();
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regression.newSampleData(y, x, covariance);
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RealMatrix combinedX = regression.X.copy();
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RealVector combinedY = regression.Y.copy();
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RealMatrix combinedCovInv = regression.getOmegaInverse();
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regression.newXSampleData(x);
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regression.newYSampleData(y);
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assertEquals(combinedX, regression.X);
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assertEquals(combinedY, regression.Y);
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assertEquals(combinedCovInv, regression.getOmegaInverse());
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}
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/**
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* Verifies that GLS with identity covariance matrix gives the same results
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* as OLS.
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*/
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@Test
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public void testGLSOLSConsistency() throws Exception {
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// Use Longley data to test
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double[] design = new double[] {
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60323,83.0,234289,2356,1590,107608,1947,
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61122,88.5,259426,2325,1456,108632,1948,
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60171,88.2,258054,3682,1616,109773,1949,
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61187,89.5,284599,3351,1650,110929,1950,
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63221,96.2,328975,2099,3099,112075,1951,
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63639,98.1,346999,1932,3594,113270,1952,
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64989,99.0,365385,1870,3547,115094,1953,
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63761,100.0,363112,3578,3350,116219,1954,
|
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66019,101.2,397469,2904,3048,117388,1955,
|
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67857,104.6,419180,2822,2857,118734,1956,
|
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68169,108.4,442769,2936,2798,120445,1957,
|
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66513,110.8,444546,4681,2637,121950,1958,
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68655,112.6,482704,3813,2552,123366,1959,
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69564,114.2,502601,3931,2514,125368,1960,
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69331,115.7,518173,4806,2572,127852,1961,
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70551,116.9,554894,4007,2827,130081,1962
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};
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RealMatrix identityCov = MatrixUtils.createRealIdentityMatrix(16);
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GLSMultipleLinearRegression glsModel = new GLSMultipleLinearRegression();
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OLSMultipleLinearRegression olsModel = new OLSMultipleLinearRegression();
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glsModel.newSampleData(design, 16, 6);
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olsModel.newSampleData(design, 16, 6);
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glsModel.newCovarianceData(identityCov.getData());
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double[] olsBeta = olsModel.calculateBeta().getData();
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double[] glsBeta = glsModel.calculateBeta().getData();
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||||
// TODO: Should have assertRelativelyEquals(double[], double[], eps) in TestUtils
|
||||
// Should also add RealVector and RealMatrix versions
|
||||
for (int i = 0; i < olsBeta.length; i++) {
|
||||
TestUtils.assertRelativelyEquals(olsBeta[i], glsBeta[i], 10E-7);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -16,6 +16,8 @@
|
|||
*/
|
||||
package org.apache.commons.math.stat.regression;
|
||||
|
||||
import org.apache.commons.math.linear.RealMatrix;
|
||||
import org.apache.commons.math.linear.RealVector;
|
||||
import static org.junit.Assert.assertEquals;
|
||||
import static org.junit.Assert.assertTrue;
|
||||
|
||||
|
|
@ -25,14 +27,14 @@ import org.junit.Test;
|
|||
|
||||
public abstract class MultipleLinearRegressionAbstractTest {
|
||||
|
||||
protected MultipleLinearRegression regression;
|
||||
protected AbstractMultipleLinearRegression regression;
|
||||
|
||||
@Before
|
||||
public void setUp(){
|
||||
regression = createRegression();
|
||||
}
|
||||
|
||||
protected abstract MultipleLinearRegression createRegression();
|
||||
protected abstract AbstractMultipleLinearRegression createRegression();
|
||||
|
||||
protected abstract int getNumberOfRegressors();
|
||||
|
||||
|
|
@ -63,5 +65,62 @@ public abstract class MultipleLinearRegressionAbstractTest {
|
|||
assertTrue(variance > 0.0);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Verifies that newSampleData methods consistently insert unitary columns
|
||||
* in design matrix. Confirms the fix for MATH-411.
|
||||
*/
|
||||
@Test
|
||||
public void testNewSample() throws Exception {
|
||||
double[] design = new double[] {
|
||||
1, 19, 22, 33,
|
||||
2, 20, 30, 40,
|
||||
3, 25, 35, 45,
|
||||
4, 27, 37, 47
|
||||
};
|
||||
double[] y = new double[] {1, 2, 3, 4};
|
||||
double[][] x = new double[][] {
|
||||
{19, 22, 33},
|
||||
{20, 30, 40},
|
||||
{25, 35, 45},
|
||||
{27, 37, 47}
|
||||
};
|
||||
AbstractMultipleLinearRegression regression = (AbstractMultipleLinearRegression) createRegression();
|
||||
regression.newSampleData(design, 4, 3);
|
||||
RealMatrix flatX = regression.X.copy();
|
||||
RealVector flatY = regression.Y.copy();
|
||||
regression.newXSampleData(x);
|
||||
regression.newYSampleData(y);
|
||||
assertEquals(flatX, regression.X);
|
||||
assertEquals(flatY, regression.Y);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testNewSampleNullData() throws Exception {
|
||||
double[] data = null;
|
||||
createRegression().newSampleData(data, 2, 3);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testNewSampleInvalidData() throws Exception {
|
||||
double[] data = new double[] {1, 2, 3, 4};
|
||||
createRegression().newSampleData(data, 2, 3);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testNewSampleInsufficientData() throws Exception {
|
||||
double[] data = new double[] {1, 2, 3, 4};
|
||||
createRegression().newSampleData(data, 1, 3);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testXSampleDataNull() {
|
||||
createRegression().newXSampleData(null);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testYSampleDataNull() {
|
||||
createRegression().newYSampleData(null);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ import org.apache.commons.math.linear.MatrixUtils;
|
|||
import org.apache.commons.math.linear.MatrixVisitorException;
|
||||
import org.apache.commons.math.linear.RealMatrix;
|
||||
import org.apache.commons.math.linear.Array2DRowRealMatrix;
|
||||
import org.apache.commons.math.linear.RealVector;
|
||||
import org.apache.commons.math.stat.StatUtils;
|
||||
import org.junit.Before;
|
||||
import org.junit.Test;
|
||||
|
|
@ -38,12 +39,12 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
|||
public void setUp(){
|
||||
y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
|
||||
x = new double[6][];
|
||||
x[0] = new double[]{1.0, 0, 0, 0, 0, 0};
|
||||
x[1] = new double[]{1.0, 2.0, 0, 0, 0, 0};
|
||||
x[2] = new double[]{1.0, 0, 3.0, 0, 0, 0};
|
||||
x[3] = new double[]{1.0, 0, 0, 4.0, 0, 0};
|
||||
x[4] = new double[]{1.0, 0, 0, 0, 5.0, 0};
|
||||
x[5] = new double[]{1.0, 0, 0, 0, 0, 6.0};
|
||||
x[0] = new double[]{0, 0, 0, 0, 0};
|
||||
x[1] = new double[]{2.0, 0, 0, 0, 0};
|
||||
x[2] = new double[]{0, 3.0, 0, 0, 0};
|
||||
x[3] = new double[]{0, 0, 4.0, 0, 0};
|
||||
x[4] = new double[]{0, 0, 0, 5.0, 0};
|
||||
x[5] = new double[]{0, 0, 0, 0, 6.0};
|
||||
super.setUp();
|
||||
}
|
||||
|
||||
|
|
@ -56,24 +57,14 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
|||
|
||||
@Override
|
||||
protected int getNumberOfRegressors() {
|
||||
return x[0].length;
|
||||
return x[0].length + 1;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected int getSampleSize() {
|
||||
return y.length;
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void cannotAddXSampleData() {
|
||||
createRegression().newSampleData(new double[]{}, null);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void cannotAddNullYSampleData() {
|
||||
createRegression().newSampleData(null, new double[][]{});
|
||||
}
|
||||
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void cannotAddSampleDataWithSizeMismatch() {
|
||||
double[] y = new double[]{1.0, 2.0};
|
||||
|
|
@ -248,17 +239,16 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
|||
44.7,46.6,16,29,50.43,
|
||||
42.8,27.7,22,29,58.33
|
||||
};
|
||||
|
||||
// Transform to Y and X required by interface
|
||||
int nobs = 47;
|
||||
int nvars = 4;
|
||||
|
||||
final int nobs = 47;
|
||||
final int nvars = 4;
|
||||
|
||||
// Estimate the model
|
||||
OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
|
||||
model.newSampleData(design, nobs, nvars);
|
||||
|
||||
// Check expected beta values from R
|
||||
double[] betaHat = model.estimateRegressionParameters();
|
||||
final double[] betaHat = model.estimateRegressionParameters();
|
||||
TestUtils.assertEquals(betaHat,
|
||||
new double[]{91.05542390271397,
|
||||
-0.22064551045715,
|
||||
|
|
@ -267,7 +257,7 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
|||
0.12441843147162}, 1E-12);
|
||||
|
||||
// Check expected residuals from R
|
||||
double[] residuals = model.estimateResiduals();
|
||||
final double[] residuals = model.estimateResiduals();
|
||||
TestUtils.assertEquals(residuals, new double[]{
|
||||
7.1044267859730512,1.6580347433531366,
|
||||
4.6944952770029644,8.4548022690166160,13.6547432343186212,
|
||||
|
|
@ -288,7 +278,7 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
|||
1E-12);
|
||||
|
||||
// Check standard errors from R
|
||||
double[] errors = model.estimateRegressionParametersStandardErrors();
|
||||
final double[] errors = model.estimateRegressionParametersStandardErrors();
|
||||
TestUtils.assertEquals(new double[] {6.94881329475087,
|
||||
0.07360008972340,
|
||||
0.27410957467466,
|
||||
|
|
@ -404,4 +394,36 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
|||
model.calculateErrorVariance() * (X.getRowDimension() - X.getColumnDimension()), 1E-20);
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Verifies that setting X and Y separately has the same effect as newSample(X,Y).
|
||||
*/
|
||||
@Test
|
||||
public void testNewSample2() throws Exception {
|
||||
double[] y = new double[] {1, 2, 3, 4};
|
||||
double[][] x = new double[][] {
|
||||
{19, 22, 33},
|
||||
{20, 30, 40},
|
||||
{25, 35, 45},
|
||||
{27, 37, 47}
|
||||
};
|
||||
OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
|
||||
regression.newSampleData(y, x);
|
||||
RealMatrix combinedX = regression.X.copy();
|
||||
RealVector combinedY = regression.Y.copy();
|
||||
regression.newXSampleData(x);
|
||||
regression.newYSampleData(y);
|
||||
assertEquals(combinedX, regression.X);
|
||||
assertEquals(combinedY, regression.Y);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testNewSampleDataYNull() {
|
||||
createRegression().newSampleData(null, new double[][] {});
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
public void testNewSampleDataXNull() {
|
||||
createRegression().newSampleData(new double[] {}, null);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue