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Made intercept/noIntercept configurable in multiple regression classes. JIRA: MATH-409.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@996404 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
820915c236
commit
aba7e04d44
@ -39,6 +39,23 @@ public abstract class AbstractMultipleLinearRegression implements
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/** Y sample data. */
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protected RealVector Y;
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/** Whether or not the regression model includes an intercept. True means no intercept. */
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private boolean noIntercept = false;
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/**
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* @return true if the model has no intercept term; false otherwise
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*/
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public boolean isNoIntercept() {
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return noIntercept;
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}
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/**
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* @param noIntercept true means the model is to be estimated without an intercept term
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*/
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public void setNoIntercept(boolean noIntercept) {
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this.noIntercept = noIntercept;
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}
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/**
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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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@ -55,7 +72,9 @@ public abstract class AbstractMultipleLinearRegression implements
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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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* specifying a model including an intercept term. If {@link #isNoIntercept()} is <code>true</code>,
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* the X matrix will be created without an initial column of "1"s; otherwise this column will
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* be added.
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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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@ -82,12 +101,15 @@ public abstract class AbstractMultipleLinearRegression implements
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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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final int cols = noIntercept ? nvars: nvars + 1;
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double[][] x = new double[nobs][cols];
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int pointer = 0;
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for (int i = 0; i < nobs; i++) {
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y[i] = data[pointer++];
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x[i][0] = 1.0d;
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for (int j = 1; j < nvars + 1; j++) {
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if (!noIntercept) {
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x[i][0] = 1.0d;
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}
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for (int j = noIntercept ? 0 : 1; j < cols; j++) {
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x[i][j] = data[pointer++];
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}
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}
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@ -145,18 +167,22 @@ public abstract class AbstractMultipleLinearRegression implements
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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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if (noIntercept) {
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this.X = new Array2DRowRealMatrix(x, true);
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} else { // Augment design matrix with initial unitary column
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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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xAug[i][0] = 1.0d;
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System.arraycopy(x[i], 0, xAug[i], 1, nVars);
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this.X = new Array2DRowRealMatrix(xAug, false);
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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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@ -22,35 +22,32 @@ import org.apache.commons.math.linear.QRDecomposition;
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import org.apache.commons.math.linear.QRDecompositionImpl;
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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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import org.apache.commons.math.stat.StatUtils;
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import org.apache.commons.math.stat.descriptive.moment.SecondMoment;
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/**
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* <p>Implements ordinary least squares (OLS) to estimate the parameters of a
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* multiple linear regression model.</p>
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*
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* <p>OLS assumes the covariance matrix of the error to be diagonal and with
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* equal variance.</p>
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* <p>
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* u ~ N(0, σ<sup>2</sup>I)
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* </p>
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*
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* <p>The regression coefficients, b, satisfy the normal equations:
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* <p>
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* X<sup>T</sup> X b = X<sup>T</sup> y
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* </p>
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* <p>The regression coefficients, <code>b</code>, satisfy the normal equations:
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* <pre><code> X<sup>T</sup> X b = X<sup>T</sup> y </code></pre></p>
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*
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* <p>To solve the normal equations, this implementation uses QR decomposition
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* of the X matrix. (See {@link QRDecompositionImpl} for details on the
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* decomposition algorithm.)
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* </p>
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* <p>X<sup>T</sup>X b = X<sup>T</sup> y <br/>
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* (QR)<sup>T</sup> (QR) b = (QR)<sup>T</sup>y <br/>
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* R<sup>T</sup> (Q<sup>T</sup>Q) R b = R<sup>T</sup> Q<sup>T</sup> y <br/>
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* R<sup>T</sup> R b = R<sup>T</sup> Q<sup>T</sup> y <br/>
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* (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> R b = (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> Q<sup>T</sup> y <br/>
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* R b = Q<sup>T</sup> y
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* </p>
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* Given Q and R, the last equation is solved by back-substitution.</p>
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* of the <code>X</code> matrix. (See {@link QRDecompositionImpl} for details on the
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* decomposition algorithm.) The <code>X</code> matrix, also known as the <i>design matrix,</i>
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* has rows corresponding to sample observations and columns corresponding to independent
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* variables. When the model is estimated using an intercept term (i.e. when
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* {@link #isNoIntercept() isNoIntercept} is false as it is by default), the <code>X</code>
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* matrix includes an initial column identically equal to 1. We solve the normal equations
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* as follows:
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* <pre><code> X<sup>T</sup>X b = X<sup>T</sup> y
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* (QR)<sup>T</sup> (QR) b = (QR)<sup>T</sup>y
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* R<sup>T</sup> (Q<sup>T</sup>Q) R b = R<sup>T</sup> Q<sup>T</sup> y
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* R<sup>T</sup> R b = R<sup>T</sup> Q<sup>T</sup> y
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* (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> R b = (R<sup>T</sup>)<sup>-1</sup> R<sup>T</sup> Q<sup>T</sup> y
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* R b = Q<sup>T</sup> y </code></pre></p>
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*
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* <p>Given <code>Q</code> and <code>R</code>, the last equation is solved by back-substitution.</p>
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*
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* @version $Revision$ $Date$
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* @since 2.0
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@ -122,12 +119,23 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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}
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/**
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* Returns the sum of squared deviations of Y from its mean.
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* <p>Returns the sum of squared deviations of Y from its mean.</p>
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*
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* @return total sum of squares
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* <p>If the model has no intercept term, <code>0</code> is used for the
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* mean of Y - i.e., what is returned is the sum of the squared Y values.</p>
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*
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* <p>The value returned by this method is the SSTO value used in
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* the {@link #calculateRSquared() R-squared} computation.</p>
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*
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* @return SSTO - the total sum of squares
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* @see #isNoIntercept()
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*/
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public double calculateTotalSumOfSquares() {
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return new SecondMoment().evaluate(Y.getData());
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if (isNoIntercept()) {
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return StatUtils.sumSq(Y.getData());
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} else {
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return new SecondMoment().evaluate(Y.getData());
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}
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}
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/**
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@ -154,24 +162,34 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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}
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/**
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* Returns the adjusted R-squared statistic, defined by the formula <pre>
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* <p>Returns the adjusted R-squared statistic, defined by the formula <pre>
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* R<sup>2</sup><sub>adj</sub> = 1 - [SSR (n - 1)] / [SSTO (n - p)]
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* </pre>
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* where SSR is the {@link #calculateResidualSumOfSquares() sum of squared residuals},
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* SSTO is the {@link #calculateTotalSumOfSquares() total sum of squares}, n is the number
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* of observations and p is the number of parameters estimated (including the intercept).
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* of observations and p is the number of parameters estimated (including the intercept).</p>
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*
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* <p>If the regression is estimated without an intercept term, what is returned is <pre>
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* <code> 1 - (1 - {@link #calculateRSquared()}) * (n / (n - p)) </code>
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* </pre></p>
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*
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* @return adjusted R-Squared statistic
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* @see #isNoIntercept()
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*/
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public double calculateAdjustedRSquared() {
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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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if (isNoIntercept()) {
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return 1 - (1 - calculateRSquared()) * (n / (n - X.getColumnDimension()));
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} else {
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return 1 - (calculateResidualSumOfSquares() * (n - 1)) /
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(calculateTotalSumOfSquares() * (n - X.getColumnDimension()));
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}
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}
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/**
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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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* <p>This implementation computes and caches the QR decomposition of the X matrix
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* 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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@ -190,7 +208,7 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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}
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/**
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* <p>Calculates the variance on the beta by OLS.
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* <p>Calculates the variance-covariance matrix of the regression parameters.
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* </p>
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* <p>Var(b) = (X<sup>T</sup>X)<sup>-1</sup>
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* </p>
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@ -198,7 +216,7 @@ public class OLSMultipleLinearRegression extends AbstractMultipleLinearRegressio
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* to (R<sup>T</sup>R)<sup>-1</sup>, with only the top p rows of
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* R included, where p = the length of the beta vector.</p>
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*
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* @return The beta variance
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* @return The beta variance-covariance matrix
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*/
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@Override
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protected RealMatrix calculateBetaVariance() {
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@ -71,6 +71,11 @@ The <action> type attribute can be add,update,fix,remove.
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</action>
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</release>
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<release version="2.2" date="TBD" description="TBD">
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<action dev="psteitz" type="update" issue="MATH-409">
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Made intercept / no intercept configurable in multiple regression classes. By default, regression
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models are estimated with an intercept term. When the "noIntercept" property is set to
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true, regression models are estimated without intercepts.
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</action>
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<action dev="luc" type="fix" issue="MATH-415">
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Fixed lost cause in MathRuntimeException.createInternalError. Note that the message is still the default
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message for internal errors asking to report a bug to commons-math JIRA tracker. In order to retrieve
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@ -84,10 +89,9 @@ The <action> type attribute can be add,update,fix,remove.
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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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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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@ -22,12 +22,12 @@
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# source("<name-of-this-file>")
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#
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#------------------------------------------------------------------------------
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tol <- 1E-8 # error tolerance for tests
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tol <- 1E-8 # default error tolerance for tests
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#------------------------------------------------------------------------------
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# Function definitions
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source("testFunctions") # utility test functions
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options(digits=16) # override number of digits displayed
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options(digits=16) # override number of digits displayed
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# function to verify OLS computations
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@ -97,7 +97,7 @@ expectedAdjRSquare <- NaN
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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expectedStdError, expectedRSquare, expectedAdjRSquare, "perfect fit")
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# Longly
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# Longley
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#
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# Data Source: J. Longley (1967) "An Appraisal of Least Squares Programs for the
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# Electronic Computer from the Point of View of the User",
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@ -144,7 +144,7 @@ estimates <- matrix(c(-3482258.63459582,890420.383607373,
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expectedBeta <- estimates[,1]
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expectedErrors <- estimates[,2]
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expectedResiduals <- c( 267.340029759711,-94.0139423988359,46.28716775752924,
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expectedResiduals <- c(267.340029759711,-94.0139423988359,46.28716775752924,
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-410.114621930906,309.7145907602313,-249.3112153297231,-164.0489563956039,
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-13.18035686637081,14.30477260005235,455.394094551857,-17.26892711483297,
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-39.0550425226967,-155.5499735953195,-85.6713080421283,341.9315139607727,
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@ -154,7 +154,32 @@ expectedRSquare <- 0.995479004577296
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expectedAdjRSquare <- 0.992465007628826
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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expectedStdError, expectedRSquare, expectedAdjRSquare, "Longly")
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expectedStdError, expectedRSquare, expectedAdjRSquare, "Longley")
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# Model with no intercept
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model <- lm(y ~ 0 + x1 + x2 + x3 + x4 + x5 + x6)
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estimates <- matrix(c(-52.99357013868291, 129.54486693117232,
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0.07107319907358, 0.03016640003786,
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-0.42346585566399, 0.41773654056612,
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-0.57256866841929, 0.27899087467676,
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-0.41420358884978, 0.32128496193363,
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48.41786562001326, 17.68948737819961),
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nrow = 6, ncol = 2, byrow = TRUE)
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expectedBeta <- estimates[,1]
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expectedErrors <- estimates[,2]
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expectedResiduals <- c(279.90274927293092, -130.32465380836874, 90.73228661967445,
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-401.31252201634948, -440.46768772620027, -543.54512853774793, 201.32111639536299,
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215.90889365977932, 73.09368242049943, 913.21694494481869, 424.82484953610174,
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-8.56475876776709, -361.32974610842876, 27.34560497213464, 151.28955976355002,
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-492.49937355336846)
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expectedStdError <- 475.1655079819517
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expectedRSquare <- 0.9999670130706
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expectedAdjRSquare <- 0.999947220913
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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expectedStdError, expectedRSquare, expectedAdjRSquare, "Longley No Intercept")
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# Swiss Fertility (R dataset named "swiss")
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@ -249,5 +274,36 @@ expectedAdjRSquare <- 0.6164363850373927
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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expectedStdError, expectedRSquare, expectedAdjRSquare, "Swiss Fertility")
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# model with no intercept
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model <- lm(y ~ 0 + x1 + x2 + x3 + x4)
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estimates <- matrix(c(0.52191832900513, 0.10470063765677,
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2.36588087917963, 0.41684100584290,
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-0.94770353802795, 0.43370143099691,
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0.30851985863609, 0.07694953606522),
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nrow = 4, ncol = 2, byrow = TRUE)
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expectedBeta <- estimates[,1]
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expectedErrors <- estimates[,2]
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expectedResiduals <- c(44.138759883538249, 27.720705122356215, 35.873200836126799,
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34.574619581211977, 26.600168342080213, 15.074636243026923, -12.704904871199814,
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1.497443824078134, 2.691972687079431, 5.582798774291231, -4.422986561283165,
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-9.198581600334345, 4.481765170730647, 2.273520207553216, -22.649827853221336,
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-17.747900013943308, 20.298314638496436, 6.861405135329779, -8.684712790954924,
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-10.298639278062371, -9.896618896845819, 4.568568616351242, -15.313570491727944,
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-13.762961360873966, 7.156100301980509, 16.722282219843990, 26.716200609071898,
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-1.991466398777079, -2.523342564719335, 9.776486693095093, -5.297535127628603,
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-16.639070567471094, -10.302057295211819, -23.549487860816846, 1.506624392156384,
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-17.939174438345930, 13.105792202765040, -1.943329906928462, -1.516005841666695,
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-0.759066561832886, 20.793137744128977, -2.485236153005426, 27.588238710486976,
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2.658333257106881, -15.998337823623046, -5.550742066720694, -14.219077806826615)
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expectedStdError <- 17.24710630547
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expectedRSquare <- 0.946350722085
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expectedAdjRSquare <- 0.9413600915813
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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expectedStdError, expectedRSquare, expectedAdjRSquare, "Swiss Fertility No Intercept")
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displayDashes(WIDTH)
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@ -93,6 +93,16 @@ public abstract class MultipleLinearRegressionAbstractTest {
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regression.newYSampleData(y);
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assertEquals(flatX, regression.X);
|
||||
assertEquals(flatY, regression.Y);
|
||||
|
||||
// No intercept
|
||||
regression.setNoIntercept(true);
|
||||
regression.newSampleData(design, 4, 3);
|
||||
flatX = regression.X.copy();
|
||||
flatY = regression.Y.copy();
|
||||
regression.newXSampleData(x);
|
||||
regression.newYSampleData(y);
|
||||
assertEquals(flatX, regression.X);
|
||||
assertEquals(flatY, regression.Y);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
|
||||
@ -137,9 +137,8 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
70551,116.9,554894,4007,2827,130081,1962
|
||||
};
|
||||
|
||||
// Transform to Y and X required by interface
|
||||
int nobs = 16;
|
||||
int nvars = 6;
|
||||
final int nobs = 16;
|
||||
final int nvars = 6;
|
||||
|
||||
// Estimate the model
|
||||
OLSMultipleLinearRegression model = new OLSMultipleLinearRegression();
|
||||
@ -182,6 +181,40 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
assertEquals(0.992465007628826, model.calculateAdjustedRSquared(), 1E-12);
|
||||
|
||||
checkVarianceConsistency(model);
|
||||
|
||||
// Estimate model without intercept
|
||||
model.setNoIntercept(true);
|
||||
model.newSampleData(design, nobs, nvars);
|
||||
|
||||
// Check expected beta values from R
|
||||
betaHat = model.estimateRegressionParameters();
|
||||
TestUtils.assertEquals(betaHat,
|
||||
new double[]{-52.99357013868291, 0.07107319907358,
|
||||
-0.42346585566399,-0.57256866841929,
|
||||
-0.41420358884978, 48.41786562001326}, 1E-11);
|
||||
|
||||
// Check standard errors from R
|
||||
errors = model.estimateRegressionParametersStandardErrors();
|
||||
TestUtils.assertEquals(new double[] {129.54486693117232, 0.03016640003786,
|
||||
0.41773654056612, 0.27899087467676, 0.32128496193363,
|
||||
17.68948737819961}, errors, 1E-11);
|
||||
|
||||
// Check expected residuals from R
|
||||
residuals = model.estimateResiduals();
|
||||
TestUtils.assertEquals(residuals, new double[]{
|
||||
279.90274927293092, -130.32465380836874, 90.73228661967445, -401.31252201634948,
|
||||
-440.46768772620027, -543.54512853774793, 201.32111639536299, 215.90889365977932,
|
||||
73.09368242049943, 913.21694494481869, 424.82484953610174, -8.56475876776709,
|
||||
-361.32974610842876, 27.34560497213464, 151.28955976355002, -492.49937355336846},
|
||||
1E-10);
|
||||
|
||||
// Check regression standard error against R
|
||||
assertEquals(475.1655079819517, model.estimateRegressionStandardError(), 1E-10);
|
||||
|
||||
// Check R-Square statistics against R
|
||||
assertEquals(0.9999670130706, model.calculateRSquared(), 1E-12);
|
||||
assertEquals(0.999947220913, model.calculateAdjustedRSquared(), 1E-12);
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
@ -248,7 +281,7 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
model.newSampleData(design, nobs, nvars);
|
||||
|
||||
// Check expected beta values from R
|
||||
final double[] betaHat = model.estimateRegressionParameters();
|
||||
double[] betaHat = model.estimateRegressionParameters();
|
||||
TestUtils.assertEquals(betaHat,
|
||||
new double[]{91.05542390271397,
|
||||
-0.22064551045715,
|
||||
@ -257,7 +290,7 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
0.12441843147162}, 1E-12);
|
||||
|
||||
// Check expected residuals from R
|
||||
final double[] residuals = model.estimateResiduals();
|
||||
double[] residuals = model.estimateResiduals();
|
||||
TestUtils.assertEquals(residuals, new double[]{
|
||||
7.1044267859730512,1.6580347433531366,
|
||||
4.6944952770029644,8.4548022690166160,13.6547432343186212,
|
||||
@ -278,7 +311,7 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
1E-12);
|
||||
|
||||
// Check standard errors from R
|
||||
final double[] errors = model.estimateRegressionParametersStandardErrors();
|
||||
double[] errors = model.estimateRegressionParametersStandardErrors();
|
||||
TestUtils.assertEquals(new double[] {6.94881329475087,
|
||||
0.07360008972340,
|
||||
0.27410957467466,
|
||||
@ -293,6 +326,48 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
assertEquals(0.6164363850373927, model.calculateAdjustedRSquared(), 1E-12);
|
||||
|
||||
checkVarianceConsistency(model);
|
||||
|
||||
// Estimate the model with no intercept
|
||||
model = new OLSMultipleLinearRegression();
|
||||
model.setNoIntercept(true);
|
||||
model.newSampleData(design, nobs, nvars);
|
||||
|
||||
// Check expected beta values from R
|
||||
betaHat = model.estimateRegressionParameters();
|
||||
TestUtils.assertEquals(betaHat,
|
||||
new double[]{0.52191832900513,
|
||||
2.36588087917963,
|
||||
-0.94770353802795,
|
||||
0.30851985863609}, 1E-12);
|
||||
|
||||
// Check expected residuals from R
|
||||
residuals = model.estimateResiduals();
|
||||
TestUtils.assertEquals(residuals, new double[]{
|
||||
44.138759883538249, 27.720705122356215, 35.873200836126799,
|
||||
34.574619581211977, 26.600168342080213, 15.074636243026923, -12.704904871199814,
|
||||
1.497443824078134, 2.691972687079431, 5.582798774291231, -4.422986561283165,
|
||||
-9.198581600334345, 4.481765170730647, 2.273520207553216, -22.649827853221336,
|
||||
-17.747900013943308, 20.298314638496436, 6.861405135329779, -8.684712790954924,
|
||||
-10.298639278062371, -9.896618896845819, 4.568568616351242, -15.313570491727944,
|
||||
-13.762961360873966, 7.156100301980509, 16.722282219843990, 26.716200609071898,
|
||||
-1.991466398777079, -2.523342564719335, 9.776486693095093, -5.297535127628603,
|
||||
-16.639070567471094, -10.302057295211819, -23.549487860816846, 1.506624392156384,
|
||||
-17.939174438345930, 13.105792202765040, -1.943329906928462, -1.516005841666695,
|
||||
-0.759066561832886, 20.793137744128977, -2.485236153005426, 27.588238710486976,
|
||||
2.658333257106881, -15.998337823623046, -5.550742066720694, -14.219077806826615},
|
||||
1E-12);
|
||||
|
||||
// Check standard errors from R
|
||||
errors = model.estimateRegressionParametersStandardErrors();
|
||||
TestUtils.assertEquals(new double[] {0.10470063765677, 0.41684100584290,
|
||||
0.43370143099691, 0.07694953606522}, errors, 1E-10);
|
||||
|
||||
// Check regression standard error against R
|
||||
assertEquals(17.24710630547, model.estimateRegressionStandardError(), 1E-10);
|
||||
|
||||
// Check R-Square statistics against R
|
||||
assertEquals(0.946350722085, model.calculateRSquared(), 1E-12);
|
||||
assertEquals(0.9413600915813, model.calculateAdjustedRSquared(), 1E-12);
|
||||
}
|
||||
|
||||
/**
|
||||
@ -415,6 +490,16 @@ public class OLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbs
|
||||
regression.newYSampleData(y);
|
||||
assertEquals(combinedX, regression.X);
|
||||
assertEquals(combinedY, regression.Y);
|
||||
|
||||
// No intercept
|
||||
regression.setNoIntercept(true);
|
||||
regression.newSampleData(y, x);
|
||||
combinedX = regression.X.copy();
|
||||
combinedY = regression.Y.copy();
|
||||
regression.newXSampleData(x);
|
||||
regression.newYSampleData(y);
|
||||
assertEquals(combinedX, regression.X);
|
||||
assertEquals(combinedY, regression.Y);
|
||||
}
|
||||
|
||||
@Test(expected=IllegalArgumentException.class)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user