(MATH-649) SimpleRegression needs the ability to suppress the intercept
This commit pushes changes to allow the estimation of the a regression in which the intercept is constrained to be zero. I am also pushing two unit tests. git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1167451 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Greg Sterijevski
parent
8929c05521
commit
88ced96cd0
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@ -84,13 +84,23 @@ public class SimpleRegression implements Serializable {
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/** mean of accumulated y values, used in updating formulas */
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private double ybar = 0;
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/** include an intercept or not */
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private final boolean hasIntercept;
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// ---------------------Public methods--------------------------------------
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/**
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* Create an empty SimpleRegression instance
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*/
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public SimpleRegression() {
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this(true);
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}
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/**
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* Secondary constructor which allows the user the ability to include/exclude const
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* @param includeIntercept boolean flag, true includes an intercept
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*/
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public SimpleRegression(boolean includeIntercept){
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super();
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hasIntercept = includeIntercept;
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}
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/**
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@ -106,22 +116,32 @@ public class SimpleRegression implements Serializable {
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* @param x independent variable value
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* @param y dependent variable value
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*/
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public void addData(double x, double y) {
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public void addData(final double x, final double y){
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if (n == 0) {
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xbar = x;
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ybar = y;
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} else {
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double dx = x - xbar;
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double dy = y - ybar;
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sumXX += dx * dx * (double) n / (n + 1d);
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sumYY += dy * dy * (double) n / (n + 1d);
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sumXY += dx * dy * (double) n / (n + 1d);
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xbar += dx / (n + 1.0);
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ybar += dy / (n + 1.0);
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if( hasIntercept ){
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final double fact1 = 1.0 + (double) n;
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final double fact2 = ((double) n) / (1.0 + (double) n);
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final double dx = x - xbar;
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final double dy = y - ybar;
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sumXX += dx * dx * fact2;
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sumYY += dy * dy * fact2;
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sumXY += dx * dy * fact2;
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xbar += dx / fact1;
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ybar += dy / fact1;
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}
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}
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if( !hasIntercept ){
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sumXX += x * x ;
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sumYY += y * y ;
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sumXY += x * y ;
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}
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sumX += x;
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sumY += y;
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n++;
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return;
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}
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@ -140,17 +160,29 @@ public class SimpleRegression implements Serializable {
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*/
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public void removeData(double x, double y) {
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if (n > 0) {
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double dx = x - xbar;
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double dy = y - ybar;
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sumXX -= dx * dx * (double) n / (n - 1d);
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sumYY -= dy * dy * (double) n / (n - 1d);
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sumXY -= dx * dy * (double) n / (n - 1d);
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xbar -= dx / (n - 1.0);
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ybar -= dy / (n - 1.0);
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sumX -= x;
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sumY -= y;
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n--;
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if (hasIntercept) {
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final double fact1 = (double) n - 1.0;
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final double fact2 = ((double) n) / ((double) n - 1.0);
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final double dx = x - xbar;
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final double dy = y - ybar;
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sumXX -= dx * dx * fact2;
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sumYY -= dy * dy * fact2;
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sumXY -= dx * dy * fact2;
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xbar -= dx / fact1;
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ybar -= dy / fact1;
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} else {
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final double fact1 = (double) n - 1.0;
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sumXX -= x * x;
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sumYY -= y * y;
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sumXY -= x * y;
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xbar -= x / fact1;
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ybar -= y / fact1;
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}
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sumX -= x;
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sumY -= y;
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n--;
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}
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return;
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}
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/**
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@ -235,7 +267,10 @@ public class SimpleRegression implements Serializable {
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*/
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public double predict(double x) {
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double b1 = getSlope();
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return getIntercept(b1) + b1 * x;
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if (hasIntercept) {
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return getIntercept(b1) + b1 * x;
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}
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return b1 * x;
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}
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/**
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@ -255,7 +290,16 @@ public class SimpleRegression implements Serializable {
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* @return the intercept of the regression line
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*/
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public double getIntercept() {
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return getIntercept(getSlope());
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return hasIntercept ? getIntercept(getSlope()) : 0.0;
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}
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/**
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* Returns true if a constant has been included false otherwise.
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*
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* @return true if constant exists, false otherwise
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*/
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public boolean hasIntercept(){
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return hasIntercept;
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}
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/**
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@ -391,7 +435,7 @@ public class SimpleRegression implements Serializable {
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if (n < 3) {
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return Double.NaN;
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}
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return getSumSquaredErrors() / (n - 2);
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return hasIntercept ? (getSumSquaredErrors() / (n - 2)) : (getSumSquaredErrors() / (n - 1));
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}
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/**
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@ -443,11 +487,15 @@ public class SimpleRegression implements Serializable {
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* <p>
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* If there are fewer that <strong>three</strong> observations in the
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* model, or if there is no variation in x, this returns
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* <code>Double.NaN</code>.</p>
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* <code>Double.NaN</code>.</p> Additionally, a <code>Double.NaN</code> is
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* returned when the intercept is constrained to be zero
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*
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* @return standard error associated with intercept estimate
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*/
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public double getInterceptStdErr() {
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if( !hasIntercept ){
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return Double.NaN;
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}
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return FastMath.sqrt(
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getMeanSquareError() * ((1d / (double) n) + (xbar * xbar) / sumXX));
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}
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@ -572,8 +620,11 @@ public class SimpleRegression implements Serializable {
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* @param slope current slope
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* @return the intercept of the regression line
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*/
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private double getIntercept(double slope) {
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private double getIntercept(double slope){
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if( hasIntercept){
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return (sumY - slope * sumX) / n;
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}
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return 0.0;
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}
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/**
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@ -80,6 +80,76 @@ public final class SimpleRegressionTest {
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{5, -1 }, {6, 12 }
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};
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/*
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* Data from NIST NOINT1
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*/
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private double[][] noint1 = {
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{130.0,60.0},
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{131.0,61.0},
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{132.0,62.0},
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{133.0,63.0},
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{134.0,64.0},
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{135.0,65.0},
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{136.0,66.0},
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{137.0,67.0},
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{138.0,68.0},
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{139.0,69.0},
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{140.0,70.0}
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};
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/*
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* Data from NIST NOINT2
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*
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*/
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private double[][] noint2 = {
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{3.0,4},
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{4,5},
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{4,6}
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};
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@Test
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public void testNoInterceot_noint2(){
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SimpleRegression regression = new SimpleRegression(false);
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regression.addData(noint2[0][1], noint2[0][0]);
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regression.addData(noint2[1][1], noint2[1][0]);
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regression.addData(noint2[2][1], noint2[2][0]);
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Assert.assertEquals("slope", 0.727272727272727,
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regression.getSlope(), 10E-12);
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Assert.assertEquals("slope std err", 0.420827318078432E-01,
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regression.getSlopeStdErr(),10E-12);
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Assert.assertEquals("number of observations", 3, regression.getN());
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Assert.assertEquals("r-square", 0.993348115299335,
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regression.getRSquare(), 10E-12);
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Assert.assertEquals("SSR", 40.7272727272727,
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regression.getRegressionSumSquares(), 10E-9);
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Assert.assertEquals("MSE", 0.136363636363636,
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regression.getMeanSquareError(), 10E-10);
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Assert.assertEquals("SSE", 0.272727272727273,
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regression.getSumSquaredErrors(),10E-9);
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}
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@Test
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public void testNoIntercept_noint1(){
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SimpleRegression regression = new SimpleRegression(false);
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for (int i = 0; i < noint1.length; i++) {
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regression.addData(noint1[i][1], noint1[i][0]);
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}
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Assert.assertEquals("slope", 2.07438016528926, regression.getSlope(), 10E-12);
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Assert.assertEquals("slope std err", 0.165289256198347E-01,
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regression.getSlopeStdErr(),10E-12);
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Assert.assertEquals("number of observations", 11, regression.getN());
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Assert.assertEquals("r-square", 0.999365492298663,
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regression.getRSquare(), 10E-12);
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Assert.assertEquals("SSR", 200457.727272727,
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regression.getRegressionSumSquares(), 10E-9);
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Assert.assertEquals("MSE", 12.7272727272727,
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regression.getMeanSquareError(), 10E-10);
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Assert.assertEquals("SSE", 127.272727272727,
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regression.getSumSquaredErrors(),10E-9);
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}
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@Test
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public void testNorris() {
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SimpleRegression regression = new SimpleRegression();
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