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Posted to dev@commons.apache.org by to...@apache.org on 2003/06/11 05:33:05 UTC
cvs commit: jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat BivariateRegressionTest.java
tobrien 2003/06/10 20:33:05
Modified: math/src/java/org/apache/commons/math/stat
BivariateRegression.java
math/src/test/org/apache/commons/math/stat
BivariateRegressionTest.java
Log:
* Fixed all checkstyle errors and eliminated redundant NaN checks. Now have
100% test path coverage.
* Used distribution framework TDistribution to implement
getSlopeConfidenceInterval and getSignificance methods.
PR: Issue #20657
Obtained from: Bugzilla
Submitted by: Phil Steitz
Reviewed by: Tim O'Brien
Revision Changes Path
1.2 +217 -120 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/BivariateRegression.java
Index: BivariateRegression.java
===================================================================
RCS file: /home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/BivariateRegression.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -u -r1.1 -r1.2
--- BivariateRegression.java 29 May 2003 20:35:45 -0000 1.1
+++ BivariateRegression.java 11 Jun 2003 03:33:05 -0000 1.2
@@ -50,30 +50,33 @@
* individuals on behalf of the Apache Software Foundation. For more
* information on the Apache Software Foundation, please see
* <http://www.apache.org/>.
- *
*/
package org.apache.commons.math.stat;
+import org.apache.commons.math.stat.distribution.DistributionFactory;
+import org.apache.commons.math.stat.distribution.TDistribution;
/**
* Estimates an ordinary least squares regression model
- * with one independent variable: <p>
- *
- * y = intercept + slope * x </code><p>
- *
+ * with one independent variable.
+ * <p>
+ * <code> y = intercept + slope * x </code>
+ * <p>
* Standard errors for <code>intercept</code> and <code>slope</code> are
- * available as well as ANOVA, r-square and Pearson's r statistics.<p>
- *
+ * available as well as ANOVA, r-square and Pearson's r statistics.
+ * <p>
* Observations (x,y pairs) can be added to the model one at a time or they
* can be provided in a 2-dimensional array. The observations are not stored
* in memory, so there is no limit to the number of observations that can be
- * added to the model. <p>
- *
+ * added to the model.
+ * <p>
* <strong>Usage Notes</strong>: <ul>
* <li> When there are fewer than two observations in the model, or when
* there is no variation in the x values (i.e. all x values are the same)
* all statistics return <code>NaN</code>. At least two observations with
- * different x coordinates are requred to estimate a bivariate regression model.</li>
+ * different x coordinates are requred to estimate a bivariate regression
+ * model.
+ * </li>
* <li> getters for the statistics always compute values based on the current
* set of observations -- i.e., you can get statistics, then add more data
* and get updated statistics without using a new instance. There is no
@@ -114,31 +117,34 @@
*/
public void addData(double x, double y) {
sumX += x;
- sumSqX += x*x;
+ sumSqX += x * x;
sumY += y;
- sumSqY += y*y;
- sumXY += x*y;
+ sumSqY += y * y;
+ sumXY += x * y;
n++;
}
/**
- * Adds the observations represented by the elements in <code>data.</code><p>
+ * Adds the observations represented by the elements in
+ * <code>data</code>.
+ * <p>
* <code>(data[0][0],data[0][1])</code> will be the first observation, then
* <code>(data[1][0],data[1][1])</code>, etc. <p>
*
* This method does not replace data that has already been added.
- * To replace all data, use <code>clear()</code> before adding the new data.
+ * To replace all data, use <code>clear()</code> before adding the new
+ * data.
*
* @param data array of observations to be added
*/
public void addData(double[][] data) {
for (int i = 0; i < data.length; i++) {
- addData(data[i][0],data[i][1]);
+ addData(data[i][0], data[i][1]);
}
}
- /*
- * Clears all data from the model
+ /**
+ * Clears all data from the model.
*/
public void clear() {
sumX = 0d;
@@ -150,9 +156,9 @@
}
/**
- * Returns the number of observations that have been added to the model
+ * Returns the number of observations that have been added to the model.
*
- * @return n
+ * @return n number of observations that have been added.
*/
public long getN() {
return n;
@@ -160,37 +166,38 @@
/**
* Returns the "predicted" <code>y</code> value associated with the
- * supplied <code>x</code> value. Specifically, <p>
- *
- * <code> predict(x) = intercept + slope * x </code> <p>
- *
- * At least two observations (with at least two different x values)
+ * supplied <code>x</code> value.
+ * <p>
+ * <code> predict(x) = intercept + slope * x </code>
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @param x input <code>x</code> value
* @return predicted <code>y</code> value
*/
public double predict(double x) {
double b1 = getSlope();
- if (b1 == Double.NaN) {
- return b1;
- }
- return getIntercept(b1) + b1*x;
+ return getIntercept(b1) + b1 * x;
}
/**
* Returns the intercept of the estimated regression line.
- * The least squares estimate of the intercept is computed using the normal
- * equations, as described
- * <a href=http://www.xycoon.com/estimation4.htm>here</a>.
- * The intercept is sometimes denoted b0. <p>
- *
- * At least two distinct data pairs (with at least two different x values)
+ * <p>
+ * The least squares estimate of the intercept is computed using the
+ * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
+ * The intercept is sometimes denoted b0.
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @return the intercept of the regression line
*/
@@ -200,15 +207,17 @@
/**
* Returns the slope of the estimated regression line.
- * The least squares estimate of the slope is computed using the normal
- * equations, as described
- * <a href=http://www.xycoon.com/estimation4.htm>here</a>.
- * The slope is sometimes denoted b1. <p>
- *
- * At least two observations (with at least two different x values)
+ * <p>
+ * The least squares estimate of the slope is computed using the
+ * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>.
+ * The slope is sometimes denoted b1.
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @return the slope of the regression line
*/
@@ -217,22 +226,24 @@
return Double.NaN; //not enough data
}
double dn = (double) n;
- double denom = sumSqX - (sumX*sumX/dn);
- if (Math.abs(denom)< 10*Double.MIN_VALUE) {
+ double denom = sumSqX - (sumX * sumX / dn);
+ if (Math.abs(denom) < 10 * Double.MIN_VALUE) {
return Double.NaN; //not enough variation in x
}
- return (sumXY - (sumX*sumY/dn))/denom;
+ return (sumXY - (sumX * sumY / dn)) / denom;
}
/**
- * Returns the sum of squared errors</a> associated with the regression
- * model. This is defined as SSE
- * <a href=http://www.xycoon.com/SumOfSquares.htm>here</a>. <p>
- *
- * At least two distinct data pairs (with at least two different x values)
+ * Returns the <a href="http://www.xycoon.com/SumOfSquares.htm">
+ * sum of squared errors</a> (SSE) associated with the regression
+ * model.
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @return sum of squared errors associated with the regression model
*/
@@ -242,10 +253,11 @@
/**
* Returns the sum of squared deviations of the y values about their mean.
+ * <p>
* This is defined as SSTO
- * <a href=http://www.xycoon.com/SumOfSquares.htm>here</a>.
+ * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.
* <p>
- * If n < 2, this returns NaN.
+ * If <code>n < 2</code>, this returns <code>Double.NaN</code>.
*
* @return sum of squared deviations of y values
*/
@@ -253,36 +265,37 @@
if (n < 2) {
return Double.NaN;
}
- return sumSqY - sumY*sumY/(double) n;
+ return sumSqY - sumY * sumY / (double) n;
}
/**
* Returns the sum of squared deviations of the predicted y values about
* their mean (which equals the mean of y).
+ * <p>
* This is usually abbreviated SSR or SSM. It is defined as SSM
- * <a href=http://www.xycoon.com/SumOfSquares.htm>here</a><p>
- *
- * At least two distinct data pairs (with at least two different x values)
+ * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @return sum of squared deviations of y values
*/
public double getRegressionSumSquares() {
double b1 = getSlope();
- if (b1 == Double.NaN) {
- return b1;
- }
- return b1*(sumXY - sumX*sumY/(double) n);
+ return b1 * (sumXY - sumX * sumY / (double) n);
}
/**
- * Returns the sum of squared errors divided by the degrees of freedom.
- * This is usually abbreviated MSE. <p>
- *
+ * Returns the sum of squared errors divided by the degrees of freedom,
+ * usually abbreviated MSE.
+ * <p>
* If there are fewer than <strong>three</strong> data pairs in the model,
- * or if there is no variation in x, this returns <code>NaN</code>.
+ * or if there is no variation in <code>x</code>, this returns
+ * <code>Double.NaN</code>.
*
* @return sum of squared deviations of y values
*/
@@ -291,29 +304,25 @@
return Double.NaN;
}
double sse = getSumSquaredErrors();
- if (sse == Double.NaN) {
- return sse;
- }
- return sse/(double) (n - 2);
+ return sse / (double) (n - 2);
}
/**
- * Returns <a href=http://www.stt.msu.edu/~xiaoyimi/STT200/Lecture5.pdf>
- * Pearson's product moment correlation coefficient</a>.
- * This is usually denoted r. <p>
- *
- * At least two observations (with at least two different x values)
+ * Returns <a href="http://www.stt.msu.edu/~xiaoyimi/STT200/Lecture5.pdf">
+ * Pearson's product moment correlation coefficient</a>,
+ * usually denoted r.
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @return Pearson's r
*/
public double getR() {
double b1 = getSlope();
- if (b1 == Double.NaN) {
- return b1;
- }
double result = Math.sqrt(getRSquare(b1));
if (b1 < 0) {
result = -result;
@@ -322,14 +331,16 @@
}
/**
- * Returns the <a href=http://www.xycoon.com/coefficient1.htm> coefficient
- * of determination</a>.
- * This is usually denoted r-square. <p>
- *
- * At least two observaions (with at least two different x values)
+ * Returns the <a href="http://www.xycoon.com/coefficient1.htm">
+ * coefficient of determination</a>,
+ * usually denoted r-square.
+ * <p>
+ * <strong>Preconditions</strong>: <ul>
+ * <li>At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, <code>Double,NaN</code> is
* returned.
+ * </li></ul>
*
* @return r-square
*/
@@ -339,70 +350,150 @@
/**
- * Returns the <a href=http://www.xycoon.com/standarderrorb0.htm>standard
- * error of the intercept estimate</a>.
- * This is usually denoted s(b0). <p>
- *
- * If there are fewer that <strong>three</strong> observations in the model,
- * or if there is no variation in x, this returns <code>NaN</code>.
+ * Returns the <a href="http://www.xycoon.com/standarderrorb0.htm">
+ * standard error of the intercept estimate</a>,
+ * usually denoted s(b0).
+ * <p>
+ * If there are fewer that <strong>three</strong> observations in the
+ * model, or if there is no variation in x, this returns
+ * <code>Double.NaN</code>.
*
* @return standard error associated with intercept estimate
*/
public double getInterceptStdErr() {
double ssx = getSumSquaresX();
- if (ssx == Double.NaN) {
- return ssx;
- }
- return Math.sqrt(getMeanSquareError()*sumSqX/(((double) n)*ssx));
+ return Math.sqrt(getMeanSquareError() * sumSqX / (((double) n) * ssx));
}
/**
- * Returns the <a http://www.xycoon.com/standerrorb(1).htm>standard
- * error of the slope estimate</a>.
- * This is usually denoted s(b1). <p>
- *
+ * Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard
+ * error of the slope estimate</a>,
+ * usually denoted s(b1).
+ * <p>
* If there are fewer that <strong>three</strong> data pairs in the model,
- * or if there is no variation in x, this returns <code>NaN</code>.
+ * or if there is no variation in x, this returns <code>Double.NaN</code>.
*
* @return standard error associated with slope estimate
*/
public double getSlopeStdErr() {
double ssx = getSumSquaresX();
- if (ssx == Double.NaN) {
- return ssx;
+ return Math.sqrt(getMeanSquareError() / ssx);
+ }
+
+ /**
+ * Returns the half-width of a 95% confidence interval for the slope
+ * estimate.
+ * <p>
+ * The 95% confidence interval is
+ * <p>
+ * <code>(getSlope() - getSlopeConfidenceInterval(),
+ * getSlope() + getSlopeConfidenceInterval())</code>
+ * <p>
+ * If there are fewer that <strong>three</strong> observations in the
+ * model, or if there is no variation in x, this returns
+ * <code>Double.NaN</code>.
+ * <p>
+ * <strong>Usage Note</strong>:<br>
+ * The validity of this statistic depends on the assumption that the
+ * observations included in the model are drawn from a
+ * <a href="http://mathworld.wolfram.com/
+ * BivariateNormalDistribution.html">Bivariate Normal Distribution</a>.
+ *
+ * @return half-width of 95% confidence interval for the slope estimate
+ */
+ public double getSlopeConfidenceInterval() {
+ return getSlopeConfidenceInterval(0.05d);
+ }
+
+ /**
+ * Returns the half-width of a (100-100*alpha)% confidence interval for
+ * the slope estimate.
+ * <p>
+ * The (100-100*alpha)% confidence interval is
+ * <p>
+ * <code>(getSlope() - getSlopeConfidenceInterval(),
+ * getSlope() + getSlopeConfidenceInterval())</code>
+ * <p>
+ * To request, for example, a 99% confidence interval, use
+ * <code>alpha = .01</code>
+ * <p>
+ * <strong>Usage Note</strong>:<br>
+ * The validity of this statistic depends on the assumption that the
+ * observations included in the model are drawn from a
+ * <a href="http://mathworld.wolfram.com/
+ * BivariateNormalDistribution.html">Bivariate Normal Distribution</a>.
+ * <p>
+ * <strong> Preconditions:</strong><ul>
+ * <li>If there are fewer that <strong>three</strong> observations in the
+ * model, or if there is no variation in x, this returns
+ * <code>Double.NaN</code>.
+ * </li>
+ * <li><code>(0 < alpha < 1)</code>; otherwise an
+ * <code>IllegalArgumentException</code> is thrown.
+ * </li></ul>
+ *
+ * @param alpha the desired significance level
+ * @return half-width of 95% confidence interval for the slope estimate
+ */
+ public double getSlopeConfidenceInterval(double alpha) {
+ if (alpha >= 1 || alpha <= 0) {
+ throw new IllegalArgumentException();
}
- return Math.sqrt(getMeanSquareError()/ssx);
+ return getSlopeStdErr() *
+ getTDistribution().inverseCummulativeProbability(1d - alpha / 2d);
+ }
+
+ /**
+ * Returns the significance level of the slope (equiv) correlation.
+ * <p>
+ * Specifically, the returned value is the smallest <code>alpha</code>
+ * such that the slope confidence interval with significance level
+ * equal to <code>alpha</code> does not include <code>0</code>.
+ * On regression output, this is often denoted <code>Prob(|t| > 0)</code>
+ * <p>
+ * <strong>Usage Note</strong>:<br>
+ * The validity of this statistic depends on the assumption that the
+ * observations included in the model are drawn from a
+ * <a href="http://mathworld.wolfram.com/
+ * BivariateNormalDistribution.html">Bivariate Normal Distribution</a>.
+ * <p>
+ * If there are fewer that <strong>three</strong> observations in the
+ * model, or if there is no variation in x, this returns
+ * <code>Double.NaN</code>.
+ *
+ * @return significance level for slope/correlation
+ */
+ public double getSignificance() {
+ return (1d - getTDistribution().cummulativeProbability(
+ Math.abs(getSlope()) / getSlopeStdErr()));
}
// ---------------------Private methods-----------------------------------
/**
* Returns the intercept of the estimated regression line, given the slope.
+ * <p>
* Will return <code>NaN</code> if slope is <code>NaN</code>.
*
* @param slope current slope
* @return the intercept of the regression line
*/
private double getIntercept(double slope) {
- if (slope == Double.NaN) {
- return slope;
- }
- return (sumY - slope*sumX)/((double) n);
+ return (sumY - slope * sumX) / ((double) n);
}
/**
- * Returns the sum of squared errors</a> associated with the regression
- * model, using the slope of the regression line. Returns NaN if the slope
- * is NaN.
- *
+ * Returns the sum of squared errors associated with the regression
+ * model, using the slope of the regression line.
+ * <p>
+ * Returns NaN if the slope is NaN.
+ *
+ * @param b1 current slope
* @return sum of squared errors associated with the regression model
*/
private double getSumSquaredErrors(double b1) {
- if (b1 == Double.NaN) {
- return b1;
- }
double b0 = getIntercept(b1);
- return sumSqY - b0*sumY - b1*sumXY;
+ return sumSqY - b0 * sumY - b1 * sumXY;
}
/**
@@ -416,24 +507,30 @@
if (n < 2) {
return Double.NaN;
}
- return sumSqX - sumX*sumX/(double) n;
+ return sumSqX - sumX * sumX / (double) n;
}
/**
* Computes r-square from the slope.
- * will return NaN if slope is Nan
+ * <p>
+ * will return NaN if slope is Nan.
*
+ * @param b1 current slope
* @return r-square
*/
private double getRSquare(double b1) {
- if (b1 == Double.NaN) {
- return b1;
- }
double ssto = getTotalSumSquares();
- if (ssto == Double.NaN) {
- return ssto;
- }
- return (ssto - getSumSquaredErrors(b1))/ssto;
+ return (ssto - getSumSquaredErrors(b1)) / ssto;
+ }
+
+ /**
+ * Uses distribution framework to get a t distribution instance
+ * with df = n - 2
+ *
+ * @return t distribution with df = n - 2
+ */
+ private TDistribution getTDistribution() {
+ return DistributionFactory.newInstance().createTDistribution(n - 2);
}
}
1.2 +40 -2 jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/BivariateRegressionTest.java
Index: BivariateRegressionTest.java
===================================================================
RCS file: /home/cvs/jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/BivariateRegressionTest.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -u -r1.1 -r1.2
--- BivariateRegressionTest.java 29 May 2003 20:35:46 -0000 1.1
+++ BivariateRegressionTest.java 11 Jun 2003 03:33:05 -0000 1.2
@@ -87,6 +87,18 @@
{90.6,111.6},{86.5,122.2},{89.7,117.6},{90.6,121.1},{82.8,136.0},
{70.1,154.2},{65.4,153.6},{61.3,158.5},{62.5,140.6},{63.6,136.2},
{52.6,168.0},{59.7,154.3},{59.5,149.0},{61.3,165.5}};
+
+ /*
+ * From Moore and Mcabe, "Introduction to the Practice of Statistics"
+ * Example 10.3
+ */
+ private double[][] infData = {{15.6,5.2},{26.8,6.1},{37.8,8.7},{36.4,8.5},
+ {35.5,8.8},{18.6,4.9},{15.3,4.5},{7.9,2.5},{0.0,1.1}};
+
+ /*
+ * From http://www.xycoon.com/simple_linear_regression.htm
+ */
+ private double[][] infData2 = {{1,3},{2,5},{3,7},{4,14},{5,11}};
public BivariateRegressionTest(String name) {
super(name);
@@ -221,6 +233,32 @@
regression.addData(data);
assertEquals("number of observations",53,regression.getN());
}
-
+
+ public void testInference() {
+ BivariateRegression regression = new BivariateRegression();
+ regression.addData(infData);
+ assertEquals("slope confidence interval", 0.0271,
+ regression.getSlopeConfidenceInterval(),0.0001);
+ assertEquals("slope std err",0.01146,
+ regression.getSlopeStdErr(),0.0001);
+
+ regression = new BivariateRegression();
+ regression.addData(infData2);
+ assertEquals("significance", 0.023331,
+ regression.getSignificance(),0.0001);
+
+ //FIXME: get a real example to test against with alpha = .01
+ assertTrue("tighter means wider",
+ regression.getSlopeConfidenceInterval() <
+ regression.getSlopeConfidenceInterval(0.01));
+
+ try {
+ double x = regression.getSlopeConfidenceInterval(1);
+ fail("expecting IllegalArgumentException for alpha = 1");
+ } catch (IllegalArgumentException ex) {
+ ;
+ }
+
+ }
}
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