An implementation of ordinary least squares regression with one independent
variable. The implementation uses running sums and does not require the data to be stored in memory. Since I could not conceive of any significantly different implementation strategies that did not amount to just improving efficiency or numerical accuracy of what I am submitting, I did not abstract the interface. The test cases validate the computations against NIST reference data and verified computations. The slope, intercept, their standard errors and r-square estimates are accurate to within 10E-12 against the reference data set. MSE and other ANOVA stats are good at least to within 10E-8. -- Phil S. PR: Issue #20224 Obtained from: Bugzilla Submitted by: Phil Steitz Reviewed by: Tim O'Brien git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140858 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Tim O'Brien
parent
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/* ====================================================================
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* The Apache Software License, Version 1.1
|
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*
|
||||
* Copyright (c) 2003 The Apache Software Foundation. All rights
|
||||
* reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright
|
||||
* notice, this list of conditions and the following disclaimer in
|
||||
* the documentation and/or other materials provided with the
|
||||
* distribution.
|
||||
*
|
||||
* 3. The end-user documentation included with the redistribution, if
|
||||
* any, must include the following acknowlegement:
|
||||
* "This product includes software developed by the
|
||||
* Apache Software Foundation (http://www.apache.org/)."
|
||||
* Alternately, this acknowlegement may appear in the software itself,
|
||||
* if and wherever such third-party acknowlegements normally appear.
|
||||
*
|
||||
* 4. The names "The Jakarta Project", "Commons", and "Apache Software
|
||||
* Foundation" must not be used to endorse or promote products derived
|
||||
* from this software without prior written permission. For written
|
||||
* permission, please contact apache@apache.org.
|
||||
*
|
||||
* 5. Products derived from this software may not be called "Apache"
|
||||
* nor may "Apache" appear in their names without prior written
|
||||
* permission of the Apache Software Foundation.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
|
||||
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
|
||||
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR
|
||||
* ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
|
||||
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
|
||||
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
|
||||
* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
|
||||
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
|
||||
* OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
|
||||
* SUCH DAMAGE.
|
||||
* ====================================================================
|
||||
*
|
||||
* This software consists of voluntary contributions made by many
|
||||
* individuals on behalf of the Apache Software Foundation. For more
|
||||
* information on the Apache Software Foundation, please see
|
||||
* <http://www.apache.org/>.
|
||||
*
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*/
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package org.apache.commons.math;
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/**
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* Estimates an ordinary least squares regression model
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* with one independent variable: <p>
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*
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* y = intercept + slope * x </code><p>
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*
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* Standard errors for <code>intercept</code> and <code>slope</code> are
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* available as well as ANOVA, r-square and Pearson's r statistics.<p>
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*
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* Observations (x,y pairs) can be added to the model one at a time or they
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* can be provided in a 2-dimensional array. The observations are not stored
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* in memory, so there is no limit to the number of observations that can be
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* added to the model. <p>
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*
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* <strong>Usage Notes</strong>: <ul>
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* <li> When there are fewer than two observations in the model, or when
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* there is no variation in the x values (i.e. all x values are the same)
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* all statistics return <code>NaN</code>. At least two observations with
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* different x coordinates are requred to estimate a bivariate regression model.</li>
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* <li> getters for the statistics always compute values based on the current
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* set of observations -- i.e., you can get statistics, then add more data
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* and get updated statistics without using a new instance. There is no
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* "compute" method that updates all statistics. Each of the getters performs
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* the necessary computations to return the requested statistic.</li>
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* </ul>
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*
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* @author Phil Steitz
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* @version $Revision: 1.1 $ $Date: 2003/05/26 02:11:50 $
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*/
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public class BivariateRegression {
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/** sum of x values */
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private double sumX = 0d;
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/** sum of squared x values */
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private double sumSqX = 0d;
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/** sum of y values */
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private double sumY = 0d;
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/** sum of squared y values */
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private double sumSqY = 0d;
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/** sum of products */
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private double sumXY = 0d;
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/** number of observations */
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private long n = 0;
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// ---------------------Public methods--------------------------------------
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/**
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* Adds the observation (x,y) to the regression data set
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*
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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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sumX += x;
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sumSqX += x*x;
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sumY += y;
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sumSqY += y*y;
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sumXY += x*y;
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n++;
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}
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/**
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* Adds the observations represented by the elements in <code>data.</code><p>
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* <code>(data[0][0],data[0][1])</code> will be the first observation, then
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* <code>(data[1][0],data[1][1])</code>, etc. <p>
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*
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* This method does not replace data that has already been added.
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* To replace all data, use <code>clear()</code> before adding the new data.
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*
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* @param data array of observations to be added
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*/
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public void addData(double[][] data) {
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for (int i = 0; i < data.length; i++) {
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addData(data[i][0],data[i][1]);
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}
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}
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/*
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* Clears all data from the model
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*/
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public void clear() {
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sumX = 0d;
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sumSqX = 0d;
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sumY = 0d;
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sumSqY = 0d;
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sumXY = 0d;
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n = 0;
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}
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/**
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* Returns the number of observations that have been added to the model
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*
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* @return n
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*/
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public long getN() {
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return n;
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}
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/**
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* Returns the "predicted" <code>y</code> value associated with the
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* supplied <code>x</code> value. Specifically, <p>
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*
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* <code> predict(x) = intercept + slope * x </code> <p>
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*
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* At least two observations (with at least two different x values)
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* must have been added before invoking this method. If this method is
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* invoked before a model can be estimated, <code>Double,NaN</code> is
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* returned.
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*
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* @param x input <code>x</code> value
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* @return predicted <code>y</code> value
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*/
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public double predict(double x) {
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double b1 = getSlope();
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if (b1 == Double.NaN) {
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return b1;
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}
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return getIntercept(b1) + b1*x;
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}
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/**
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* Returns the intercept of the estimated regression line.
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* The least squares estimate of the intercept is computed using the normal
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* equations, as described
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* <a href=http://www.xycoon.com/estimation4.htm>here</a>.
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* The intercept is sometimes denoted b0. <p>
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*
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* At least two distinct data pairs (with at least two different x values)
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* must have been added before invoking this method. If this method is
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* invoked before a model can be estimated, <code>Double,NaN</code> is
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* returned.
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*
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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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}
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/**
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* Returns the slope of the estimated regression line.
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* The least squares estimate of the slope is computed using the normal
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* equations, as described
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* <a href=http://www.xycoon.com/estimation4.htm>here</a>.
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* The slope is sometimes denoted b1. <p>
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*
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* At least two observations (with at least two different x values)
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* must have been added before invoking this method. If this method is
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* invoked before a model can be estimated, <code>Double,NaN</code> is
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* returned.
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*
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* @return the slope of the regression line
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*/
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public double getSlope() {
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if (n < 2) {
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return Double.NaN; //not enough data
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}
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double dn = (double) n;
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double denom = sumSqX - (sumX*sumX/dn);
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if (Math.abs(denom)< 10*Double.MIN_VALUE) {
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return Double.NaN; //not enough variation in x
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}
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return (sumXY - (sumX*sumY/dn))/denom;
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}
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/**
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* Returns the sum of squared errors</a> associated with the regression
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* model. This is defined as SSE
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* <a href=http://www.xycoon.com/SumOfSquares.htm>here</a>. <p>
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*
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* At least two distinct data pairs (with at least two different x values)
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* must have been added before invoking this method. If this method is
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* invoked before a model can be estimated, <code>Double,NaN</code> is
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* returned.
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*
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* @return sum of squared errors associated with the regression model
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*/
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public double getSumSquaredErrors() {
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return getSumSquaredErrors(getSlope());
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}
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/**
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* Returns the sum of squared deviations of the y values about their mean.
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* This is defined as SSTO
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* <a href=http://www.xycoon.com/SumOfSquares.htm>here</a>.
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* <p>
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* If n < 2, this returns NaN.
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*
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* @return sum of squared deviations of y values
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*/
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public double getTotalSumSquares() {
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if (n < 2) {
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return Double.NaN;
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}
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return sumSqY - sumY*sumY/(double) n;
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}
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/**
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* Returns the sum of squared deviations of the predicted y values about
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* their mean (which equals the mean of y).
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* This is usually abbreviated SSR or SSM. It is defined as SSM
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* <a href=http://www.xycoon.com/SumOfSquares.htm>here</a><p>
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*
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* At least two distinct data pairs (with at least two different x values)
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* must have been added before invoking this method. If this method is
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* invoked before a model can be estimated, <code>Double,NaN</code> is
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* returned.
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*
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* @return sum of squared deviations of y values
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*/
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public double getRegressionSumSquares() {
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double b1 = getSlope();
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if (b1 == Double.NaN) {
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return b1;
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}
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return b1*(sumXY - sumX*sumY/(double) n);
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}
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/**
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* Returns the sum of squared errors divided by the degrees of freedom.
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* This is usually abbreviated MSE. <p>
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*
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* If there are fewer than <strong>three</strong> data pairs in the model,
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* or if there is no variation in x, this returns <code>NaN</code>.
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*
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* @return sum of squared deviations of y values
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*/
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public double getMeanSquareError() {
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if (n < 3) {
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return Double.NaN;
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}
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double sse = getSumSquaredErrors();
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if (sse == Double.NaN) {
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return sse;
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}
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return sse/(double) (n - 2);
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}
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/**
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* Returns <a href=http://www.stt.msu.edu/~xiaoyimi/STT200/Lecture5.pdf>
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* Pearson's product moment correlation coefficient</a>.
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* This is usually denoted r. <p>
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*
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* At least two observations (with at least two different x values)
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* must have been added before invoking this method. If this method is
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* invoked before a model can be estimated, <code>Double,NaN</code> is
|
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* returned.
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*
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* @return Pearson's r
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*/
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public double getR() {
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double b1 = getSlope();
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if (b1 == Double.NaN) {
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return b1;
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}
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double result = Math.sqrt(getRSquare(b1));
|
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if (b1 < 0) {
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result = -result;
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}
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return result;
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}
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/**
|
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* Returns the <a href=http://www.xycoon.com/coefficient1.htm> coefficient
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* of determination</a>.
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* This is usually denoted r-square. <p>
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*
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* At least two observaions (with at least two different x values)
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* must have been added before invoking this method. If this method is
|
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* invoked before a model can be estimated, <code>Double,NaN</code> is
|
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* returned.
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*
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* @return r-square
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*/
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public double getRSquare() {
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return getRSquare(getSlope());
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}
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/**
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* Returns the <a href=http://www.xycoon.com/standarderrorb0.htm>standard
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* error of the intercept estimate</a>.
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* This is usually denoted s(b0). <p>
|
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*
|
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* If there are fewer that <strong>three</strong> observations in the model,
|
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* or if there is no variation in x, this returns <code>NaN</code>.
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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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double ssx = getSumSquaresX();
|
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if (ssx == Double.NaN) {
|
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return ssx;
|
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}
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return Math.sqrt(getMeanSquareError()*sumSqX/(((double) n)*ssx));
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}
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/**
|
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* Returns the <a http://www.xycoon.com/standerrorb(1).htm>standard
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* error of the slope estimate</a>.
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* This is usually denoted s(b1). <p>
|
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*
|
||||
* If there are fewer that <strong>three</strong> data pairs in the model,
|
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* or if there is no variation in x, this returns <code>NaN</code>.
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*
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* @return standard error associated with slope estimate
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*/
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public double getSlopeStdErr() {
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double ssx = getSumSquaresX();
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if (ssx == Double.NaN) {
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return ssx;
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}
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return Math.sqrt(getMeanSquareError()/ssx);
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}
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// ---------------------Private methods-----------------------------------
|
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/**
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* Returns the intercept of the estimated regression line, given the slope.
|
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* Will return <code>NaN</code> if slope is <code>NaN</code>.
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*
|
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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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if (slope == Double.NaN) {
|
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return slope;
|
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}
|
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return (sumY - slope*sumX)/((double) n);
|
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}
|
||||
|
||||
/**
|
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* Returns the sum of squared errors</a> associated with the regression
|
||||
* model, using the slope of the regression line. Returns NaN if the slope
|
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* is NaN.
|
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*
|
||||
* @return sum of squared errors associated with the regression model
|
||||
*/
|
||||
private double getSumSquaredErrors(double b1) {
|
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if (b1 == Double.NaN) {
|
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return b1;
|
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}
|
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double b0 = getIntercept(b1);
|
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return sumSqY - b0*sumY - b1*sumXY;
|
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}
|
||||
|
||||
/**
|
||||
* Returns the sum of squared deviations of the x values about their mean.
|
||||
* <p>
|
||||
* If n < 2, this returns NaN.
|
||||
*
|
||||
* @return sum of squared deviations of x values
|
||||
*/
|
||||
private double getSumSquaresX() {
|
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if (n < 2) {
|
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return Double.NaN;
|
||||
}
|
||||
return sumSqX - sumX*sumX/(double) n;
|
||||
}
|
||||
|
||||
/**
|
||||
* Computes r-square from the slope.
|
||||
* will return NaN if slope is Nan
|
||||
*
|
||||
* @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;
|
||||
}
|
||||
}
|
||||
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|
|
@ -0,0 +1,226 @@
|
|||
/* ====================================================================
|
||||
* The Apache Software License, Version 1.1
|
||||
*
|
||||
* Copyright (c) 2003 The Apache Software Foundation. All rights
|
||||
* reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright
|
||||
* notice, this list of conditions and the following disclaimer in
|
||||
* the documentation and/or other materials provided with the
|
||||
* distribution.
|
||||
*
|
||||
* 3. The end-user documentation included with the redistribution, if
|
||||
* any, must include the following acknowlegement:
|
||||
* "This product includes software developed by the
|
||||
* Apache Software Foundation (http://www.apache.org/)."
|
||||
* Alternately, this acknowlegement may appear in the software itself,
|
||||
* if and wherever such third-party acknowlegements normally appear.
|
||||
*
|
||||
* 4. The names "The Jakarta Project", "Commons", and "Apache Software
|
||||
* Foundation" must not be used to endorse or promote products derived
|
||||
* from this software without prior written permission. For written
|
||||
* permission, please contact apache@apache.org.
|
||||
*
|
||||
* 5. Products derived from this software may not be called "Apache"
|
||||
* nor may "Apache" appear in their names without prior written
|
||||
* permission of the Apache Software Foundation.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
|
||||
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
|
||||
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
* DISCLAIMED. IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR
|
||||
* ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
|
||||
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
|
||||
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
|
||||
* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
|
||||
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
|
||||
* OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
|
||||
* SUCH DAMAGE.
|
||||
* ====================================================================
|
||||
*
|
||||
* This software consists of voluntary contributions made by many
|
||||
* 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;
|
||||
|
||||
import junit.framework.Test;
|
||||
import junit.framework.TestCase;
|
||||
import junit.framework.TestSuite;
|
||||
/**
|
||||
* Test cases for the TestStatistic class.
|
||||
*
|
||||
* @author Phil Steitz
|
||||
* @version $Revision: 1.1 $ $Date: 2003/05/26 02:11:50 $
|
||||
*/
|
||||
|
||||
public final class BivariateRegressionTest extends TestCase {
|
||||
|
||||
/*
|
||||
* NIST "Norris" refernce data set from
|
||||
* http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Norris.dat
|
||||
* Strangely, order is {y,x}
|
||||
*/
|
||||
private double[][] data = {{0.1,0.2},{338.8,337.4},{118.1,118.2},
|
||||
{888.0,884.6},{9.2,10.1},{228.1,226.5},{668.5,666.3},{998.5,996.3},
|
||||
{449.1,448.6},{778.9,777.0},{559.2,558.2},{0.3,0.4},{0.1,0.6},
|
||||
{778.1,775.5},{668.8,666.9},{339.3,338.0},{448.9,447.5},{10.8,11.6},
|
||||
{557.7,556.0},{228.3,228.1},{998.0,995.8},{888.8,887.6},{119.6,120.2},
|
||||
{0.3,0.3},{0.6,0.3},{557.6,556.8},{339.3,339.1},{888.0,887.2},
|
||||
{998.5,999.0},{778.9,779.0},{10.2,11.1},{117.6,118.3},{228.9,229.2},
|
||||
{668.4,669.1},{449.2,448.9},{0.2,0.5}};
|
||||
|
||||
/*
|
||||
* Correlation example from
|
||||
* http://www.xycoon.com/correlation.htm
|
||||
*/
|
||||
private double[][] corrData = {{101.0,99.2},{100.1,99.0},{100.0,100.0},
|
||||
{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}};
|
||||
|
||||
public BivariateRegressionTest(String name) {
|
||||
super(name);
|
||||
}
|
||||
|
||||
public void setUp() {
|
||||
}
|
||||
|
||||
public static Test suite() {
|
||||
TestSuite suite = new TestSuite(BivariateRegressionTest.class);
|
||||
suite.setName("BivariateRegression Tests");
|
||||
return suite;
|
||||
}
|
||||
|
||||
public void testNorris() {
|
||||
BivariateRegression regression = new BivariateRegression();
|
||||
for (int i = 0; i < data.length; i++) {
|
||||
regression.addData(data[i][1],data[i][0]);
|
||||
}
|
||||
assertEquals("slope",1.00211681802045,
|
||||
regression.getSlope(),10E-12);
|
||||
assertEquals("slope std err",0.429796848199937E-03,
|
||||
regression.getSlopeStdErr(),10E-12);
|
||||
assertEquals("number of observations",36,regression.getN());
|
||||
assertEquals("intercept", -0.262323073774029,
|
||||
regression.getIntercept(),10E-12);
|
||||
assertEquals("std err intercept", 0.232818234301152,
|
||||
regression.getInterceptStdErr(),10E-12);
|
||||
assertEquals("r-square",0.999993745883712,
|
||||
regression.getRSquare(),10E-12);
|
||||
assertEquals("SSR",4255954.13232369,
|
||||
regression.getRegressionSumSquares(),10E-8);
|
||||
assertEquals("MSE",0.782864662630069,
|
||||
regression.getMeanSquareError(),10E-8);
|
||||
assertEquals("SSE",26.6173985294224,
|
||||
regression.getSumSquaredErrors(),10E-8);
|
||||
assertEquals("predict(0)",-0.262323073774029,
|
||||
regression.predict(0),10E-12);
|
||||
assertEquals("predict(1)",1.00211681802045-0.262323073774029,
|
||||
regression.predict(1),10E-11);
|
||||
}
|
||||
|
||||
public void testCorr() {
|
||||
BivariateRegression regression = new BivariateRegression();
|
||||
regression.addData(corrData);
|
||||
assertEquals("number of observations",17,regression.getN());
|
||||
assertEquals("r-square",.896123,
|
||||
regression.getRSquare(),10E-6);
|
||||
assertEquals("r",-.946638,
|
||||
regression.getR(),10E-6);
|
||||
}
|
||||
|
||||
public void testNaNs() {
|
||||
|
||||
BivariateRegression regression = new BivariateRegression();
|
||||
|
||||
assertTrue("intercept not NaN",Double.isNaN(regression.getIntercept()));
|
||||
assertTrue("slope not NaN",Double.isNaN(regression.getSlope()));
|
||||
assertTrue("slope std err not NaN",
|
||||
Double.isNaN(regression.getSlopeStdErr()));
|
||||
assertTrue("intercept std err not NaN",
|
||||
Double.isNaN(regression.getInterceptStdErr()));
|
||||
assertTrue("MSE not NaN",Double.isNaN(regression.getMeanSquareError()));
|
||||
assertTrue("e not NaN",Double.isNaN(regression.getR()));
|
||||
assertTrue("r-square not NaN",Double.isNaN(regression.getRSquare()));
|
||||
assertTrue("RSS not NaN",
|
||||
Double.isNaN(regression.getRegressionSumSquares()));
|
||||
assertTrue("SSE not NaN",Double.isNaN(regression.getSumSquaredErrors()));
|
||||
assertTrue("SSTO not NaN",Double.isNaN(regression.getTotalSumSquares()));
|
||||
assertTrue("predict not NaN",Double.isNaN(regression.predict(0)));
|
||||
|
||||
regression.addData(1,2);
|
||||
regression.addData(1,3);
|
||||
|
||||
// No x variation, so these should still blow...
|
||||
assertTrue("intercept not NaN",Double.isNaN(regression.getIntercept()));
|
||||
assertTrue("slope not NaN",Double.isNaN(regression.getSlope()));
|
||||
assertTrue("slope std err not NaN",
|
||||
Double.isNaN(regression.getSlopeStdErr()));
|
||||
assertTrue("intercept std err not NaN",
|
||||
Double.isNaN(regression.getInterceptStdErr()));
|
||||
assertTrue("MSE not NaN",Double.isNaN(regression.getMeanSquareError()));
|
||||
assertTrue("e not NaN",Double.isNaN(regression.getR()));
|
||||
assertTrue("r-square not NaN",Double.isNaN(regression.getRSquare()));
|
||||
assertTrue("RSS not NaN",
|
||||
Double.isNaN(regression.getRegressionSumSquares()));
|
||||
assertTrue("SSE not NaN",Double.isNaN(regression.getSumSquaredErrors()));
|
||||
assertTrue("predict not NaN",Double.isNaN(regression.predict(0)));
|
||||
|
||||
// but SSTO should be OK
|
||||
assertTrue("SSTO NaN",!Double.isNaN(regression.getTotalSumSquares()));
|
||||
|
||||
regression = new BivariateRegression();
|
||||
|
||||
regression.addData(1,2);
|
||||
regression.addData(3,3);
|
||||
|
||||
// All should be OK except MSE, s(b0), s(b1) which need one more df
|
||||
assertTrue("interceptNaN",!Double.isNaN(regression.getIntercept()));
|
||||
assertTrue("slope NaN",!Double.isNaN(regression.getSlope()));
|
||||
assertTrue("slope std err not NaN",
|
||||
Double.isNaN(regression.getSlopeStdErr()));
|
||||
assertTrue("intercept std err not NaN",
|
||||
Double.isNaN(regression.getInterceptStdErr()));
|
||||
assertTrue("MSE not NaN",Double.isNaN(regression.getMeanSquareError()));
|
||||
assertTrue("r NaN",!Double.isNaN(regression.getR()));
|
||||
assertTrue("r-square NaN",!Double.isNaN(regression.getRSquare()));
|
||||
assertTrue("RSS NaN",
|
||||
!Double.isNaN(regression.getRegressionSumSquares()));
|
||||
assertTrue("SSE NaN",!Double.isNaN(regression.getSumSquaredErrors()));
|
||||
assertTrue("SSTO NaN",!Double.isNaN(regression.getTotalSumSquares()));
|
||||
assertTrue("predict NaN",!Double.isNaN(regression.predict(0)));
|
||||
|
||||
regression.addData(1,4);
|
||||
|
||||
// MSE, MSE, s(b0), s(b1) should all be OK now
|
||||
assertTrue("MSE NaN",!Double.isNaN(regression.getMeanSquareError()));
|
||||
assertTrue("slope std err NaN",
|
||||
!Double.isNaN(regression.getSlopeStdErr()));
|
||||
assertTrue("intercept std err NaN",
|
||||
!Double.isNaN(regression.getInterceptStdErr()));
|
||||
}
|
||||
|
||||
public void testClear() {
|
||||
BivariateRegression regression = new BivariateRegression();
|
||||
regression.addData(corrData);
|
||||
assertEquals("number of observations",17,regression.getN());
|
||||
regression.clear();
|
||||
assertEquals("number of observations",0,regression.getN());
|
||||
regression.addData(corrData);
|
||||
assertEquals("r-square",.896123,regression.getRSquare(),10E-6);
|
||||
regression.addData(data);
|
||||
assertEquals("number of observations",53,regression.getN());
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue