/***********************************************************************
This file is part of JavaNPST, a Java library of NonParametric
Statistical Tests.
Copyright (C) 2011
J. Derrac (jderrac@decsai.ugr.es)
S. Garcia (sglopez@ujaen.es)
F. Herrera (herrera@decsai.ugr.es)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
package javanpst.tests.twoSample.K_STest;
import java.util.Arrays;
import javanpst.data.structures.dataTable.DataTable;
import javanpst.distributions.tests.KolmogorovTwoSampleDistribution;
import javanpst.tests.StatisticalTest;
/**
* The Kolmogorov-Smirnov test for two samples
*
* This test can be applied to test the null hypothesis of equal distributions
* against the one side and two-side alternatives.
*
* The exact p-value is computed is the number of values per sample is equal or less
* than 40. In addition, an approximation to the normal distribution is computed.
*
* @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
* @version 1.0
*/
public class K_STest extends StatisticalTest{
/**
* Test distribution
*/
private KolmogorovTwoSampleDistribution distribution;
/**
* Data to analyze
*/
private DataTable data;
/**
* Quantiles of sample 1
*/
private double Sn [];
/**
* Quantiles of sample 2
*/
private double Sm [];
/**
* Values of the first sample
*/
private double Fn [];
/**
* Values of the second sample
*/
private double Fm [];
/**
* Test statistics
*/
private double Dn,DnPos,DnNeg;
/**
* Array with all the values of the combined sample
*/
private double combined[];
/**
* Left tail asymptotic p-value
*/
private double asymptoticLeftTail;
/**
* Right tail asymptotic p-value
*/
private double asymptoticRightTail;
/**
* Double tail asymptotic p-value
*/
private double asymptoticDoubleTail;
/**
* Left tail p-value
*/
private double exactLeftTail;
/**
* Right tail p-value
*/
private double exactRightTail;
/**
* Double tail p-value
*/
private double exactDoubleTail;
/**
* Default builder
*/
public K_STest(){
distribution = KolmogorovTwoSampleDistribution.getInstance();
setReportFormat();
clearData();
}//end-method
/**
* Clears the data stored in the test
*/
public void clearData(){
data=new DataTable();
performed=false;
dataReady=false;
Dn=0;
Sn=null;
Sm=null;
Fn=null;
Fm=null;
exactLeftTail=-1.0;
exactRightTail=-1.0;
exactDoubleTail=-1.0;
asymptoticLeftTail=-1.0;
asymptoticRightTail=-1.0;
asymptoticDoubleTail=-1.0;
}//end-method
/**
* Builder. Load data to test.
*
* @param newData data to test.
*/
public K_STest(DataTable newData){
distribution = KolmogorovTwoSampleDistribution.getInstance();
setReportFormat();
data=DataTable.newInstance(newData);
if(data.getColumns()!=2){
System.out.println("Kolmogorov-Smirnov test only can be employed with two samples");
clearData();
return;
}
Fn=new double [data.getRows()];
Fm=new double [data.getRows()];
combined= new double [data.getRows()+data.getRows()];
for(int i=0;i0){
System.out.println("No null values allowed in this test.");
clearData();
return;
}
}
Fn=new double [data.getRows()];
Fm=new double [data.getRows()];
combined= new double [data.getRows()+data.getRows()];
for(int i=0;i X) statistic
*
* @return Kolmogorov statistic
*/
public double getDnNeg(){
return DnNeg;
}//end-method
/**
* Get Kolmogorov righ tail statistic
*
* @return Kolmogorov statistic
*/
public double getDnPos(){
return DnPos;
}//end-method
/**
* Get left tail exact p-value
*
* @return left tail p-value computed
*/
public double getExactLeftPValue(){
return exactLeftTail;
}//end-method
/**
* Get right tail exact p-value
*
* @return right tail p-value computed
*/
public double getExactRightPValue(){
return exactRightTail;
}//end-method
/**
* Get double tail exact p-value
*
* @return double tail p-value computed
*/
public double getExactDoublePValue(){
return exactDoubleTail;
}//end-method
/**
* Get left tail p-value
*
* @return left tail p-value computed
*/
public double getLeftPValue(){
return asymptoticLeftTail;
}//end-method
/**
* Get right tail p-value
*
* @return right tail p-value computed
*/
public double getRightPValue(){
return asymptoticRightTail;
}//end-method
/**
* Get double tail p-value
*
* @return double tail p-value computed
*/
public double getDoublePValue(){
return asymptoticDoubleTail;
}//end-method
/**
* Prints the data stored in the test
*
* @return Data stored
*/
public String printData(){
String text="";
text+="\n"+data;
return text;
}//end-method
/**
* Prints a report with the results of the test
*
* @return Output report
*/
public String printReport(){
String report="";
if(!performed){
report+="The test has not been performed.\n";
return report;
}
report+="\n***************************************\n";
report+="Kolmogorov Two Sample test\n";
report+="***************************************\n\n";
report+="Dn+ statistic: "+nf6.format(DnPos)+"\n";
report+="Dn- statistic: "+nf6.format(DnNeg)+"\n";
report+="Dn statistic: "+nf6.format(Dn)+"\n\n";
if(Fn.length<9){
report+="Exact P-Value (Left tail, Y > X): "+nf6.format(exactLeftTail)+"\n";
report+="Exact P-Value (Right tail, Y < X): "+nf6.format(exactRightTail)+"\n";
report+="Exact P-Value (Double tail, Y != X): "+nf6.format(exactDoubleTail)+"\n\n";
}
else{
report+="Asymptotic P-Value (Left tail, Y > X): "+nf6.format(asymptoticLeftTail)+"\n";
report+="Asymptotic P-Value (Right tail, Y > X): "+nf6.format(asymptoticRightTail)+"\n";
report+="Asymptotic P-Value (Double tail, Y != X): "+nf6.format(asymptoticDoubleTail)+"\n\n";
}
return report;
}//end-method
}//end-class
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