/***********************************************************************
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.oneSample.wilcoxonTest;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import javanpst.data.structures.dataTable.DataTable;
import javanpst.distributions.DistributionDefinitions;
import javanpst.distributions.tests.WilcoxonDistribution;
import javanpst.tests.StatisticalTest;
/**
* The Wilcoxon Signed-Ranks test
*
* This test is proposed for use with paired-sample data
* in making inferences concerning the value of the median
* of the population of differences.
*
* Zero differences are shared between both samples. If there
* is a odd number of them, one is discarded. Ties are handled
* by the midrank method.
*
* An exact p-value (for p-values equal or lower than 0.2, and
* sample size equal or lower than 50) is provided. An
* asymptotic approximation to the normal distribution is
* also provided.
*
* Furthermore, this version of the test also provides two
* confidence intervals (at 0.90 and 0.95 levels) for the
* location of the median of the differences, through the use of
* Walsh averages.
*
* @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
* @version 1.0
*/
public class WilcoxonTest extends StatisticalTest{
/**
* Distribution of the test
*/
private WilcoxonDistribution distribution;
/**
* Data to analyze
*/
private DataTable data;
/**
* Wilcoxon ranks
*/
private double rPlus, rMinus;
/**
* Left tail p-value
*/
private double exactLeftTail;
/**
* Right tail p-value
*/
private double exactRightTail;
/**
* Double tail p-value
*/
private double exactDoubleTail;
/**
* Left tail asymptotic p-value
*/
private double asymptoticLeftTail;
/**
* Right tail asymptotic p-value
*/
private double asymptoticRightTail;
/**
* Double tail asymptotic p-value
*/
private double asymptoticDoubleTail;
/**
* Upper limit of confidence intervals
*/
double upper90, upper95;
/**
* Upper limit of confidence intervals
*/
double lower90, lower95;
/**
* Exact confidence
*/
double exactConfidence90,exactConfidence95;
/**
* Default builder
*/
public WilcoxonTest(){
distribution=WilcoxonDistribution.getInstance();
setReportFormat();
clearData();
}//end-method
/**
* Clears the data stored in the test
*/
public void clearData(){
rPlus=0.0;
rMinus=0.0;
upper90=0.0;
upper95=0.0;
lower90=0.0;
lower95=0.0;
exactLeftTail=-1.0;
exactRightTail=-1.0;
exactDoubleTail=-1.0;
asymptoticLeftTail=-1.0;
asymptoticRightTail=-1.0;
asymptoticDoubleTail=-1.0;
data = new DataTable();
dataReady=false;
performed=false;
}//end-method
/**
* Builder. Load data to test.
*
* @param newData data to test.
*/
public WilcoxonTest(DataTable newData){
distribution=WilcoxonDistribution.getInstance();
setReportFormat();
data=DataTable.newInstance(newData);
if(data.getColumns()!=2){
System.out.println("Wilcoxon test only can be employed with two samples");
clearData();
return;
}
for(int i=0;i0){
System.out.println("No null values allowed in this test.");
clearData();
return;
}
}
dataReady=true;
performed=false;
}//end-method
/**
* Load data to test.
*
* @param newData data to test.
*/
public void setData(DataTable newData){
data=DataTable.newInstance(newData);
if(data.getColumns()!=2){
System.out.println("Wilcoxon test only can be employed with two samples");
clearData();
return;
}
for(int i=0;i0){
System.out.println("No null values allowed in this test.");
clearData();
return;
}
}
dataReady=true;
performed=false;
}//end-method
/**
* Performs the test
*/
public void doTest(){
double AOld [],A [];
double BOld [],B [];
double diffOld [], diff [];
int zeroDifferences,N,pointer;
boolean sign[];
double ranks[];
ArrayList walsh;
double tiesWeight;
int criticalN;
if(!dataReady){
System.out.println("Data is not ready");
return;
}
AOld=new double[data.getRows()];
BOld=new double[data.getRows()];
diffOld=new double[data.getRows()];
zeroDifferences=0;
tiesWeight=0.0;
//compute population of differences
for(int i=0;i0.0){
sign[pointer]=true;
}
else{
sign[pointer]=false;
}
pointer++;
}
}
//compute ranks
double min;
double points;
int tied;
Arrays.fill(ranks, -1.0);
for(int rank=1;rank<=N;){
min=Double.MAX_VALUE;
tied=1;
for(int i=0;i1){
//If there is a odd number of them, one is discarded.
if(zeroDifferences%2==1){
increment=zeroDifferences-1.0;
}
else{
increment=zeroDifferences;
}
//Addition of ranks due to zero differences
//(e.g if 2 zero differences were added, the sum of these ranks is:
//((2+1)*(2))/2 = 3, thus 1.5 points are added to each statistic)
sum0=(((double)zeroDifferences+1.0)*(double)zeroDifferences)/2.0;
sum0/=2.0;
rPlus+=sum0;
rMinus+=sum0;
//Scaling the rest of ranks
//(e.g if 2 zero differences were added, each rank must be increased by 2)
for(int i=0;i();
double aux;
for(int i=0;i
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