/***********************************************************************
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.signTest;
import javanpst.data.structures.dataTable.DataTable;
import javanpst.data.structures.sequence.NumericSequence;
import javanpst.distributions.common.continuous.NormalDistribution;
import javanpst.distributions.common.discrete.BinomialDistribution;
import javanpst.tests.StatisticalTest;
/**
* The sign test
*
* This is a special case of the Population Quantile test
* where the quantile is 0.5 (the median) and the hypothesis
* value is 0.
*
* If a sequence is tested, the null hypothesis will be median=0
* If a two-column table is analyzed, then the population of
* differences will be tested, thus testing which population is greater
*
* @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
* @version 1.0
*/
public class SignTest extends StatisticalTest{
/**
* Sequence to analyze
*/
private NumericSequence sequence;
/**
* Binomial distribution
*/
private BinomialDistribution binomial;
/**
* Normal distribution
*/
private NormalDistribution normal;
/**
* K Statistic
*/
private double K,K2;
/**
* 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;
/**
* Default builder
*/
public SignTest(){
setReportFormat();
clearData();
}//end-method
/**
* Clears the data stored in the test
*/
public void clearData(){
K=0;
K2=0;
exactLeftTail=-1.0;
exactRightTail=-1.0;
exactDoubleTail=-1.0;
asymptoticLeftTail=-1.0;
asymptoticRightTail=-1.0;
asymptoticDoubleTail=-1.0;
sequence = new NumericSequence();
dataReady=false;
performed=false;
}//end-method
/**
* Builder. Load data to test.
*
* @param newSequence data to test.
*/
public SignTest(NumericSequence newSequence){
setReportFormat();
sequence = new NumericSequence(newSequence);
binomial = new BinomialDistribution();
normal = new NormalDistribution();
binomial.setP(0.5);
dataReady=true;
performed=false;
}//end-method
/**
* Sets data to test
*
* @param newSequence data to test.
*/
public void setData(NumericSequence newSequence){
sequence = new NumericSequence(newSequence);
binomial = new BinomialDistribution();
normal = new NormalDistribution();
binomial.setP(0.5);
dataReady=true;
performed=false;
}//end-method
/**
* Builder. Load data from two populations to test.
*
* @param newData data to test.
*/
public SignTest(DataTable newData){
setReportFormat();
if(newData.getColumns()!=2){
System.out.println("Sign 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;
}
}
double differences [];
differences = new double [newData.getRows()];
for(int i=0;i0){
System.out.println("No null values allowed in this test.");
clearData();
return;
}
}
double differences [];
differences = new double [newData.getRows()];
for(int i=0;i0){
K++;
}
if(sequence.get(i)<0){
K2++;
}
if(sequence.get(i)==0){
draws++;
}
}
//compute p-values
effectiveSamples=sequence.size()-draws;
binomial.setN(effectiveSamples);
exactLeftTail=binomial.computeCumulativeProbability(effectiveSamples-K2);
exactRightTail=binomial.computeCumulativeProbability(effectiveSamples-K);
exactDoubleTail=Math.min(Math.min(exactLeftTail,exactRightTail)*2.0,1.0);
//asymptotic p-values
double numerator, denominator;
denominator=0.5*Math.sqrt(effectiveSamples);
numerator=((double)K-(0.5*effectiveSamples)+0.5);
asymptoticLeftTail=normal.getTipifiedProbability(numerator/denominator, false);
numerator=((double)K-(0.5*effectiveSamples)-0.5);
asymptoticRightTail=1.0-normal.getTipifiedProbability(numerator/denominator, false);
asymptoticDoubleTail=Math.min(Math.min(asymptoticLeftTail,asymptoticRightTail)*2.0,1.0);
performed=true;
}//end-method
/**
* Get K statistic of the first sample
*
* @return K statistic
*/
public double getK(){
return K;
}//end-method
/**
* Get K statistic of the second sample
*
* @return K statistic
*/
public double getK2(){
return K2;
}//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"+sequence;
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+="Sign test\n";
report+="*********\n\n";
report+="K statistic (sample 1): "+nf6.format(K)+"\n";
report+="K statistic (sample 2): "+nf6.format(K2)+"\n\n";
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";
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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