/***********************************************************************
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.populationQuantile;
import javanpst.data.structures.sequence.NumericSequence;
import javanpst.distributions.common.discrete.BinomialDistribution;
import javanpst.tests.StatisticalTest;
/**
* A procedure to testing hypotheses about population quantiles.
*
* This test allows to test an hypothesis value for a given
* population quantile and a given sample.
*
* A binomial distribution is used to compute left tail p-value
* (true value of the quantile is greater than the estimation),
* right tail p-value (true value is lower than the estimation),
* and double tail p-value (true value is distinct than the estimation).
*
* @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
* @version 1.0
*/
public class PopulationQuantile extends StatisticalTest{
/**
* Binomial distribution
*/
private BinomialDistribution distribution;
/**
* Sample data from the population
*/
private NumericSequence sequence;
/**
* Quantile estimated
*/
private double quantile;
/**
* Value to test
*/
private double hyphotesisValue;
/**
* Test statistic
*/
private int K;
/**
* Left tail p-value
*/
private double exactLeftTail;
/**
* Right tail p-value
*/
private double exactRightTail;
/**
* Double tail p-value
*/
private double exactDoubleTail;
/**
* Default builder
*/
public PopulationQuantile(){
setReportFormat();
clearData();
}//end-method
/**
* Clears the data stored in the test
*/
public void clearData(){
K=0;
quantile=0.0;
hyphotesisValue=0.0;
exactLeftTail=-1.0;
exactRightTail=-1.0;
exactDoubleTail=-1.0;
sequence = new NumericSequence();
dataReady=false;
performed=false;
}//end-method
/**
* Builder. Set data and all the necessary parameters of the test.
*
* @param newSequence sample data
* @param quantile quantile to test
* @param value hyphotesis value
*/
public PopulationQuantile(NumericSequence newSequence,double quantile,double value){
setReportFormat();
sequence = new NumericSequence(newSequence);
this.quantile = quantile;
hyphotesisValue = value;
distribution=new BinomialDistribution();
distribution.setN(sequence.size());
distribution.setP(1.0-quantile);
dataReady=true;
}//end-method
/**
* Sets data and all the necessary parameters of the test
*
* @param newSequence sample data
* @param quantile quantile to test
* @param value hyphotesis value
*/
public void setData(NumericSequence newSequence,double quantile,double value){
sequence = new NumericSequence(newSequence);
this.quantile = quantile;
hyphotesisValue = value;
distribution=new BinomialDistribution();
distribution.setN(sequence.size());
distribution.setP(1.0-quantile);
dataReady=true;
}//end-method
/**
* Set quantile to estimate
*
* @param p quantile
*/
public void setQuantile(double p){
this.quantile = p;
}//end-method
/**
* Set hypothesis value
*
* @param value hypothesis value
*/
public void setConfidence(double value){
this.hyphotesisValue = value;
}//end-method
/**
* Performs the test
*/
public void doTest(){
if(!dataReady){
System.out.println("Data is not ready");
return;
}
K=0;
sequence.sort();
for(double d : sequence.getSequence()){
if(d>hyphotesisValue){
K++;
}
}
//computation of p-value
exactLeftTail=distribution.computeCumulativeProbability(K);
exactRightTail=1.0-distribution.computeCumulativeProbability(K-1);
exactDoubleTail=Math.min(exactLeftTail,exactRightTail)*2.0;
performed=true;
}//end-method
/**
* Get K statistic
*
* @return K statistic
*/
public double getK(){
return K;
}//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
/**
* 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+="Testing a population quantile\n";
report+="***************************************\n\n";
report+="Hypothesis value value: "+nf6.format(hyphotesisValue)+"\n";
report+="Quantile: "+nf6.format(quantile)+"\n";
report+="K statistic: "+nf6.format(K)+"\n\n";
report+="Exact P-Value (Left tail, Kp < Kp0): "+nf6.format(exactLeftTail)+"\n";
report+="Exact P-Value (Right tail, Kp > Kp0): "+nf6.format(exactRightTail)+"\n";
report+="Exact P-Value (Double tail, Kp != Kp0): "+nf6.format(exactDoubleTail)+"\n\n";
return report;
}//end-method
}//end-class
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