/***********************************************************************

	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.countData.mcNemarTest;

import javanpst.data.structures.dataTable.DataTable;
import javanpst.distributions.tests.McNemarDistribution;
import javanpst.tests.StatisticalTest;

/**
 * The McNemar test.
 * 
 * This test can be applied to determine whether the row and column marginal frequencies 
 * of a 2 x 2 contingency table are equal.
 * 
 * Three approximations are provided:
 * 
 * - Exact p-value (through a binomial distribution)
 * - Asymptotic p-value (through a normal distribution)
 * - Asymptotic p-value (through a chi-square distribution)
 * 
 * @author Joaqu�n Derrac Rus (jderrac@decsai.ugr.es)
 * @version 1.0
 */
public class McNemarTest extends StatisticalTest{

	/**
	 * Distribution of the test
	 */
	private McNemarDistribution distribution;
	
	/**
	 * Data to analyze
	 */
	private DataTable data;
	
	/**
	 * Contingency matrix
	 */
	private double contingencyTable [][];
	
	/**
	 * S statistic (Binomial)
	 */
	private double S;
	
	/**
	 * Exact p-value
	 */
	private double exactPValue;
	
	/**
	 * Z statistic (Normal)
	 */
	private double Z;
	
	/**
	 * Asymptotic p-value (Normal)
	 */
	private double asymptoticNormalPValue;
	
	/**
	 * T statistic (Chi-square)
	 */
	private double T;
	
	/**
	 * Asymptotic p-value (Chi-square)
	 */
	private double asymptoticChiPValue;
	
	/**
	 * Default builder
	 */
	public McNemarTest(){

		distribution=McNemarDistribution.getInstance();
		setReportFormat();
		clearData();
		
	}//end-method
	
	/**
	 * Clears the data stored in the test
	 */
	public void clearData(){
		
		data=new DataTable();
		
		performed=false;
		dataReady=false;
		
		T=0.0;
		Z=0.0;
		S=0.0;
		
		contingencyTable=null;
		
		asymptoticChiPValue=-1.0;
		asymptoticNormalPValue=-1.0;
		exactPValue=-1.0;
		
	}//end-method
	
	/**
	 * Builder. Load data to test.
	 * 
	 * @param newData data to test.
	 */
	public McNemarTest(DataTable newData){
		
		distribution=McNemarDistribution.getInstance();
		setReportFormat();
		
		data=DataTable.newInstance(newData);
		
		if((data.getColumns()>2)||(data.getColumns()<2)||(data.getRows()>2)||(data.getRows()<2)){
			System.out.println("Data must be represented in two rows and two columns");
			clearData();
			return;
		}
		
		for(int i=0;i0){
				System.out.println("No null values allowed in this test.");
				clearData();
				return;
			}
		}
		
		contingencyTable=new double[data.getRows()][data.getColumns()];
		
		for(int i=0;i2)||(data.getColumns()<2)||(data.getRows()>2)||(data.getRows()<2)){
			System.out.println("Data must be represented in two rows and two columns");
			clearData();
			return;
		}
		
		for(int i=0;i0){
				System.out.println("No null values allowed in this test.");
				clearData();
				return;
			}
		}
		
		contingencyTable=new double[data.getRows()][data.getColumns()];
		
		for(int i=0;i