package com.imsl.test.example.stat;

import com.imsl.stat.*;
import com.imsl.math.PrintMatrix;

/**
 * <p>
 * Generates binary regressors for classification variables.
 * </p>
 * 
 * This example uses  {@link RegressorsForGLM} to generate
 * binary variables for two classification variables, \(A\) and
 * \(B\), with two and three values, respectively. Regressors for a one-way
 * model (the default model order) are generated using the default dummy method.
 * The five regressors generated are \(A_1, A_2, B_1, B_2\) and
 * \(B_3\).
 *
 * @see <a href="RegressorsForGLMEx1.java">Code</a>
 * @see <a href="doc-files/RegressorsForGLMEx1.out">Output</a>
 */
public class RegressorsForGLMEx1 {

    public static void main(String args[]) {
        double x[][] = {
            {10.0, 5.0},
            {20.0, 15.0},
            {20.0, 10.0},
            {10.0, 10.0},
            {10.0, 15.0},
            {20.0, 5.0}
        };

        RegressorsForGLM r = new RegressorsForGLM(x, 2);
        double regressors[][] = r.getRegressors();
        int n = r.getNumberOfRegressors();
        System.out.println("Number of regressors = " + n);
        new PrintMatrix("Regressors").print(regressors);
    }
}