package com.imsl.test.example.stat;

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

/**
 * <p>
 * Sets up data for a two-way analysis of covariance.</p>
 *
 * <p>
 * In this example, a two-way analysis of covariance model containing all the
 * interaction terms is fit. First, <code>RegressorsForGLM</code> is used to
 * produce a matrix of regressors, <code>regressors</code>, from the data
 * <code>x</code>. Then, <code>regressors</code> is used as the input matrix
 * into <code>Regression</code> to produce the final fit. The mapping from
 * effects to columns in the matrix <code>regressors</code> is given by the
 * jagged array returned by <code>getEffectsColumns</code>. Each row in this
 * matrix gives the column number for the corresponding effect.
 * </p>
 *
 *
 * @see <a href="RegressorsForGLMEx2.java">Code</a>
 * @see <a href="doc-files/RegressorsForGLMEx2.out">Output</a>
 */

public class RegressorsForGLMEx2 {

    static public void main(String arg[]) {
        double x[][] = {
            {1.0, 1.0, 1.11},
            {1.0, 1.0, 2.22},
            {1.0, 1.0, 3.33},
            {1.0, 2.0, 1.11},
            {1.0, 2.0, 2.22},
            {1.0, 2.0, 3.33},
            {1.0, 3.0, 1.11},
            {1.0, 3.0, 2.22},
            {1.0, 3.0, 3.33},
            {2.0, 1.0, 1.11},
            {2.0, 1.0, 2.22},
            {2.0, 1.0, 3.33},
            {2.0, 2.0, 1.11},
            {2.0, 2.0, 2.22},
            {2.0, 2.0, 3.33},
            {2.0, 3.0, 1.11},
            {2.0, 3.0, 2.22},
            {2.0, 3.0, 3.33}
        };
        double y[] = {
            1.0, 2.0, 2.0, 4.0, 4.0, 6.0,
            3.0, 3.5, 4.0, 4.5, 5.0, 5.5,
            2.0, 3.0, 4.0, 5.0, 6.0, 7.0
        };
        int effects[][] = {
            {0},
            {1},
            {0, 1},
            {2},
            {0, 2},
            {1, 2},
            {0, 1, 2}
        };

        int nClassVariables = 2;

        RegressorsForGLM r = new RegressorsForGLM(x, nClassVariables);
        r.setDummyMethod(RegressorsForGLM.LEAVE_OUT_LAST);
        r.setEffects(effects);

        System.out.println("Mapping of Effects to Regressor Columns");
        int col[][] = r.getEffectsColumns();
        for (int iEffect = 0; iEffect < effects.length; iEffect++) {
            System.out.print("Effect {");
            for (int j = 0; j < effects[iEffect].length; j++) {
                if (j > 0) {
                    System.out.print(", ");
                }
                System.out.print(effects[iEffect][j]);
            }
            System.out.print("} is in regressor column(s) {");
            for (int j = 0; j < col[iEffect].length; j++) {
                if (j > 0) {
                    System.out.print(", ");
                }
                System.out.print(col[iEffect][j]);
            }
            System.out.println("}");
        }
        System.out.println();

        double regressors[][] = r.getRegressors();

        PrintMatrixFormat pmf = new PrintMatrixFormat();
        pmf.setColumnLabels(new String[]{"Alpha1", "Beta1", "Beta2",
            "Gamma11", "Gamma12", "Delta", "Zeta1", "Eta1", "Eta2",
            "Theta11", "Theta12"});
        new PrintMatrix("Regressors").print(pmf, regressors);

        int nRegressors = r.getNumberOfRegressors();
        LinearRegression regression = new LinearRegression(nRegressors, true);
        regression.update(regressors, y);

        System.out.println("        * * * Analysis of Variance * * *");
        ANOVA anova = regression.getANOVA();

        Object table[][] = new Object[15][2];
        table[0][0] = "Degrees of freedom for the model       ";
        table[0][1] = anova.getDegreesOfFreedomForModel();
        table[1][0] = "Degrees of freedom for the error       ";
        table[1][1] = anova.getDegreesOfFreedomForError();
        table[2][0] = "Total degrees of freedom               ";
        table[2][1] = anova.getTotalDegreesOfFreedom();
        table[3][0] = "Sum of squares for the model           ";
        table[3][1] = anova.getSumOfSquaresForModel();
        table[4][0] = "Sum of squares for error               ";
        table[4][1] = anova.getSumOfSquaresForError();
        table[5][0] = "Total sum of squares                   ";
        table[5][1] = anova.getTotalSumOfSquares();
        table[6][0] = "Model mean square                      ";
        table[6][1] = anova.getModelMeanSquare();
        table[7][0] = "Error mean square                      ";
        table[7][1] = anova.getErrorMeanSquare();
        table[8][0] = "F-statistic                            ";
        table[8][1] = anova.getF();
        table[9][0] = "p-value                                ";
        table[9][1] = anova.getP();
        table[10][0] = "R-squared                             ";
        table[10][1] = anova.getRSquared();
        table[11][0] = "Adjusted R-squared                    ";
        table[11][1] = anova.getAdjustedRSquared();
        table[12][0] = "Standard deviation for the model error";
        table[12][1] = anova.getModelErrorStdev();
        table[13][0] = "Overall mean of y                     ";
        table[13][1] = anova.getMeanOfY();
        table[14][0] = "Coefficient of variation              ";
        table[14][1] = anova.getCoefficientOfVariation();
        pmf = new PrintMatrixFormat();
        pmf.setNoColumnLabels();
        pmf.setNoRowLabels();
        pmf.setNumberFormat(new java.text.DecimalFormat("0.0000"));
        new PrintMatrix().print(pmf, table);
    }
}