package com.imsl.test.example.stat;

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

/**
 * <p>
 * Performs stepwise regression variable selection.</p>
 *
 * <p>
 * This example uses a data set from Draper and Smith (1981, pp. 629-630). The
 * method <code>compute</code> is invoked to find the best regression subset
 * from the four candidate variables.
 * </p>
 *
 *
 * @see <a href="StepwiseRegressionEx1.java">Code</a>
 * @see <a href="doc-files/StepwiseRegressionEx1.out">Output</a>
 */
public class StepwiseRegressionEx1 {

    private static void print(String[] labels, ANOVA anova) {

        double[] values = anova.getArray();

        for (int i = 0; i < values.length - 2; i++) {
            System.out.printf("%41s%s\n", labels[i], customDecimalFormat(values[i]));
        }
        System.out.println();
    }

    private static String customDecimalFormat(double value) {

        java.text.DecimalFormat df1 = new java.text.DecimalFormat("##,###.###");
        String s = df1.format(value);
        String[] toc = s.split("\\.");
        if (toc.length == 1) {
            s = String.format("%6s", s);
        } else {
            toc[0] = String.format("%6s", toc[0]);
            toc[1] = String.format("%-3s", toc[1]);
            s = toc[0] + "." + toc[1];
        }
        return s;
    }

    public static void main(String[] args) throws Exception {
        double x[][] = {
            {7., 26., 6., 60.}, {1., 29., 15., 52.}, {11., 56., 8., 20.},
            {11., 31., 8., 47.}, {7., 52., 6., 33.}, {11., 55., 9., 22.},
            {3., 71., 17., 6.}, {1., 31., 22., 44.}, {2., 54., 18., 22.},
            {21., 47., 4., 26}, {1., 40., 23., 34.}, {11., 66., 9., 12.},
            {10.0, 68., 8., 12.}
        };

        double y[] = {
            78.5, 74.3, 104.3, 87.6, 95.9, 109.2, 102.7,
            72.5, 93.1, 115.9, 83.8, 113.3, 109.4
        };

        String[] rowLabels = {
            "degrees of freedom for regression: ",
            "degrees of freedom for error: ",
            "total degrees of freedom: ",
            "sum of squares for regression: ",
            "sum of squares for error: ",
            "total sum of squares: ",
            "regression mean square: ",
            "error mean square: ",
            "F-statistic: ",
            "p-value: ",
            "R-squared (in percent): ",
            "adjusted R-squared (in percent): ",
            "est. standard deviation of within error: "
        };

        StepwiseRegression sr = new StepwiseRegression(x, y);
        sr.compute();

        System.out.printf("%20s%s\n", " ", "*** ANOVA ***");
        System.out.printf("%45s%s\n", " ", "Value");

        print(rowLabels, sr.getANOVA());

        StepwiseRegression.CoefficientTTests coefT = sr.getCoefficientTTests();
        double coef[][] = new double[4][4];
        for (int i = 0; i < 4; i++) {
            coef[i][0] = coefT.getCoefficient(i);
            coef[i][1] = coefT.getStandardError(i);
            coef[i][2] = coefT.getTStatistic(i);
            coef[i][3] = coefT.getPValue(i);
        }

        String[] cLabels = {"Coef", "Std. Err", "T-Stat", "p-Value"};

        PrintMatrix pm = new PrintMatrix();
        PrintMatrixFormat pmf = new PrintMatrixFormat();
        pmf.setColumnLabels(cLabels);

        pm.setTitle("*** Coef *** ");
        pm.print(pmf, coef);
        pm.setTitle("*** History *** ");
        pm.print(sr.getHistory());
        pm.setTitle("*** VIF *** ");
        pm.print(sr.getCoefficientVIF());
        pm.setTitle("*** CovS *** ");
        pm.print(sr.getCovariancesSwept());
        System.out.println("*** Intercept ***   " + sr.getIntercept());
    }
}