package com.imsl.test.example.stat;

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

/**
 * <p>
 * Fits a logit and probit categorical model to beetle mortality
 * data.</p>
 * 
 * The first example is from Prentice (1976) and involves the mortality of
 * beetles after exposure to various concentrations of carbon disulphide. Both a
 * logit and a probit fit are produced for linear model \(\mu + \beta x\). The
 * data is given as
 * <center>
 * <table border="1">
 * <tr><td align="center">Concentration (x)</td><td>N</td><td>y</td></tr>
 * <tr><td align="center" valign="top">1.690</td><td>59</td><td>6</td></tr>
 * <tr><td align="center" valign="top">1.724</td><td>60</td><td>13</td></tr>
 * <tr><td align="center" valign="top">1.755</td><td>62</td><td>18</td></tr>
 * <tr><td align="center" valign="top">1.784</td><td>56</td><td>28</td></tr>
 * <tr><td align="center" valign="top">1.811</td><td>63</td><td>52</td></tr>
 * <tr><td align="center" valign="top">1.836</td><td>59</td><td>53</td></tr>
 * <tr><td align="center" valign="top">1.861</td><td>62</td><td>61</td></tr>
 * <tr><td align="center" valign="top">1.883</td><td>60</td><td>60</td></tr>
 * </table><br/>
 *</center>
 *
 * @see <a href="CategoricalGenLinModelEx1.java">Code</a>
 * @see <a href="doc-files/CategoricalGenLinModelEx1.out">Output</a>
 */

public class CategoricalGenLinModelEx1 {

    public static void main(String argv[]) throws Exception {
        // Set up a PrintMatrix object for later use.
        PrintMatrixFormat mf;
        PrintMatrix p;
        p = new PrintMatrix();
        mf = new PrintMatrixFormat();
        mf.setNoRowLabels();
        mf.setNoColumnLabels();

        double[][] x = {
            {1.690, 59.0, 6.0},
            {1.724, 60.0, 13.0},
            {1.755, 62.0, 18.0},
            {1.784, 56.0, 28.0},
            {1.811, 63.0, 52.0},
            {1.836, 59.0, 53.0},
            {1.861, 62.0, 61.0},
            {1.883, 60.0, 60.0}
        };
        CategoricalGenLinModel CATGLM3, CATGLM4;
        // MODEL3
        CATGLM3 = new CategoricalGenLinModel(x, CategoricalGenLinModel.MODEL3);
        CATGLM3.setLowerEndpointColumn(2);
        CATGLM3.setOptionalDistributionParameterColumn(1);
        CATGLM3.setInfiniteEstimateMethod(1);
        CATGLM3.setModelIntercept(1);
        int[] nvef = {1};
        int[] indef = {0};
        CATGLM3.setEffects(indef, nvef);
        CATGLM3.setUpperBound(1);

        System.out.println("MODEL3");
        p.setTitle("Coefficient Statistics");
        p.print(CATGLM3.solve());
        System.out.println("Log likelihood " + CATGLM3.getOptimizedCriterion());
        p.setTitle("Asymptotic Coefficient Covariance");
        p.setMatrixType(1);
        p.print(CATGLM3.getCovarianceMatrix());
        p.setMatrixType(0);
        p.setTitle("Case Analysis");
        p.print(CATGLM3.getCaseAnalysis());
        p.setTitle("Last Coefficient Update");
        p.print(CATGLM3.getLastParameterUpdates());
        p.setTitle("Covariate Means");
        p.print(CATGLM3.getDesignVariableMeans());
        p.setTitle("Observation Codes");
        p.print(CATGLM3.getExtendedLikelihoodObservations());
        System.out.println("Number of Missing Values "
                + CATGLM3.getNRowsMissing());

        // MODEL4
        CATGLM4 = new CategoricalGenLinModel(x, CategoricalGenLinModel.MODEL4);
        CATGLM4.setLowerEndpointColumn(2);
        CATGLM4.setOptionalDistributionParameterColumn(1);
        CATGLM4.setInfiniteEstimateMethod(1);
        CATGLM4.setModelIntercept(1);
        CATGLM4.setEffects(indef, nvef);
        CATGLM4.setUpperBound(1);
        CATGLM4.solve();

        System.out.println("MODEL4");
        System.out.println("Log likelihood " + CATGLM4.getOptimizedCriterion());
        p.setTitle("Coefficient Statistics");
        p.print(CATGLM4.getParameters());
    }
}