package com.imsl.test.example.stat;

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

/**
 * <p>
 * Analyzes interval type data with the Poisson model.</p>
 *
 * This example illustrates the Poisson model when all types of interval data
 * are present. The example also illustrates the use of classification variables
 * and the detection of potentially infinite estimates (which turn out here to
 * be finite). These potential estimates lead to the two iteration summaries.
 * The input data is
 * <center>
 * <table border="1">
 * <tr align="center"><td><b>ilt</b></td><td><b>irt</b></td><td><b>icen</b></td>
 * <td><b>Class 1</b></td><td><b>Class 2</b></td></tr>
 * <tr align="center"><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td></tr>
 * <tr align="center"><td>9</td><td>4</td><td>3</td><td>0</td><td>0</td></tr>
 * <tr align="center"><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td></tr>
 * <tr align="center"><td>9</td><td>0</td><td>2</td><td>1</td><td>1</td></tr>
 * <tr align="center"><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>
 * </table><br/>
 * </center>
 * A linear model \( \mu + \beta_1 x_1 + \beta_2 x_2 \) is fit where \(x_1 = 1\)
 * if the Class 1 variable is 0, \(x_1 = 1\), otherwise, and the \(x_2\)
 * variable is similarly defined
 *
 *
 * @see <a href="CategoricalGenLinModelEx2.java">Code</a>
 * @see <a href="doc-files/CategoricalGenLinModelEx2.out">Output</a>
 */
public class CategoricalGenLinModelEx2 {

    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 = {
            {0.0, 5.0, 0.0, 1.0, 0.0},
            {9.0, 4.0, 3.0, 0.0, 0.0},
            {0.0, 4.0, 1.0, 0.0, 0.0},
            {9.0, 0.0, 2.0, 1.0, 1.0},
            {0.0, 1.0, 0.0, 0.0, 1.0}
        };
        CategoricalGenLinModel CATGLM;
        CATGLM = new CategoricalGenLinModel(x, CategoricalGenLinModel.MODEL0);
        CATGLM.setUpperEndpointColumn(0);
        CATGLM.setLowerEndpointColumn(1);
        CATGLM.setOptionalDistributionParameterColumn(1);
        CATGLM.setCensorColumn(2);
        CATGLM.setInfiniteEstimateMethod(0);
        CATGLM.setModelIntercept(1);
        int[] indcl = {3, 4};
        CATGLM.setClassificationVariableColumn(indcl);
        int[] nvef = {1, 1};
        int[] indef = {3, 4};
        CATGLM.setEffects(indef, nvef);
        CATGLM.setUpperBound(4);

        p.setTitle("Coefficient Statistics");
        p.print(CATGLM.solve());
        System.out.println("Log likelihood " + CATGLM.getOptimizedCriterion());
        p.setTitle("Asymptotic Coefficient Covariance");
        p.setMatrixType(1);
        p.print(CATGLM.getCovarianceMatrix());
        p.setMatrixType(0);
        p.setTitle("Case Analysis");
        p.print(CATGLM.getCaseAnalysis());
        p.setTitle("Last Coefficient Update");
        p.print(CATGLM.getLastParameterUpdates());
        p.setTitle("Covariate Means");
        p.print(CATGLM.getDesignVariableMeans());
        p.setTitle("Distinct Values For Each Class Variable");
        p.print(CATGLM.getClassificationVariableValues());
        System.out.println("Number of Missing Values "
                + CATGLM.getNRowsMissing());
    }
}