import static java.lang.System.out;

import java.util.*;

import javastat.*;

import javastat.regression.glm.*;

import static javastat.util.Argument.*;

import static javastat.util.Output.*;

import javastat.util.*;

/**

*

* <p>Example: class LogisticRegression.</p>

*/

public class LogisticRegressionExample

{

public static void main(String[] args)

{

String[][] nominalCovariate = { {"a", "a", "b", "b", "c", "c", "a", "a",

"b", "c", "c", "c"},

{"T", "T", "T", "F", "T", "F", "T",

"F", "F", "T", "F", "F"} };

String[] stringBinaryResponse = {"s", "d", "s", "d", "s", "d", "d",

"s", "d", "s", "d", "d"};

DataManager dm = new DataManager();

LogisticRegression testclass1 = new LogisticRegression(

stringBinaryResponse, nominalCovariate);

double[] coefficients = testclass1.coefficients;

double[][] confidenceInterval = testclass1.confidenceInterval;

double[] testStatistic = testclass1.testStatistic;

double[] pValue = testclass1.pValue;

double[][] devianceTable = testclass1.devianceTable;

LogisticRegression testclass2 = new LogisticRegression();

coefficients =

testclass2.coefficients(stringBinaryResponse, nominalCovariate);

confidenceInterval = testclass2.confidenceInterval(

0.1, stringBinaryResponse, nominalCovariate);

testStatistic = testclass2.testStatistic(stringBinaryResponse,

nominalCovariate);

pValue = testclass2.pValue(stringBinaryResponse, nominalCovariate);

devianceTable = testclass2.devianceTable(stringBinaryResponse,

nominalCovariate);

Hashtable argument1 = new Hashtable();

StatisticalAnalysis testclass3 = new LogisticRegression(

argument1, stringBinaryResponse, nominalCovariate).

statisticalAnalysis;

coefficients = (double[]) testclass3.output.get(COEFFICIENTS);

confidenceInterval = (double[][]) testclass3.output.get(

CONFIDENCE_INTERVAL);

testStatistic = (double[]) testclass3.output.get(TEST_STATISTIC);

pValue = (double[]) testclass3.output.get(PVALUE);

devianceTable =

(double[][]) testclass3.output.get(DEVIANCE_TABLE);

Hashtable argument2 = new Hashtable();

LogisticRegression testclass4 =

new LogisticRegression(argument2, null);

coefficients = testclass4.coefficients(argument2, stringBinaryResponse,

nominalCovariate);

argument2.put(ALPHA, 0.1);

confidenceInterval = testclass4.confidenceInterval(argument2,

stringBinaryResponse, nominalCovariate);

testStatistic = testclass4.testStatistic(

argument2, stringBinaryResponse, nominalCovariate);

pValue = testclass4.

pValue(argument2, stringBinaryResponse, nominalCovariate);

devianceTable = testclass4.devianceTable(

argument2, stringBinaryResponse, nominalCovariate);

}

}

Results:

The estimated coefficients based on non-null constructor

= [-1.928 , 0.259 , 0.447 , 2.436]

The t statistics based on non-null constructor

= [-1.118 , 0.135 , 0.269 , 1.594]

The p-values for the t statistics based on non-null constructor

= [0.263 , 0.893 , 0.788 , 0.111]

The 95% confidence interval for parameter 1 based on non-null constructor

= [-5.306 , 1.451]

The 95% confidence interval for parameter 2 based on non-null constructor

= [-3.511 , 4.029]

The 95% confidence interval for parameter 3 based on non-null constructor

= [-2.81 , 3.704]

The 95% confidence interval for parameter 4 based on non-null constructor

= [-0.56 , 5.431]

D.F. Difference Deviance Difference D.F. Deviance

Null 0.0 0.0 11.0 12.482

Factor A 2.0 0.206 9.0 12.275

Factor A+B 1.0 3.123 8.0 9.152

{CONFIDENCE_INTERVAL=[[D@109a4c,

PEARSON_RESIDUALS=[D@201f9, RESPONSE_VARIANCE=[D@1cf8583,

TEST_STATISTIC=[D@14693c7, PVALUE=[D@901887,

DEVIANCE_RESIDUALS=[D@3a6727, DEVIANCE_TABLE=[[D@4a65e0,

MEANS=[D@665753, FITTED_VALUES=[D@ef22f8,

COEFFICIENTS=[D@1e0cf70, RESPONSE_RESIDUALS=[D@52fe85}

The estimated coefficients based on null constructor

= [-1.928 , 0.259 , 0.447 , 2.436]

The z statistics based on null constructor

= [-1.118 , 0.135 , 0.269 , 1.594]

The p-values for the t statistics based on null constructor

= [0.263 , 0.893 , 0.788 , 0.111]

The 90% confidence interval for parameter 1 based on null constructor

= [-4.763 , 0.907]

The 90% confidence interval for parameter 2 based on null constructor

= [-2.905 , 3.423]

The 90% confidence interval for parameter 3 based on null constructor

= [-2.286 , 3.18]

The 90% confidence interval for parameter 4 based on null constructor

= [-0.078 , 4.95]

D.F. Difference Deviance Difference D.F. Deviance

Null 0.0 0.0 11.0 12.482

Factor A 2.0 0.206 9.0 12.275

Factor A+B 1.0 3.123 8.0 9.152