Two Way ANOVA
/*
July 2006. Annie Che <chea@stat.ucla.edu>. UCLA Statistics.
Source of example data: An Introduction to Computational Statitics by Robert I Jennrich,
Page 207, example of regression on time for coins to reach bottom of fountains.
*/
package edu.ucla.stat.SOCR.analyses.example;
import java.util.HashMap;
import edu.ucla.stat.SOCR.analyses.data.Data;
import edu.ucla.stat.SOCR.analyses.data.DataType;
import edu.ucla.stat.SOCR.analyses.result.AnovaTwoWayResult;
public class AnovaTwoWayExample {
public static void main(String args[]) {
String[] group1 = {"1","1","1","2","2","2"};
String[] group2 = {"1","2","3","1","2","3"};
double[] score = {93,136,198,88,148,279};
// you'll need to instantiate a data instance first.
Data data = new Data();
/*********************************************************************
then put the data into the Data Object.
append the predictor data using method "addPredictor".
append the response data using method "addResponse".
**********************************************************************/
data.addPredictor("I", group1, DataType.FACTOR);
data.addPredictor("J", group2, DataType.FACTOR);
data.addResponse("Y", score, DataType.QUANTITATIVE);
try {
AnovaTwoWayResult result = data.modelAnovaTwoWay();
System.out.println("result = " + result);
if (result != null) {
// Getting the model's parameter estiamtes and statistics.
int dfCTotal = result.getDFTotal();
int dfModel = result.getDFModel();
int dfError = result.getDFError();
System.out.println("dfCTotal = " + dfCTotal);
System.out.println("dfModel = " + dfModel);
System.out.println("dfError = " + dfError);
double rssTotal = result.getRSSTotal();
double rssModel = result.getRSSModel();
double rssError = result.getRSSError();
System.out.println("rssTotal = " + rssTotal);
System.out.println("rssModel = " + rssModel);
System.out.println("rssError = " + rssError);
double mssModel = result.getMSSModel();
double mssError = result.getMSSError();
System.out.println("mssModel = " + mssModel);
System.out.println("mssError = " + mssError);
double fValue = result.getFValue();
String pValue = result.getPValue();
System.out.println("fValue = " + fValue);
System.out.println("pValue = " + pValue);
String[] varList = result.getVariableList();
int[] dfGroup = result.getDFGroup();
double[] rssGourp = result.getRSSGroup();
double[] mseGourp = result.getMSEGroup();
double[] fValueGroup = result.getFValueGroup();
String[] pValueGroup = result.getPValueGroup();
double[] residuals = result.getResiduals();
double[] predicted = result.getPredicted();
// residuals after being sorted ascendantly.
double[] sortedResiduals = result.getSortedResiduals();
// sortedResiduals after being standardized.
double[] sortedStandardizedResiduals =
result.getSortedStandardizedResiduals();
// the original index of sortedResiduals, stored as integer array.
int[] sortedResidualsIndex = result.getSortedResidualsIndex();
// the normal quantiles of sortedResiduals.
double[] sortedNormalQuantiles = result.getSortedNormalQuantiles();
// sortedNormalQuantiles after being standardized.
double[] sortedStandardizedNormalQuantiles =
result.getSortedStandardizedNormalQuantiles();
System.out.println("dfCTotal = " + dfCTotal);
System.out.println("dfModel = " + dfModel);
System.out.println("dfError = " + dfError);
System.out.println("rssTotal = " + rssTotal);
System.out.println("rssModel = " + rssModel);
System.out.println("rssError = " + rssError);
System.out.println("mssModel = " + mssModel);
System.out.println("mssError = " + mssError);
System.out.println("fValue = " + fValue);
System.out.println("pValue = " + pValue);
for (int i = 0; i < varList.length; i++) {
System.out.println("varList["+i+"] = " + varList[i]);
}
for (int i = 0; i < residuals.length; i++) {
System.out.println("residuals["+i+"] = " + residuals[i]);
}
}
} catch (Exception e) {
System.out.println(e);
}
}
}
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