com.itemanalysis.psychometrics.factoranalysis.FactorAnalysisTest.java Source code

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Here is the source code for com.itemanalysis.psychometrics.factoranalysis.FactorAnalysisTest.java

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/**
 * Copyright 2014 J. Patrick Meyer
 * <p/>
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 * <p/>
 * http://www.apache.org/licenses/LICENSE-2.0
 * <p/>
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package com.itemanalysis.psychometrics.factoranalysis;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;

import org.apache.commons.io.FileUtils;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.EigenDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.junit.Test;

import com.itemanalysis.psychometrics.measurement.DiagonalMatrix;

public class FactorAnalysisTest {

    /**
     * Correlation matrix from Harman74.cor$cov in R psych package.
     * 
     * @return
     */
    private double[][] readHarman74Data() {
        double[][] harman74 = new double[24][24];
        try {
            File f = FileUtils.toFile(this.getClass().getResource("/testdata/harman74.txt"));
            BufferedReader br = new BufferedReader(new FileReader(f));
            String line = "";
            String[] s = null;
            int row = 0;
            while ((line = br.readLine()) != null) {
                s = line.split(",");
                for (int j = 0; j < s.length; j++) {
                    harman74[row][j] = Double.parseDouble(s[j]);
                }
                row++;
            }
            br.close();
        } catch (IOException ex) {
            ex.printStackTrace();
        }
        return harman74;
    }

    @Test
    public void eigenValues() {
        RealMatrix R = new Array2DRowRealMatrix(readHarman74Data());

        int nFactors = 2;
        int nVariables = 24;
        double[] x = new double[nVariables];
        for (int i = 0; i < nVariables; i++) {
            x[i] = .5;
        }

        for (int i = 0; i < nVariables; i++) {
            R.setEntry(i, i, 1.0 - x[i]);
        }

        EigenDecomposition eigen = new EigenDecomposition(R);
        RealMatrix eigenVectors = eigen.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);

        double[] ev = new double[nFactors];
        for (int i = 0; i < nFactors; i++) {
            ev[i] = Math.sqrt(eigen.getRealEigenvalue(i));
        }
        DiagonalMatrix evMatrix = new DiagonalMatrix(ev);// USE Apache version
        // of Diagonal
        // matrix when
        // upgrade to
        // version 3.2
        RealMatrix LAMBDA = eigenVectors.multiply(evMatrix);
        RealMatrix SIGMA = (LAMBDA.multiply(LAMBDA.transpose()));
        RealMatrix RESID = R.subtract(SIGMA);

        double sum = 0.0;
        double squared = 0.0;
        for (int i = 0; i < RESID.getRowDimension(); i++) {
            for (int j = 0; j < RESID.getColumnDimension(); j++) {
                if (i != j) {
                    sum += Math.pow(RESID.getEntry(i, j), 2);
                }
            }
        }

        System.out.println(sum);

        // RealMatrix SIGMA = (LAMBDA.multiply(LAMBDA.transpose()));
        //
        // System.out.println("SIGMA");
        // for(int i=0;i<SIGMA.getRowDimension();i++){
        // for(int j=0;j<SIGMA.getColumnDimension();j++){
        // System.out.print(SIGMA.getEntry(i, j) + " ");
        // }
        // System.out.println();
        // }
    }
    /*
     * //@Ignore
     * 
     * @Test public void harmanTest(){ RealMatrix correlationMatrix = new
     * Array2DRowRealMatrix(readHarman74Data()); FactorAnalysis fa = new
     * FactorAnalysis(correlationMatrix, 2); fa.estimateParameters();
     * System.out.println(fa.printFactorLoadings());
     * 
     * 
     * 
     * }
     */
}