com.itemanalysis.psychometrics.cfa.GeneralizedLeastSquares.java Source code

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Here is the source code for com.itemanalysis.psychometrics.cfa.GeneralizedLeastSquares.java

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/*
 * Copyright 2012 J. Patrick Meyer
 *
 * 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
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.cfa;

import com.itemanalysis.psychometrics.statistics.IdentityMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.log4j.Logger;

/**
 *
 * @author J. Patrick Meyer <meyerjp at itemanalysis.com>
 */
@Deprecated
public class GeneralizedLeastSquares extends AbstractConfirmatoryFactorAnalysisEstimator {

    static Logger logger = Logger.getLogger("jmetrik-logger");

    public GeneralizedLeastSquares(ConfirmatoryFactorAnalysisModel model, RealMatrix varcov,
            double numberOfExaminees) {
        super(model, varcov, numberOfExaminees);
    }

    public double evaluate(double[] argument, double[] gradient) {
        F = value(argument);
        return F;
    }

    /**
     * 
     *
     * @param argument
     * @return
     */
    public double value(double[] argument) {
        SIGMA = model.getImpliedCovariance(argument);
        RealMatrix D = (varcov.subtract(SIGMA)).multiply(VCinv);
        RealMatrix D2 = D.multiply(D);
        F = 0.5 * D2.getTrace();
        return F;
    }

    public double gfi() {
        double fit = 0.0;
        double q = Double.valueOf(model.getNumberOfItems()).doubleValue();
        RealMatrix I = new IdentityMatrix(nItems);
        RealMatrix P = SIGMA.multiply(VCinv);
        RealMatrix D = I.subtract(P);
        RealMatrix D2 = D.multiply(D);
        fit = 1.0 - D2.getTrace() / q;
        return fit;
    }

    public double agfi() {
        double gfi = gfi();
        double q = Double.valueOf(model.getNumberOfItems()).doubleValue();
        double p1 = (q * (q + 1)) / (2.0 * degreesOfFreedom());
        double p2 = 1.0 - gfi;
        double fit = 1.0 - p1 * p2;
        return fit;
    }

    /**
     * Gradient computation
     *
     * @param x
     * @return
     */
    public double[] derivativeAt(double[] x) {
        double[] g = null;
        SIGMA = model.getImpliedCovariance(x);
        RealMatrix D = SIGMA.subtract(varcov);
        RealMatrix derLambda = VCinv.multiply(D).multiply(VCinv).multiply(model.getBeta(x)).scalarMultiply(2.0);
        RealMatrix derError = VCinv.multiply(D).multiply(VCinv);
        g = model.getGradient(derLambda, derError);
        return g;
    }

    public int domainDimension() {
        return model.getNumberOfParameters();
    }

    //    replaced by domainDimension
    //    public int getNumArguments(){
    //      return model.getNumberOfParameters();
    //   }

}