Example usage for org.apache.commons.math3.stat.regression OLSMultipleLinearRegression estimateRegressionParametersVariance

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.regression OLSMultipleLinearRegression estimateRegressionParametersVariance.

Prototype

public double[][] estimateRegressionParametersVariance() 

Source Link

Usage

From source file:org.apache.solr.client.solrj.io.eval.OLSRegressionEvaluator.java

@Override
public Object doWork(Object... values) throws IOException {

    Matrix observations = null;//from  w w w  .  ja v a2 s . co m
    List<Number> outcomes = null;

    if (values[0] instanceof Matrix) {
        observations = (Matrix) values[0];
    } else {
        throw new IOException("The first parameter for olsRegress should be the observation matrix.");
    }

    if (values[1] instanceof List) {
        outcomes = (List) values[1];
    } else {
        throw new IOException("The second parameter for olsRegress should be outcome array. ");
    }

    double[][] observationData = observations.getData();
    double[] outcomeData = new double[outcomes.size()];
    for (int i = 0; i < outcomeData.length; i++) {
        outcomeData[i] = outcomes.get(i).doubleValue();
    }

    OLSMultipleLinearRegression multipleLinearRegression = (OLSMultipleLinearRegression) regress(
            observationData, outcomeData);

    Map map = new HashMap();

    map.put("regressandVariance", multipleLinearRegression.estimateRegressandVariance());
    map.put("regressionParameters", list(multipleLinearRegression.estimateRegressionParameters()));
    map.put("RSquared", multipleLinearRegression.calculateRSquared());
    map.put("adjustedRSquared", multipleLinearRegression.calculateAdjustedRSquared());
    map.put("residualSumSquares", multipleLinearRegression.calculateResidualSumOfSquares());

    try {
        map.put("regressionParametersStandardErrors",
                list(multipleLinearRegression.estimateRegressionParametersStandardErrors()));
        map.put("regressionParametersVariance",
                new Matrix(multipleLinearRegression.estimateRegressionParametersVariance()));
    } catch (Exception e) {
        //Exception is thrown if the matrix is singular
    }

    return new MultipleRegressionTuple(multipleLinearRegression, map);
}

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From source file:org.opentestsystem.airose.regression.ols.OLSRegressionModeller.java

protected AbstractModel customProcessData(double[] y, double[][] x) {
    OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
    regression.newSampleData(y, x);//w  w w . jav  a2  s .  c om

    double[] beta = regression.estimateRegressionParameters();
    double[] residuals = regression.estimateResiduals();
    double[][] parametersVariance = regression.estimateRegressionParametersVariance();
    double regressandVariance = regression.estimateRegressandVariance();
    double rSquared = regression.calculateRSquared();
    double sigma = regression.estimateRegressionStandardError();

    OLSModel olsModel = new OLSModel(beta, residuals, parametersVariance, regressandVariance, rSquared, sigma,
            y, x);
    return olsModel;
}

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