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

Introduction

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

Prototype

public double estimateRegressandVariance() 

Source Link

Usage

From source file:com.mebigfatguy.damus.main.DamusCalculator.java

private boolean calcLinearRegression() {
    try {//from   w w  w .  j a va 2 s.  c o  m
        Context context = Context.instance();
        PredictionModel model = context.getPredictionModel();
        TrainingData data = context.getTrainingData();

        int numMetrics = model.getNumMetrics();
        int numResults = model.getNumResults();
        int numItems = data.getNumItems();

        OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();

        double[][] xx = new double[numItems][];
        for (int i = 0; i < numItems; i++) {
            double[] x = new double[numMetrics];
            for (int m = 0; m < numMetrics; m++) {
                Metric metric = model.getMetric(m);
                double value;
                switch (metric.getType()) {
                case Percent:
                    value = ((Number) data.getItem(i).getValue(metric)).doubleValue();
                    break;

                case Real:
                    value = ((BigDecimal) data.getItem(i).getValue(metric)).doubleValue();
                    break;

                case YesNo:
                    value = ((Boolean) data.getItem(i).getValue(metric)).booleanValue() ? 1.0 : 0.0;
                    break;

                default:
                    value = 0.0;
                    break;
                }
                x[m] = value;
            }
            xx[i] = x;
        }

        for (int r = 0; r < numResults; r++) {
            Metric metric = model.getResult(r);
            double[] y = new double[numItems];
            for (int i = 0; i < numItems; i++) {
                double value;
                switch (metric.getType()) {
                case Percent:
                    value = ((Number) data.getItem(i).getValue(metric)).doubleValue();
                    break;

                case Real:
                    value = ((BigDecimal) data.getItem(i).getValue(metric)).doubleValue();
                    break;

                case YesNo:
                    value = ((Boolean) data.getItem(i).getValue(metric)).booleanValue() ? 1.0 : 0.0;
                    break;

                default:
                    value = 0.0;
                    break;
                }
                y[i] = value;
            }

            regression.newSampleData(y, xx);
            double result = regression.estimateRegressandVariance();
            trainingItem.setValue(metric, new BigDecimal(result));
        }
        return true;
    } catch (Exception e) {
        return false;
    }
}

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

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

    Matrix observations = null;//  ww  w.j av a  2  s . com
    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. java  2s . c o m*/

    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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