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

Introduction

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

Prototype

public double calculateTotalSumOfSquares() throws MathIllegalArgumentException 

Source Link

Document

Returns the sum of squared deviations of Y from its mean.

If the model has no intercept term, 0 is used for the mean of Y - i.e., what is returned is the sum of the squared Y values.

The value returned by this method is the SSTO value used in the #calculateRSquared() R-squared computation.

Usage

From source file:modelcreation.ModelCreation.java

public static void printRegressionStatistics(OLSMultipleLinearRegression regression) {
    System.out.println("Adjusted R^2 = " + regression.calculateAdjustedRSquared());
    System.out.println("R^2 = " + regression.calculateRSquared());
    System.out.println("Residual Sum Of Squares = " + regression.calculateResidualSumOfSquares());
    System.out.println("Total Sum of Squares = " + regression.calculateTotalSumOfSquares());

    double[] standardErrors = regression.estimateRegressionParametersStandardErrors();
    double[] residuals = regression.estimateResiduals();
    double[] parameters = regression.estimateRegressionParameters();

    int residualdf = residuals.length - parameters.length;
    for (int i = 0; i < parameters.length; i++) {
        double coeff = parameters[i];
        double tstat = parameters[i] / regression.estimateRegressionParametersStandardErrors()[i];
        double pvalue = new TDistribution(residualdf).cumulativeProbability(-FastMath.abs(tstat)) * 2;

        System.out.println("Coefficient(" + i + ") : " + coeff);
        System.out.println("Standard Error(" + i + ") : " + standardErrors[i]);
        System.out.println("t-stats(" + i + ") : " + tstat);
        System.out.println("p-value(" + i + ") : " + pvalue);
    }//www .  j ava 2  s  . co  m
}

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From source file:modelcreation.ModelCreation.java

public static double evaluateModel(OLSMultipleLinearRegression regression, double[][] subXTest,
        double[] subYTest) {
    System.out.println("Adjusted R^2 = " + regression.calculateAdjustedRSquared());
    System.out.println("R^2 = " + regression.calculateRSquared());
    System.out.println("Residual Sum Of Squares = " + regression.calculateResidualSumOfSquares());
    System.out.println("Total Sum of Squares = " + regression.calculateTotalSumOfSquares());

    double[] parameters = regression.estimateRegressionParameters();
    double[] predictions = new double[subYTest.length];

    for (int i = 0; i < subYTest.length; i++) {
        double prediction = parameters[0] + (parameters[1] * subXTest[i][0]) + (parameters[2] * subXTest[i][1]);
        predictions[i] = prediction;//from  w  w  w .j  ava2  s  .  c  o  m
    }

    double meanSquaredError = calculateMeanSquaredError(subYTest, predictions);
    System.out.println("Mean Squared Error = " + meanSquaredError);
    return meanSquaredError;
}

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