Examples of org.apache.commons.math3.stat.regression.SimpleRegression

Package org.apache.commons.math3.stat.regression

Examples of org.apache.commons.math3.stat.regression.SimpleRegression

org.apache.commons.math3.stat.regression.SimpleRegression
Estimates an ordinary least squares regression model with one independent variable.
y = intercept + slope * x

Standard errors for intercept and slope are available as well as ANOVA, r-square and Pearson's r statistics.

Observations (x,y pairs) can be added to the model one at a time or they can be provided in a 2-dimensional array. The observations are not stored in memory, so there is no limit to the number of observations that can be added to the model.

Usage Notes:
- When there are fewer than two observations in the model, or when there is no variation in the x values (i.e. all x values are the same) all statistics return NaN. At least two observations with different x coordinates are required to estimate a bivariate regression model.
- Getters for the statistics always compute values based on the current set of observations -- i.e., you can get statistics, then add more data and get updated statistics without using a new instance. There is no "compute" method that updates all statistics. Each of the getters performs the necessary computations to return the requested statistic.
- The intercept term may be suppressed by passing {@code false} tothe {@link #SimpleRegression(boolean)} constructor. When the{@code hasIntercept} property is false, the model is estimated without aconstant term and {@link #getIntercept()} returns {@code 0}.

     * @param upper Upper bound (inclusive) of this distribution.
     * @throws NumberIsTooLargeException if {@code lower >= upper}.
     */
    public UniformIntegerDistribution(int lower, int upper)
        throws NumberIsTooLargeException {
        this(new Well19937c(), lower, upper);
    }

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     * @throws NumberIsTooLargeException if {@code a >= b} or if {@code c > b}.
     * @throws NumberIsTooSmallException if {@code c < a}.
     */
    public TriangularDistribution(double a, double c, double b)
        throws NumberIsTooLargeException, NumberIsTooSmallException {
        this(new Well19937c(), a, c, b);
    }

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            dest.meanImpl = new Mean(dest.secondMoment);
        } else {
            dest.meanImpl = source.meanImpl.copy();
        }
        if (source.getGeoMeanImpl() instanceof GeometricMean) {
            dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl);
        } else {
            dest.geoMeanImpl = source.geoMeanImpl.copy();
        }


        // Make sure that if stat == statImpl in source, same

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            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);
        } else {
            dest.meanImpl = source.meanImpl.copy();
        }
        if (source.getGeoMeanImpl() instanceof GeometricMean) {
            dest.geoMeanImpl = new GeometricMean((SumOfLogs) dest.sumLogImpl);

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     * <p>Double.NaN is returned if no values have been added.</p>
     *
     * @return the population variance
     */
    public double getPopulationVariance() {
        Variance populationVariance = new Variance(secondMoment);
        populationVariance.setBiasCorrected(false);
        return populationVariance.getResult();
    }

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        dest.secondMoment = source.secondMoment.copy();
        dest.n = source.n;


        // Keep commons-math supplied statistics with embedded moments in synch
        if (source.getVarianceImpl() instanceof Variance) {
            dest.varianceImpl = new Variance(dest.secondMoment);
        } else {
            dest.varianceImpl = source.varianceImpl.copy();
        }
        if (source.meanImpl instanceof Mean) {
            dest.meanImpl = new Mean(dest.secondMoment);

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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =

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        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

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     * @return Returns Pearson's correlation coefficient for the two arrays
     * @throws DimensionMismatchException if the arrays lengths do not match
     * @throws MathIllegalArgumentException if there is insufficient data
     */
    public double correlation(final double[] xArray, final double[] yArray) {
        SimpleRegression regression = new SimpleRegression();
        if (xArray.length != yArray.length) {
            throw new DimensionMismatchException(xArray.length, yArray.length);
        } else if (xArray.length < 2) {
            throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_DIMENSION,
                                                   xArray.length, 2);
        } else {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
    }

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    /**
     * Directly solve the linear problem, using the {@link SimpleRegression}
     * class.
     */
    public double[] solve() {
        final SimpleRegression regress = new SimpleRegression(true);
        for (double[] d : points) {
            regress.addData(d[0], d[1]);
        }


        final double[] result = { regress.getSlope(), regress.getIntercept() };
        return result;
    }

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Related Classes of org.apache.commons.math3.stat.regression.SimpleRegression

org.apache.commons.math3.analysis.differentiation.DerivativeStructure

org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

org.apache.commons.math3.analysis.function.HarmonicOscillator

org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.complex.Complex

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