Examples of NaturalRanking

org.apache.commons.math.stat.ranking.NaturalRanking

Ranking based on the natural ordering on doubles.

NaNs are treated according to the configured {@link NaNStrategy} and tiesare handled using the selected {@link TiesStrategy}. Configuration settings are supplied in optional constructor arguments. Defaults are {@link NaNStrategy#MAXIMAL} and {@link TiesStrategy#AVERAGE}, respectively. When using {@link TiesStrategy#RANDOM}, a {@link RandomGenerator} may be supplied as a constructor argument.

Examples:

Input data: (20, 17, 30, 42.3, 17, 50, Double.NaN, Double.NEGATIVE_INFINITY, 17)
NaNStrategy	TiesStrategy	`rank(data)`
default (NaNs maximal)	default (ties averaged)	(5, 3, 6, 7, 3, 8, 9, 1, 3)
default (NaNs maximal)	MINIMUM	(5, 2, 6, 7, 2, 8, 9, 1, 2)
MINIMAL	default (ties averaged)	(6, 4, 7, 8, 4, 9, 1.5, 1.5, 4)
REMOVED	SEQUENTIAL	(5, 2, 6, 7, 3, 8, 1, 4)
MINIMAL	MAXIMUM	(6, 5, 7, 8, 5, 9, 2, 2, 5)

@since 2.0 @version $Revision: 1061496 $ $Date: 2011-01-20 21:32:16 +0100 (jeu. 20 janv. 2011) $

org.apache.commons.math3.stat.ranking.NaturalRanking

Ranking based on the natural ordering on doubles.

NaNs are treated according to the configured {@link NaNStrategy} and tiesare handled using the selected {@link TiesStrategy}. Configuration settings are supplied in optional constructor arguments. Defaults are {@link NaNStrategy#FAILED} and {@link TiesStrategy#AVERAGE}, respectively. When using {@link TiesStrategy#RANDOM}, a {@link RandomGenerator} may be supplied as a constructor argument.

Examples:

Input data: (20, 17, 30, 42.3, 17, 50, Double.NaN, Double.NEGATIVE_INFINITY, 17)
NaNStrategy	TiesStrategy	`rank(data)`
default (NaNs maximal)	default (ties averaged)	(5, 3, 6, 7, 3, 8, 9, 1, 3)
default (NaNs maximal)	MINIMUM	(5, 2, 6, 7, 2, 8, 9, 1, 2)
MINIMAL	default (ties averaged)	(6, 4, 7, 8, 4, 9, 1.5, 1.5, 4)
REMOVED	SEQUENTIAL	(5, 2, 6, 7, 3, 8, 1, 4)
MINIMAL	MAXIMUM	(6, 5, 7, 8, 5, 9, 2, 2, 5)

@since 2.0

Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     *
     * @param dataMatrix matrix of data with columns representing
     * variables to correlate
     */
    public SpearmansCorrelation(final RealMatrix dataMatrix) {
        this(dataMatrix, new NaturalRanking());
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     * Create a test instance where NaN's are left in place and ties get
     * the average of applicable ranks. Use this unless you are very sure
     * of what you are doing.
     */
    public WilcoxonSignedRankTest() {
        naturalRanking = new NaturalRanking(NaNStrategy.FIXED,
                TiesStrategy.AVERAGE);
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     * @param tiesStrategy
     *            specifies the strategy that should be used for ties
     */
    public WilcoxonSignedRankTest(final NaNStrategy nanStrategy,
                                  final TiesStrategy tiesStrategy) {
        naturalRanking = new NaturalRanking(nanStrategy, tiesStrategy);
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     * Create a test instance using where NaN's are left in place and ties get
     * the average of applicable ranks. Use this unless you are very sure of
     * what you are doing.
     */
    public MannWhitneyUTest() {
        naturalRanking = new NaturalRanking(NaNStrategy.FIXED,
                TiesStrategy.AVERAGE);
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     * @param tiesStrategy
     *            specifies the strategy that should be used for ties
     */
    public MannWhitneyUTest(final NaNStrategy nanStrategy,
                            final TiesStrategy tiesStrategy) {
        naturalRanking = new NaturalRanking(nanStrategy, tiesStrategy);
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

    @Test
    public void testMath891Array() {
        final double[] xArray = new double[] { Double.NaN, 1.9, 2, 100, 3 };
        final double[] yArray = new double[] { 10, 2, 10, Double.NaN, 4 };


        NaturalRanking ranking = new NaturalRanking(NaNStrategy.REMOVED);
        SpearmansCorrelation spearman = new SpearmansCorrelation(ranking);
        
        Assert.assertEquals(0.5, spearman.correlation(xArray, yArray), Double.MIN_VALUE);
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

            matrix.addToEntry(i, 0, xArray[i]);
            matrix.addToEntry(i, 1, yArray[i]);
        }


        // compute correlation
        NaturalRanking ranking = new NaturalRanking(NaNStrategy.REMOVED);
        SpearmansCorrelation spearman = new SpearmansCorrelation(matrix, ranking);
        
        Assert.assertEquals(0.5, spearman.getCorrelationMatrix().getEntry(0, 1), Double.MIN_VALUE);
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     *
     * @param dataMatrix matrix of data with columns representing
     * variables to correlate
     */
    public SpearmansCorrelation(final RealMatrix dataMatrix) {
        this(dataMatrix, new NaturalRanking());
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

    /**
     * Create a SpearmansCorrelation without data.
     */
    public SpearmansCorrelation() {
        data = null;
        this.rankingAlgorithm = new NaturalRanking();
        rankCorrelation = null;
    }

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Examples of org.apache.commons.math3.stat.ranking.NaturalRanking

     * Create a test instance using where NaN's are left in place and ties get
     * the average of applicable ranks. Use this unless you are very sure of
     * what you are doing.
     */
    public MannWhitneyUTest() {
        naturalRanking = new NaturalRanking(NaNStrategy.FIXED,
                TiesStrategy.AVERAGE);
    }

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