com.datumbox.framework.core.statistics.descriptivestatistics

Class Descriptives

java.lang.Object

com.datumbox.framework.core.statistics.descriptivestatistics.Descriptives

public class Descriptives
extends java.lang.Object

This class provides several methods to estimate Descriptive Statistics about a particular list of values.

Constructor Summary

Constructors

Constructor and Description
Descriptives()

Method Summary

Modifier and Type	Method and Description
static double	autocorrelation(FlatDataList flatDataList, int lags) Calculates the autocorrelation of a flatDataCollection for a predifined lag
static int	count(java.lang.Iterable it) Returns the number of not-null items in the iteratable.
static double	covariance(TransposeDataList transposeDataList, boolean isSample) Calculates the covariance for a given transposed array (2xn table)
static double	cv(double std, double mean) Calculates Coefficient of variation
static AssociativeArray	frequencies(FlatDataCollection flatDataCollection) Calculates the Frequency Table
static double	geometricMean(FlatDataCollection flatDataCollection) Calculates Geometric Mean
static double	harmonicMean(FlatDataCollection flatDataCollection) Calculates Harmonic Mean
static double	kurtosis(FlatDataCollection flatDataCollection) Calculates Kurtosis.
static double	kurtosisSE(FlatDataCollection flatDataCollection) Calculates Standard Error of Kurtosis.
static double	max(FlatDataCollection flatDataCollection) Calculates Maximum.
static double	maxAbsolute(FlatDataCollection flatDataCollection) Calculates Maximum absolute value.
static double	mean(FlatDataCollection flatDataCollection) Calculates the simple mean
static double	meanSE(FlatDataCollection flatDataCollection) Calculates Standard Error of Mean under SRS
static double	median(FlatDataCollection flatDataCollection) Calculates the median.
static double	min(FlatDataCollection flatDataCollection) Calculates Minimum.
static double	minAbsolute(FlatDataCollection flatDataCollection) Calculates Minimum absolute value.
static FlatDataCollection	mode(FlatDataCollection flatDataCollection) Calculates the modes (more than one if found).
static double	moment(FlatDataCollection flatDataCollection, int r) Calculates Moment R if the mean is not known.
static double	moment(FlatDataCollection flatDataCollection, int r, double mean) Calculates Moment R if the mean is known.
static void	normalize(AssociativeArray associativeArray) Normalizes the provided associative array by dividing its values with the sum of the observations.
static void	normalizeExp(AssociativeArray associativeArray) Normalizes the exponentials of provided associative array by using the log-sum-exp trick.
static AssociativeArray	percentiles(FlatDataCollection flatDataCollection, int cutPoints) Calculates the percentiles given a number of cutPoints
static AssociativeArray	quartiles(FlatDataCollection flatDataCollection) Calculates the quartiles
static double	range(FlatDataCollection flatDataCollection) Calculates Range
static double	skewness(FlatDataCollection flatDataCollection) Calculates Skewness.
static double	skewnessSE(FlatDataCollection flatDataCollection) Calculates Standard Error of Skweness.
static double	std(FlatDataCollection flatDataCollection, boolean isSample) Calculates the Standard Deviation
static double	sum(FlatDataCollection flatDataCollection) Returns the sum of a Collection
static double	variance(FlatDataCollection flatDataCollection, boolean isSample) Calculates the Variance

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Descriptives

public Descriptives()

Method Detail

count

public static int count(java.lang.Iterable it)

Returns the number of not-null items in the iteratable.

Parameters:

it -

Returns:

sum

public static double sum(FlatDataCollection flatDataCollection)

Returns the sum of a Collection

Parameters:

flatDataCollection -

Returns:

mean

public static double mean(FlatDataCollection flatDataCollection)

Calculates the simple mean

Parameters:

flatDataCollection -

Returns:

meanSE

public static double meanSE(FlatDataCollection flatDataCollection)

Calculates Standard Error of Mean under SRS

Parameters:

flatDataCollection -

Returns:

median

public static double median(FlatDataCollection flatDataCollection)

Calculates the median.

Parameters:

flatDataCollection -

Returns:

min

public static double min(FlatDataCollection flatDataCollection)

Calculates Minimum.

Parameters:

flatDataCollection -

Returns:

max

public static double max(FlatDataCollection flatDataCollection)

Calculates Maximum.

Parameters:

flatDataCollection -

Returns:

minAbsolute

public static double minAbsolute(FlatDataCollection flatDataCollection)

Calculates Minimum absolute value.

Parameters:

flatDataCollection -

Returns:

maxAbsolute

public static double maxAbsolute(FlatDataCollection flatDataCollection)

Calculates Maximum absolute value.

Parameters:

flatDataCollection -

Returns:

range

public static double range(FlatDataCollection flatDataCollection)

Calculates Range

Parameters:

flatDataCollection -

Returns:

geometricMean

public static double geometricMean(FlatDataCollection flatDataCollection)

Calculates Geometric Mean

Parameters:

flatDataCollection -

Returns:

harmonicMean

public static double harmonicMean(FlatDataCollection flatDataCollection)

Calculates Harmonic Mean

Parameters:

flatDataCollection -

Returns:

variance

public static double variance(FlatDataCollection flatDataCollection,
                              boolean isSample)

Calculates the Variance

Parameters:

flatDataCollection -

isSample -

Returns:

std

public static double std(FlatDataCollection flatDataCollection,
                         boolean isSample)

Calculates the Standard Deviation

Parameters:

flatDataCollection -

isSample -

Returns:

cv

public static double cv(double std,
                        double mean)

Calculates Coefficient of variation

Parameters:

std -

mean -

Returns:

moment

public static double moment(FlatDataCollection flatDataCollection,
                            int r)

Calculates Moment R if the mean is not known.

Parameters:

flatDataCollection -

r -

Returns:

moment

public static double moment(FlatDataCollection flatDataCollection,
                            int r,
                            double mean)

Calculates Moment R if the mean is known.

Parameters:

flatDataCollection -

r -

mean -

Returns:

kurtosis

public static double kurtosis(FlatDataCollection flatDataCollection)

Calculates Kurtosis. Uses a formula similar to SPSS as suggested in their documentation (local help)

Parameters:

flatDataCollection -

Returns:

kurtosisSE

public static double kurtosisSE(FlatDataCollection flatDataCollection)

Calculates Standard Error of Kurtosis. Uses a formula similar to SPSS as suggested by http://suite101.com/article/kurtosis-and-how-it-is-calculated-by-statistics-software-packages-a235641

Parameters:

flatDataCollection -

Returns:

skewness

public static double skewness(FlatDataCollection flatDataCollection)

Calculates Skewness. Uses a formula as suggested by http://en.wikipedia.org/wiki/Skewness

Parameters:

flatDataCollection -

Returns:

skewnessSE

public static double skewnessSE(FlatDataCollection flatDataCollection)

Calculates Standard Error of Skweness. Uses a formula as suggested by http://en.wikipedia.org/wiki/Skewness

Parameters:

flatDataCollection -

Returns:

percentiles

public static AssociativeArray percentiles(FlatDataCollection flatDataCollection,
                                           int cutPoints)

Calculates the percentiles given a number of cutPoints

Parameters:

flatDataCollection -

cutPoints -

Returns:

quartiles

public static AssociativeArray quartiles(FlatDataCollection flatDataCollection)

Calculates the quartiles

Parameters:

flatDataCollection -

Returns:

covariance

public static double covariance(TransposeDataList transposeDataList,
                                boolean isSample)

Calculates the covariance for a given transposed array (2xn table)

Parameters:

transposeDataList -

isSample -

Returns:

autocorrelation

public static double autocorrelation(FlatDataList flatDataList,
                                     int lags)

Calculates the autocorrelation of a flatDataCollection for a predifined lag

Parameters:

flatDataList -

lags -

Returns:

frequencies

public static AssociativeArray frequencies(FlatDataCollection flatDataCollection)

Calculates the Frequency Table

Parameters:

flatDataCollection -

Returns:

mode

public static FlatDataCollection mode(FlatDataCollection flatDataCollection)

Calculates the modes (more than one if found).

Parameters:

flatDataCollection -

Returns:

normalize

public static void normalize(AssociativeArray associativeArray)

Normalizes the provided associative array by dividing its values with the sum of the observations.

Parameters:

associativeArray -

normalizeExp

public static void normalizeExp(AssociativeArray associativeArray)

Normalizes the exponentials of provided associative array by using the log-sum-exp trick.

Parameters:

associativeArray -