Package com.google.common.math

Class PairedStats

java.lang.Object

com.google.common.math.PairedStats

All Implemented Interfaces:

java.io.Serializable

@Beta
@GwtIncompatible
public final class PairedStats
extends java.lang.Object
implements java.io.Serializable

An immutable value object capturing some basic statistics about a collection of paired double values (e.g. points on a plane). Build instances with PairedStatsAccumulator.snapshot().

Since:

20.0

See Also:

Serialized Form

Method Summary

Modifier and Type	Method	Description
long	count()	Returns the number of pairs in the dataset.
boolean	equals(java.lang.Object obj)
static PairedStats	fromByteArray(byte[] byteArray)	Creates a PairedStats instance from the given byte representation which was obtained by toByteArray().
int	hashCode()
LinearTransformation	leastSquaresFit()	Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression of y as a function of x.
double	pearsonsCorrelationCoefficient()	Returns the Pearson's or product-moment correlation coefficient of the values.
double	populationCovariance()	Returns the population covariance of the values.
double	sampleCovariance()	Returns the sample covariance of the values.
byte[]	toByteArray()	Gets a byte array representation of this instance.
java.lang.String	toString()
Stats	xStats()	Returns the statistics on the x values alone.
Stats	yStats()	Returns the statistics on the y values alone.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Method Detail

count

public long count()

Returns the number of pairs in the dataset.

xStats

public Stats xStats()

Returns the statistics on the x values alone.

yStats

public Stats yStats()

Returns the statistics on the y values alone.

populationCovariance

public double populationCovariance()

Returns the population covariance of the values. The count must be non-zero.

This is guaranteed to return zero if the dataset contains a single pair of finite values. It is not guaranteed to return zero when the dataset consists of the same pair of values multiple times, due to numerical errors.

Non-finite values

If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.

Throws:

java.lang.IllegalStateException - if the dataset is empty

sampleCovariance

public double sampleCovariance()

Returns the sample covariance of the values. The count must be greater than one.

This is not guaranteed to return zero when the dataset consists of the same pair of values multiple times, due to numerical errors.

Non-finite values

If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.

Throws:

java.lang.IllegalStateException - if the dataset is empty or contains a single pair of values

pearsonsCorrelationCoefficient

public double pearsonsCorrelationCoefficient()

Returns the Pearson's or product-moment correlation coefficient of the values. The count must greater than one, and the x and y values must both have non-zero population variance (i.e. xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0). The result is not guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

Non-finite values

If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is Double.NaN.

Throws:

java.lang.IllegalStateException - if the dataset is empty or contains a single pair of values, or either the x and y dataset has zero population variance

leastSquaresFit

public LinearTransformation leastSquaresFit()

Returns a linear transformation giving the best fit to the data according to Ordinary Least Squares linear regression of y as a function of x. The count must be greater than one, and either the x or y data must have a non-zero population variance (i.e. xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0). The result is guaranteed to be horizontal if there is variance in the x data but not the y data, and vertical if there is variance in the y data but not the x data.

This fit minimizes the root-mean-square error in y as a function of x. This error is defined as the square root of the mean of the squares of the differences between the actual y values of the data and the values predicted by the fit for the x values (i.e. it is the square root of the mean of the squares of the vertical distances between the data points and the best fit line). For this fit, this error is a fraction sqrt(1 - R*R) of the population standard deviation of y, where R is the Pearson's correlation coefficient (as given by pearsonsCorrelationCoefficient()).

The corresponding root-mean-square error in x as a function of y is a fraction sqrt(1/(R*R) - 1) of the population standard deviation of x. This fit does not normally minimize that error: to do that, you should swap the roles of x and y.

Non-finite values

If the dataset contains any non-finite values (Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, or Double.NaN) then the result is LinearTransformation.forNaN().

Throws:

java.lang.IllegalStateException - if the dataset is empty or contains a single pair of values, or both the x and y dataset must have zero population variance

equals

public boolean equals(@Nullable
                      java.lang.Object obj)

Note: This tests exact equality of the calculated statistics, including the floating point values. It is definitely true for instances constructed from exactly the same values in the same order. It is also true for an instance round-tripped through java serialization. However, floating point rounding errors mean that it may be false for some instances where the statistics are mathematically equal, including the same values in a different order.

Overrides:

equals in class java.lang.Object

hashCode

public int hashCode()

Note: This hash code is consistent with exact equality of the calculated statistics, including the floating point values. See the note on equals(java.lang.Object) for details.

Overrides:

hashCode in class java.lang.Object

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

toByteArray

public byte[] toByteArray()

Gets a byte array representation of this instance.

Note: No guarantees are made regarding stability of the representation between versions.

fromByteArray

public static PairedStats fromByteArray(byte[] byteArray)

Creates a PairedStats instance from the given byte representation which was obtained by toByteArray().

Note: No guarantees are made regarding stability of the representation between versions.