Package com.google.common.math

Class PairedStatsAccumulator

java.lang.Object

com.google.common.math.PairedStatsAccumulator

@Beta
@GwtIncompatible
public final class PairedStatsAccumulator
extends java.lang.Object

A mutable object which accumulates paired double values (e.g. points on a plane) and tracks some basic statistics over all the values added so far. This class is not thread safe.

Since:

20.0

Constructor Summary

Constructors

Constructor	Description
PairedStatsAccumulator()

Method Summary

Modifier and Type	Method	Description
void	add(double x, double y)	Adds the given pair of values to the dataset.
void	addAll(PairedStats values)	Adds the given statistics to the dataset, as if the individual values used to compute the statistics had been added directly.
long	count()	Returns the number of pairs in the dataset.
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.
PairedStats	snapshot()	Returns an immutable snapshot of the current statistics.
Stats	xStats()	Returns an immutable snapshot of the statistics on the x values alone.
Stats	yStats()	Returns an immutable snapshot of the statistics on the y values alone.

Methods inherited from class java.lang.Object

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

Constructor Detail

PairedStatsAccumulator

public PairedStatsAccumulator()

Method Detail

add

public void add(double x,
                double y)

Adds the given pair of values to the dataset.

addAll

public void addAll(PairedStats values)

Adds the given statistics to the dataset, as if the individual values used to compute the statistics had been added directly.

snapshot

public PairedStats snapshot()

Returns an immutable snapshot of the current statistics.

count

public long count()

Returns the number of pairs in the dataset.

xStats

public Stats xStats()

Returns an immutable snapshot of the statistics on the x values alone.

yStats

public Stats yStats()

Returns an immutable snapshot of 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 final 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 final 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 final 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 have zero population variance