org.apache.commons.math4.distribution

## Class FDistribution

• ### Nested classes/interfaces inherited from interface org.apache.commons.math4.distribution.RealDistribution

RealDistribution.Sampler
• ### Field Summary

Fields
Modifier and Type Field and Description
static double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
• ### Fields inherited from class org.apache.commons.math4.distribution.AbstractRealDistribution

SOLVER_DEFAULT_ABSOLUTE_ACCURACY
• ### Constructor Summary

Constructors
Constructor and Description
FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom)
Creates a using the given degrees of freedom.
FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)
Creates a distribution.
• ### Method Summary

All Methods
Modifier and Type Method and Description
protected double calculateNumericalVariance()
double cumulativeProbability(double x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
double density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point x.
double getDenominatorDegreesOfFreedom()
Access the denominator degrees of freedom.
double getNumeratorDegreesOfFreedom()
Access the numerator degrees of freedom.
double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution.
double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution.
protected double getSolverAbsoluteAccuracy()
Returns the solver absolute accuracy for inverse cumulative computation.
double getSupportLowerBound()
Access the lower bound of the support.
double getSupportUpperBound()
Access the upper bound of the support.
boolean isSupportConnected()
Use this method to get information about whether the support is connected, i.e.
double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.
• ### Methods inherited from class org.apache.commons.math4.distribution.AbstractRealDistribution

createSampler, inverseCumulativeProbability, probability, probability, sample
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Field Detail

• #### DEFAULT_INVERSE_ABSOLUTE_ACCURACY

public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
Default inverse cumulative probability accuracy.
Since:
2.1
Constant Field Values
• ### Constructor Detail

• #### FDistribution

public FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
throws NotStrictlyPositiveException
Creates a using the given degrees of freedom.
Parameters:
numeratorDegreesOfFreedom - Numerator degrees of freedom.
denominatorDegreesOfFreedom - Denominator degrees of freedom.
Throws:
NotStrictlyPositiveException - if numeratorDegreesOfFreedom <= 0 or denominatorDegreesOfFreedom <= 0.
• #### FDistribution

public FDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom,
double inverseCumAccuracy)
throws NotStrictlyPositiveException
Creates a distribution.
Parameters:
numeratorDegreesOfFreedom - Numerator degrees of freedom.
denominatorDegreesOfFreedom - Denominator degrees of freedom.
inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates.
Throws:
NotStrictlyPositiveException - if numeratorDegreesOfFreedom <= 0 or denominatorDegreesOfFreedom <= 0.
• ### Method Detail

• #### density

public double density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the CDF. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.
Parameters:
x - the point at which the PDF is evaluated
Returns:
the value of the probability density function at point x
Since:
2.1
• #### logDensity

public double logDensity(double x)
Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the CDF. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of RealDistribution.density(double).

The default implementation simply computes the logarithm of density(x).

Specified by:
logDensity in interface RealDistribution
Overrides:
logDensity in class AbstractRealDistribution
Parameters:
x - the point at which the PDF is evaluated
Returns:
the logarithm of the value of the probability density function at point x
• #### cumulativeProbability

public double cumulativeProbability(double x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution. The implementation of this method is based on
Parameters:
x - the point at which the CDF is evaluated
Returns:
the probability that a random variable with this distribution takes a value less than or equal to x
• #### getNumeratorDegreesOfFreedom

public double getNumeratorDegreesOfFreedom()
Access the numerator degrees of freedom.
Returns:
the numerator degrees of freedom.
• #### getDenominatorDegreesOfFreedom

public double getDenominatorDegreesOfFreedom()
Access the denominator degrees of freedom.
Returns:
the denominator degrees of freedom.
• #### getSolverAbsoluteAccuracy

protected double getSolverAbsoluteAccuracy()
Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
Overrides:
getSolverAbsoluteAccuracy in class AbstractRealDistribution
Returns:
the maximum absolute error in inverse cumulative probability estimates
• #### getNumericalMean

public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. For denominator degrees of freedom parameter b, the mean is
• if b > 2 then b / (b - 2),
• else undefined (Double.NaN).
Returns:
the mean or Double.NaN if it is not defined
• #### getNumericalVariance

public double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution. For numerator degrees of freedom parameter a and denominator degrees of freedom parameter b, the variance is
• if b > 4 then [2 * b^2 * (a + b - 2)] / [a * (b - 2)^2 * (b - 4)],
• else undefined (Double.NaN).
Returns:
the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistribution) or Double.NaN if it is not defined
• #### calculateNumericalVariance

protected double calculateNumericalVariance()
Returns:
the variance of this distribution
• #### getSupportLowerBound

public double getSupportLowerBound()
Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

inf {x in R | P(X <= x) > 0}.

The lower bound of the support is always 0 no matter the parameters.
Returns:
lower bound of the support (always 0)
• #### getSupportUpperBound

public double getSupportUpperBound()
Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

inf {x in R | P(X <= x) = 1}.

The upper bound of the support is always positive infinity no matter the parameters.
Returns:
upper bound of the support (always Double.POSITIVE_INFINITY)
• #### isSupportConnected

public boolean isSupportConnected()
Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
Returns:
true