Package org.apache.commons.math.distribution

Class TDistributionImpl

java.lang.Object

org.apache.commons.math.distribution.AbstractDistribution

org.apache.commons.math.distribution.AbstractContinuousDistribution

org.apache.commons.math.distribution.TDistributionImpl

All Implemented Interfaces:

java.io.Serializable, ContinuousDistribution, Distribution, TDistribution

public class TDistributionImpl
extends AbstractContinuousDistribution
implements TDistribution, java.io.Serializable

Default implementation of TDistribution.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
static double	DEFAULT_INVERSE_ABSOLUTE_ACCURACY	Default inverse cumulative probability accuracy

Constructor Summary

Constructors

Constructor	Description
TDistributionImpl(double degreesOfFreedom)	Create a t distribution using the given degrees of freedom.
TDistributionImpl(double degreesOfFreedom, double inverseCumAccuracy)	Create a t distribution using the given degrees of freedom and the specified inverse cumulative probability absolute accuracy.

Method Summary

Modifier and Type	Method	Description
double	cumulativeProbability(double x)	For this distribution, X, this method returns P(X < x).
double	density(double x)	Returns the probability density for a particular point.
double	getDegreesOfFreedom()	Access the degrees of freedom.
double	getNumericalMean()	Returns the mean.
double	getNumericalVariance()	Returns the variance.
double	getSupportLowerBound()	Returns the lower bound of the support for the distribution.
double	getSupportUpperBound()	Returns the upper bound of the support for the distribution.
double	inverseCumulativeProbability(double p)	For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
void	setDegreesOfFreedom(double degreesOfFreedom)	Deprecated. as of 2.1 (class will become immutable in 3.0)

Methods inherited from class org.apache.commons.math.distribution.AbstractContinuousDistribution

reseedRandomGenerator, sample, sample

Methods inherited from class org.apache.commons.math.distribution.AbstractDistribution

cumulativeProbability

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.commons.math.distribution.Distribution

cumulativeProbability

Field Detail

DEFAULT_INVERSE_ABSOLUTE_ACCURACY

public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY

Default inverse cumulative probability accuracy

Since:

2.1

See Also:

Constant Field Values

Constructor Detail

TDistributionImpl

public TDistributionImpl(double degreesOfFreedom,
                         double inverseCumAccuracy)

Create a t distribution using the given degrees of freedom and the specified inverse cumulative probability absolute accuracy.

Parameters:

degreesOfFreedom - the degrees of freedom.

inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY)

Since:

2.1

TDistributionImpl

public TDistributionImpl(double degreesOfFreedom)

Create a t distribution using the given degrees of freedom.

Parameters:

degreesOfFreedom - the degrees of freedom.

Method Detail

setDegreesOfFreedom

@Deprecated
public void setDegreesOfFreedom(double degreesOfFreedom)

Deprecated.

as of 2.1 (class will become immutable in 3.0)

Modify the degrees of freedom.

Specified by:

setDegreesOfFreedom in interface TDistribution

Parameters:

degreesOfFreedom - the new degrees of freedom.

getDegreesOfFreedom

public double getDegreesOfFreedom()

Access the degrees of freedom.

Specified by:

getDegreesOfFreedom in interface TDistribution

Returns:

the degrees of freedom.

density

public double density(double x)

Returns the probability density for a particular point.

Overrides:

density in class AbstractContinuousDistribution

Parameters:

x - The point at which the density should be computed.

Returns:

The pdf at point x.

Since:

2.1

cumulativeProbability

public double cumulativeProbability(double x)
                             throws MathException

For this distribution, X, this method returns P(X < x).

Specified by:

cumulativeProbability in interface Distribution

Parameters:

x - the value at which the CDF is evaluated.

Returns:

CDF evaluated at x.

Throws:

MathException - if the cumulative probability can not be computed due to convergence or other numerical errors.

inverseCumulativeProbability

public double inverseCumulativeProbability(double p)
                                    throws MathException

For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.

Specified by:

inverseCumulativeProbability in interface ContinuousDistribution

Overrides:

inverseCumulativeProbability in class AbstractContinuousDistribution

Parameters:

p - the desired probability

Returns:

x, such that P(X < x) = p

Throws:

MathException - if the inverse cumulative probability can not be computed due to convergence or other numerical errors.

java.lang.IllegalArgumentException - if p is not a valid probability.

getSupportLowerBound

public double getSupportLowerBound()

Returns the lower bound of the support for the distribution. The lower bound of the support is always negative infinity no matter the parameters.

Returns:

lower bound of the support (always Double.NEGATIVE_INFINITY)

Since:

2.2

getSupportUpperBound

public double getSupportUpperBound()

Returns the upper bound of the support for the distribution. The upper bound of the support is always positive infinity no matter the parameters.

Returns:

upper bound of the support (always Double.POSITIVE_INFINITY)

Since:

2.2

getNumericalMean

public double getNumericalMean()

Returns the mean. For degrees of freedom parameter df, the mean is

• if df > 1 then 0
• else undefined

Returns:

the mean

Since:

2.2

getNumericalVariance

public double getNumericalVariance()

Returns the variance. For degrees of freedom parameter df, the variance is

• if df > 2 then df / (df - 2)
• if 1 < df <= 2 then positive infinity
• else undefined

Returns:

the variance

Since:

2.2