org.apache.commons.math.distribution

## Class ChiSquaredDistributionImpl

• ### Field Summary

Fields
Modifier and Type Field and Description
`static double` `DEFAULT_INVERSE_ABSOLUTE_ACCURACY`
Default inverse cumulative probability accuracy
• ### Constructor Summary

Constructors
Constructor and Description
`ChiSquaredDistributionImpl(double df)`
Create a Chi-Squared distribution with the given degrees of freedom.
```ChiSquaredDistributionImpl(double df, double inverseCumAccuracy)```
Create a Chi-Squared distribution with the given degrees of freedom and inverse cumulative probability accuracy.
```ChiSquaredDistributionImpl(double df, GammaDistribution g)```
Deprecated.
as of 2.1 (to avoid possibly inconsistent state, the "GammaDistribution" will be instantiated internally)
• ### Method Summary

All Methods
Modifier and Type Method and Description
`double` `cumulativeProbability(double x)`
For this distribution, X, this method returns P(X < x).
`double` `density(double x)`
Return the probability density for a particular point.
`double` `density(java.lang.Double x)`
Deprecated.
`double` `getDegreesOfFreedom()`
Access the degrees of freedom.
`double` `getNumericalMean()`
Returns the mean of the distribution.
`double` `getNumericalVariance()`
Returns the variance of the distribution.
`double` `getSupportLowerBound()`
Returns the lower bound of the support for the distribution.
`double` `getSupportUpperBound()`
Returns the upper bound for 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)
`void` `setGamma(GammaDistribution g)`
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
Constant Field Values
• ### Constructor Detail

• #### ChiSquaredDistributionImpl

`public ChiSquaredDistributionImpl(double df)`
Create a Chi-Squared distribution with the given degrees of freedom.
Parameters:
`df` - degrees of freedom.
• #### ChiSquaredDistributionImpl

```@Deprecated
public ChiSquaredDistributionImpl(double df,
Deprecated. as of 2.1 (to avoid possibly inconsistent state, the "GammaDistribution" will be instantiated internally)
Create a Chi-Squared distribution with the given degrees of freedom.
Parameters:
`df` - degrees of freedom.
`g` - the underlying gamma distribution used to compute probabilities.
Since:
1.2
• #### ChiSquaredDistributionImpl

```public ChiSquaredDistributionImpl(double df,
double inverseCumAccuracy)```
Create a Chi-Squared distribution with the given degrees of freedom and inverse cumulative probability accuracy.
Parameters:
`df` - degrees of freedom.
`inverseCumAccuracy` - the maximum absolute error in inverse cumulative probability estimates (defaults to `DEFAULT_INVERSE_ABSOLUTE_ACCURACY`)
Since:
2.1
• ### 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 `ChiSquaredDistribution`
Parameters:
`degreesOfFreedom` - the new degrees of freedom.
• #### getDegreesOfFreedom

`public double getDegreesOfFreedom()`
Access the degrees of freedom.
Specified by:
`getDegreesOfFreedom` in interface `ChiSquaredDistribution`
Returns:
the degrees of freedom.
• #### density

```@Deprecated
public double density(java.lang.Double x)```
Deprecated.
Return the probability density for a particular point.
Specified by:
`density` in interface `ChiSquaredDistribution`
Specified by:
`density` in interface `HasDensity<java.lang.Double>`
Parameters:
`x` - The point at which the density should be computed.
Returns:
The pdf at point x.
• #### density

`public double density(double x)`
Return 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 for this distribution.
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 0 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.
• #### setGamma

```@Deprecated
Deprecated. as of 2.1 (class will become immutable in 3.0)
Modify the underlying gamma distribution. The caller is responsible for insuring the gamma distribution has the proper parameter settings.
Parameters:
`g` - the new distribution.
Since:
• #### getSupportLowerBound

`public double getSupportLowerBound()`
Returns the lower bound of the support for the distribution. The lower bound of the support is always 0 no matter the degrees of freedom.
Returns:
lower bound of the support (always 0)
Since:
2.2
• #### getSupportUpperBound

`public double getSupportUpperBound()`
Returns the upper bound for the support for the distribution. The upper bound of the support is always positive infinity no matter the degrees of freedom.
Returns:
upper bound of the support (always Double.POSITIVE_INFINITY)
Since:
2.2
• #### getNumericalMean

`public double getNumericalMean()`
Returns the mean of the distribution. For `k` degrees of freedom, the mean is `k`
Returns:
the mean
Since:
2.2
• #### getNumericalVariance

`public double getNumericalVariance()`
Returns the variance of the distribution. For `k` degrees of freedom, the variance is `2 * k`
Returns:
the variance
Since:
2.2