g01er returns the probability associated with the lower tail of the von Mises distribution between $-\pi$ and $\pi$ through the function name.

# Syntax

C#
```public static double g01er(
double t,
double vk,
out int ifail
)```
Visual Basic
```Public Shared Function g01er ( _
t As Double, _
vk As Double, _
<OutAttribute> ByRef ifail As Integer _
) As Double```
Visual C++
```public:
static double g01er(
double t,
double vk,
[OutAttribute] int% ifail
)```
F#
```static member g01er :
t : float *
vk : float *
ifail : int byref -> float
```

#### Parameters

t
Type: System..::..Double
On entry: $\theta$, the observed von Mises statistic measured in radians.
vk
Type: System..::..Double
On entry: the concentration parameter $\kappa$, of the von Mises distribution.
Constraint: ${\mathbf{vk}}\ge 0.0$.
ifail
Type: System..::..Int32%
On exit: ${\mathbf{ifail}}={0}$ unless the method detects an error or a warning has been flagged (see [Error Indicators and Warnings]).

#### Return Value

g01er returns the probability associated with the lower tail of the von Mises distribution between $-\pi$ and $\pi$ through the function name.

# Description

The von Mises distribution is a symmetric distribution used in the analysis of circular data. The lower tail area of this distribution on the circle with mean direction ${\mu }_{0}=0$ and concentration parameter kappa, $\kappa$, can be written as
 $PrΘ≤θ:κ=12πI0κ∫-πθeκcos ΘdΘ,$
where $\theta$ is reduced modulo $2\pi$ so that $-\pi \le \theta <\pi$ and $\kappa \ge 0$. Note that if $\theta =\pi$ then g01er returns a probability of $1$. For very small $\kappa$ the distribution is almost the uniform distribution, whereas for $\kappa \to \infty$ all the probability is concentrated at one point.
The method of calculation for small $\kappa$ involves backwards recursion through a series expansion in terms of modified Bessel functions, while for large $\kappa$ an asymptotic Normal approximation is used.
In the case of small $\kappa$ the series expansion of Pr($\Theta \le \theta$: $\kappa$) can be expressed as
 $PrΘ≤θ:κ=12+θ2π+1πI0κ∑n=1∞n-1Inκsin nθ,$
where ${I}_{n}\left(\kappa \right)$ is the modified Bessel function. This series expansion can be represented as a nested expression of terms involving the modified Bessel function ratio ${R}_{n}$,
 $Rnκ=InκIn-1κ, n=1,2,3,…,$
which is calculated using backwards recursion.
For large values of $\kappa$ (see [Accuracy]) an asymptotic Normal approximation is used. The angle $\Theta$ is transformed to the nearly Normally distributed variate $Z$,
 $Z=bκsin Θ2,$
where
 $bκ=2πeκI0κ$
and $b\left(\kappa \right)$ is computed from a continued fraction approximation. An approximation to order ${\kappa }^{-4}$ of the asymptotic normalizing series for $z$ is then used. Finally the Normal probability integral is evaluated.
For a more detailed analysis of the methods used see Hill (1977).

# References

Hill G W (1977) Algorithm 518: Incomplete Bessel function ${I}_{0}$: The Von Mises distribution ACM Trans. Math. Software 3 279–284
Mardia K V (1972) Statistics of Directional Data Academic Press

# Error Indicators and Warnings

Errors or warnings detected by the method:
${\mathbf{ifail}}=1$
 On entry, ${\mathbf{vk}}<0.0$ and g01er returns $0$.
${\mathbf{ifail}}=-9000$
An error occured, see message report.

# Accuracy

g01er uses one of two sets of constants depending on the value of machine precision. One set gives an accuracy of six digits and uses the Normal approximation when ${\mathbf{vk}}\ge 6.5$, the other gives an accuracy of $12$ digits and uses the Normal approximation when ${\mathbf{vk}}\ge 50.0$.

# Parallelism and Performance

None.

Using the series expansion for small $\kappa$ the time taken by g01er increases linearly with $\kappa$; for larger $\kappa$, for which the asymptotic Normal approximation is used, the time taken is much less.
If angles outside the region $-\pi \le \theta <\pi$ are used care has to be taken in evaluating the probability of being in a region ${\theta }_{1}\le \theta \le {\theta }_{2}$ if the region contains an odd multiple of $\pi$, $\left(2n+1\right)\pi$. The value of $F\left({\theta }_{2}\text{;}\kappa \right)-F\left({\theta }_{1}\text{;}\kappa \right)$ will be negative and the correct probability should then be obtained by adding one to the value.

# Example

This example inputs four values from the von Mises distribution along with the values of the parameter $\kappa$. The probabilities are computed and printed.

Example program (C#): g01ere.cs

Example program data: g01ere.d

Example program results: g01ere.r