# NAG FL Interfaceg01kff (pdf_​gamma)

## 1Purpose

g01kff returns the value of the probability density function (PDF) for the gamma distribution with shape parameter $\alpha$ and scale parameter $\beta$ at a point $x$.

## 2Specification

Fortran Interface
 Function g01kff ( x, a, b,
 Real (Kind=nag_wp) :: g01kff Integer, Intent (Inout) :: ifail Real (Kind=nag_wp), Intent (In) :: x, a, b
#include <nag.h>
 double g01kff_ (const double *x, const double *a, const double *b, Integer *ifail)
The routine may be called by the names g01kff or nagf_stat_pdf_gamma.

## 3Description

The gamma distribution has PDF
 $fx= 1βαΓα xα-1e-x/β if ​x≥0; α,β>0 fx=0 otherwise.$
If $0.01\le x,\alpha ,\beta \le 100$ then an algorithm based directly on the gamma distribution's PDF is used. For values outside this range, the function is calculated via the Poisson distribution's PDF as described in Loader (2000) (see Section 9).

## 4References

Loader C (2000) Fast and accurate computation of binomial probabilities (not yet published)

## 5Arguments

1: $\mathbf{x}$Real (Kind=nag_wp) Input
On entry: $x$, the value at which the PDF is to be evaluated.
2: $\mathbf{a}$Real (Kind=nag_wp) Input
On entry: $\alpha$, the shape parameter of the gamma distribution.
Constraint: ${\mathbf{a}}>0.0$.
3: $\mathbf{b}$Real (Kind=nag_wp) Input
On entry: $\beta$, the scale parameter of the gamma distribution.
Constraints:
• ${\mathbf{b}}>0.0$;
• $\frac{{\mathbf{x}}}{{\mathbf{b}}}<\frac{1}{{\mathbf{x02amf}}\left(\right)}$.
4: $\mathbf{ifail}$Integer Input/Output
On entry: ifail must be set to $0$, . If you are unfamiliar with this argument you should refer to Section 4 in the Introduction to the NAG Library FL Interface for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is $0$. When the value is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

## 6Error Indicators and Warnings

If on entry ${\mathbf{ifail}}=0$ or $-1$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
If ${\mathbf{ifail}}\ne {\mathbf{0}}$, then g01kff returns $0.0$.
${\mathbf{ifail}}=1$
On entry, ${\mathbf{a}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{a}}>0.0$.
${\mathbf{ifail}}=2$
On entry, ${\mathbf{b}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{b}}>0.0$.
${\mathbf{ifail}}=3$
Computation abandoned owing to overflow due to extreme parameter values.
${\mathbf{ifail}}=-99$
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

Not applicable.

## 8Parallelism and Performance

g01kff is not threaded in any implementation.

Due to the lack of a stable link to Loader (2000) paper, we give a brief overview of the method, as applied to the Poisson distribution. The Poisson distribution has a continuous mass function given by,
 $px;λ = λx x! e-λ .$ (1)
The usual way of computing this quantity would be to take the logarithm and calculate,
 $logx;λ = x log⁡λ - log x! - λ .$
For large $x$ and $\lambda$, $x\mathrm{log}\lambda$ and $\mathrm{log}\left(x!\right)$ are very large, of the same order of magnitude and when calculated have rounding errors. The subtraction of these two terms can therefore result in a number, many orders of magnitude smaller and hence we lose accuracy due to subtraction errors. For example for $x=2×{10}^{6}$ and $\lambda =2×{10}^{6}$, $\mathrm{log}\left(x!\right)\approx 2.7×{10}^{7}$ and $\mathrm{log}\left(p\left(x;\lambda \right)\right)=-8.17326744645834$. But calculated with the method shown later we have $\mathrm{log}\left(p\left(x;\lambda \right)\right)=-8.1732674441334492$. The difference between these two results suggests a loss of about $7$ significant figures of precision.
Loader introduces an alternative way of expressing (1) based on the saddle point expansion,
 $log p x;λ = log p x;x - Dx;λ ,$ (2)
where $D\left(x;\lambda \right)$, the deviance for the Poisson distribution is given by,
 $Dx;λ = log p x;x - log p x;λ , = λ D0 x λ ,$ (3)
and
 $D0 ε = ε log⁡ε + 1 - ε .$
For $\epsilon$ close to $1$, ${D}_{0}\left(\epsilon \right)$ can be evaluated through the series expansion
 $λ D0 x λ = x-λ 2 x+λ + 2x ∑ j=1 ∞ v 2j+1 2j+1 , where ​ v = x-λ x+λ ,$
otherwise ${D}_{0}\left(\epsilon \right)$ can be evaluated directly. In addition, Loader suggests evaluating $\mathrm{log}\left(x!\right)$ using the Stirling–De Moivre series,
 $logx! = 12 log⁡ 2πx + x logx -x + δx ,$ (4)
where the error $\delta \left(x\right)$ is given by
 $δx = 112x - 1 360x3 + 1 1260x5 + O x-7 .$
Finally $\mathrm{log}\left(p\left(x;\lambda \right)\right)$ can be evaluated by combining equations (1)(4) to get,
 $p x;λ = 1 2πx e - δx - λ D0 x/λ .$

## 10Example

This example prints the value of the gamma distribution PDF at six different points x with differing a and b.

### 10.1Program Text

Program Text (g01kffe.f90)

### 10.2Program Data

Program Data (g01kffe.d)

### 10.3Program Results

Program Results (g01kffe.r)