NAG Library Routine Document

1Purpose

g01scf returns a number of lower or upper tail probabilities for the ${\chi }^{2}$-distribution with real degrees of freedom.

2Specification

Fortran Interface
 Subroutine g01scf ( tail, lx, x, ldf, df, p,
 Integer, Intent (In) :: ltail, lx, ldf Integer, Intent (Inout) :: ifail Integer, Intent (Out) :: ivalid(*) Real (Kind=nag_wp), Intent (In) :: x(lx), df(ldf) Real (Kind=nag_wp), Intent (Out) :: p(*) Character (1), Intent (In) :: tail(ltail)
#include <nagmk26.h>
 void g01scf_ (const Integer *ltail, const char tail[], const Integer *lx, const double x[], const Integer *ldf, const double df[], double p[], Integer ivalid[], Integer *ifail, const Charlen length_tail)

3Description

The lower tail probability for the ${\chi }^{2}$-distribution with ${\nu }_{i}$ degrees of freedom, $P=\left({X}_{i}\le {x}_{i}:{\nu }_{i}\right)$ is defined by:
 $P = Xi≤xi:νi = 1 2 νi/2 Γ νi/2 ∫ 0.0 xi Xi νi/2-1 e -Xi/2 dXi , xi ≥ 0 , νi > 0 .$
To calculate $P=\left({X}_{i}\le {x}_{i}:{\nu }_{i}\right)$ a transformation of a gamma distribution is employed, i.e., a ${\chi }^{2}$-distribution with ${\nu }_{i}$ degrees of freedom is equal to a gamma distribution with scale parameter $2$ and shape parameter ${\nu }_{i}/2$.
The input arrays to this routine are designed to allow maximum flexibility in the supply of vector arguments by re-using elements of any arrays that are shorter than the total number of evaluations required. See Section 2.6 in the G01 Chapter Introduction for further information.

4References

NIST Digital Library of Mathematical Functions
Hastings N A J and Peacock J B (1975) Statistical Distributions Butterworth

5Arguments

1:     $\mathbf{ltail}$ – IntegerInput
On entry: the length of the array tail.
Constraint: ${\mathbf{ltail}}>0$.
2:     $\mathbf{tail}\left({\mathbf{ltail}}\right)$ – Character(1) arrayInput
On entry: indicates whether the lower or upper tail probabilities are required. For , for $\mathit{i}=1,2,\dots ,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ltail}},{\mathbf{lx}},{\mathbf{ldf}}\right)$:
${\mathbf{tail}}\left(j\right)=\text{'L'}$
The lower tail probability is returned, i.e., ${p}_{i}=P\left({X}_{i}\le {x}_{i}:{\nu }_{i}\right)$.
${\mathbf{tail}}\left(j\right)=\text{'U'}$
The upper tail probability is returned, i.e., ${p}_{i}=P\left({X}_{i}\ge {x}_{i}:{\nu }_{i}\right)$.
Constraint: ${\mathbf{tail}}\left(\mathit{j}\right)=\text{'L'}$ or $\text{'U'}$, for $\mathit{j}=1,2,\dots ,{\mathbf{ltail}}$.
3:     $\mathbf{lx}$ – IntegerInput
On entry: the length of the array x.
Constraint: ${\mathbf{lx}}>0$.
4:     $\mathbf{x}\left({\mathbf{lx}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: ${x}_{i}$, the values of the ${\chi }^{2}$ variates with ${\nu }_{i}$ degrees of freedom with ${x}_{i}={\mathbf{x}}\left(j\right)$, .
Constraint: ${\mathbf{x}}\left(\mathit{j}\right)\ge 0.0$, for $\mathit{j}=1,2,\dots ,{\mathbf{lx}}$.
5:     $\mathbf{ldf}$ – IntegerInput
On entry: the length of the array df.
Constraint: ${\mathbf{ldf}}>0$.
6:     $\mathbf{df}\left({\mathbf{ldf}}\right)$ – Real (Kind=nag_wp) arrayInput
On entry: ${\nu }_{i}$, the degrees of freedom of the ${\chi }^{2}$-distribution with ${\nu }_{i}={\mathbf{df}}\left(j\right)$, .
Constraint: ${\mathbf{df}}\left(\mathit{j}\right)>0.0$, for $\mathit{j}=1,2,\dots ,{\mathbf{ldf}}$.
7:     $\mathbf{p}\left(*\right)$ – Real (Kind=nag_wp) arrayOutput
Note: the dimension of the array p must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ltail}},{\mathbf{ldf}},{\mathbf{lx}}\right)$.
On exit: ${p}_{i}$, the probabilities for the ${\chi }^{2}$ distribution.
8:     $\mathbf{ivalid}\left(*\right)$ – Integer arrayOutput
Note: the dimension of the array ivalid must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ltail}},{\mathbf{ldf}},{\mathbf{lx}}\right)$.
On exit: ${\mathbf{ivalid}}\left(i\right)$ indicates any errors with the input arguments, with
${\mathbf{ivalid}}\left(i\right)=0$
No error.
${\mathbf{ivalid}}\left(i\right)=1$
 On entry, invalid value supplied in tail when calculating ${p}_{i}$.
${\mathbf{ivalid}}\left(i\right)=2$
 On entry, ${x}_{i}<0.0$.
${\mathbf{ivalid}}\left(i\right)=3$
 On entry, ${\nu }_{i}\le 0.0$.
${\mathbf{ivalid}}\left(i\right)=4$
The solution has failed to converge while calculating the gamma variate. The result returned should represent an approximation to the solution.
9:     $\mathbf{ifail}$ – IntegerInput/Output
On entry: ifail must be set to $0$, . If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation 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, because for this routine the values of the output arguments may be useful even if ${\mathbf{ifail}}\ne {\mathbf{0}}$ on exit, the recommended value is $-1$. 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).
Note: g01scf may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the routine:
${\mathbf{ifail}}=1$
On entry, at least one value of x, df or tail was invalid, or the solution failed to converge.
${\mathbf{ifail}}=2$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ltail}}>0$.
${\mathbf{ifail}}=3$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lx}}>0$.
${\mathbf{ifail}}=4$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ldf}}>0$.
${\mathbf{ifail}}=-99$
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7Accuracy

A relative accuracy of five significant figures is obtained in most cases.

8Parallelism and Performance

g01scf is not threaded in any implementation.

For higher accuracy the transformation described in Section 3 may be used with a direct call to s14baf.

10Example

Values from various ${\chi }^{2}$-distributions are read, the lower tail probabilities calculated, and all these values printed out.

10.1Program Text

Program Text (g01scfe.f90)

10.2Program Data

Program Data (g01scfe.d)

10.3Program Results

Program Results (g01scfe.r)