G01ECF (PDF version)
G01 Chapter Contents
G01 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

G01ECF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

 Contents

    1  Purpose
    7  Accuracy

1  Purpose

G01ECF returns the lower or upper tail probability for the χ2-distribution with real degrees of freedom, via the routine name.

2  Specification

FUNCTION G01ECF ( TAIL, X, DF, IFAIL)
REAL (KIND=nag_wp) G01ECF
INTEGER  IFAIL
REAL (KIND=nag_wp)  X, DF
CHARACTER(1)  TAIL

3  Description

The lower tail probability for the χ2-distribution with ν degrees of freedom, PXx:ν is defined by:
PXx:ν=12ν/2Γν/2 0.0xXν/2-1e-X/2dX,  x0,ν>0.  
To calculate PXx:ν a transformation of a gamma distribution is employed, i.e., a χ2-distribution with ν degrees of freedom is equal to a gamma distribution with scale parameter 2 and shape parameter ν/2.

4  References

Abramowitz M and Stegun I A (1972) Handbook of Mathematical Functions (3rd Edition) Dover Publications
Hastings N A J and Peacock J B (1975) Statistical Distributions Butterworth

5  Parameters

1:     TAIL – CHARACTER(1)Input
On entry: indicates whether the upper or lower tail probability is required.
TAIL='L'
The lower tail probability is returned, i.e., PXx:ν.
TAIL='U'
The upper tail probability is returned, i.e., PXx:ν.
Constraint: TAIL='L' or 'U'.
2:     X – REAL (KIND=nag_wp)Input
On entry: x, the value of the χ2 variate with ν degrees of freedom.
Constraint: X0.0.
3:     DF – REAL (KIND=nag_wp)Input
On entry: ν, the degrees of freedom of the χ2-distribution.
Constraint: DF>0.0.
4:     IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to 0, -1​ or ​1. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ or ​1 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 parameters may be useful even if IFAIL0 on exit, the recommended value is -1. When the value -1​ or ​1 is used it is essential to test the value of IFAIL on exit.
On exit: IFAIL=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6  Error Indicators and Warnings

If on entry IFAIL=0 or -1, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Note: G01ECF may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the routine:
If IFAIL=1, 2 or 3 on exit, then G01ECF returns 0.0.
IFAIL=1
On entry,TAIL'L' or 'U'.
IFAIL=2
On entry,X<0.0.
IFAIL=3
On entry,DF0.0.
IFAIL=4
The solution has failed to converge while calculating the gamma variate. The result returned should represent an approximation to the solution.
IFAIL=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.
IFAIL=-399
Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.
IFAIL=-999
Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7  Accuracy

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

8  Parallelism and Performance

Not applicable.

9  Further Comments

For higher accuracy the transformation described in Section 3 may be used with a direct call to S14BAF.

10  Example

Values from various χ2-distributions are read, the lower tail probabilities calculated, and all these values printed out, until the end of data is reached.

10.1  Program Text

Program Text (g01ecfe.f90)

10.2  Program Data

Program Data (g01ecfe.d)

10.3  Program Results

Program Results (g01ecfe.r)


G01ECF (PDF version)
G01 Chapter Contents
G01 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015