NAG Library Routine Document

g08ccf  (test_ks_1sample_user)

 Contents

    1  Purpose
    7  Accuracy

1
Purpose

g08ccf performs the one sample Kolmogorov–Smirnov distribution test, using a user-specified distribution.

2
Specification

Fortran Interface
Subroutine g08ccf ( n, x, cdf, ntype, d, z, p, sx, ifail)
Integer, Intent (In):: n, ntype
Integer, Intent (Inout):: ifail
Real (Kind=nag_wp), External:: cdf
Real (Kind=nag_wp), Intent (In):: x(n)
Real (Kind=nag_wp), Intent (Out):: d, z, p, sx(n)
C Header Interface
#include nagmk26.h
void  g08ccf_ ( const Integer *n, const double x[],
double (NAG_CALL *cdf)( const double *x),
const Integer *ntype, double *d, double *z, double *p, double sx[], Integer *ifail)

3
Description

The data consists of a single sample of n observations, denoted by x1,x2,,xn. Let Snxi and F0xi represent the sample cumulative distribution function and the theoretical (null) cumulative distribution function respectively at the point xi, where xi is the ith smallest sample observation.
The Kolmogorov–Smirnov test provides a test of the null hypothesis H0: the data are a random sample of observations from a theoretical distribution specified by you (in cdf) against one of the following alternative hypotheses.
(i) H1: the data cannot be considered to be a random sample from the specified null distribution.
(ii) H2: the data arise from a distribution which dominates the specified null distribution. In practical terms, this would be demonstrated if the values of the sample cumulative distribution function Snx tended to exceed the corresponding values of the theoretical cumulative distribution function F0x.
(iii) H3: the data arise from a distribution which is dominated by the specified null distribution. In practical terms, this would be demonstrated if the values of the theoretical cumulative distribution function F0x tended to exceed the corresponding values of the sample cumulative distribution function Snx.
One of the following test statistics is computed depending on the particular alternative hypothesis specified (see the description of the argument ntype in Section 5).
For the alternative hypothesis H1:
For the alternative hypothesis H2:
For the alternative hypothesis H3:
The standardized statistic, Z=D×n, is also computed, where D may be Dn,Dn+ or Dn- depending on the choice of the alternative hypothesis. This is the standardized value of D with no continuity correction applied and the distribution of Z converges asymptotically to a limiting distribution, first derived by Kolmogorov (1933), and then tabulated by Smirnov (1948). The asymptotic distributions for the one-sided statistics were obtained by Smirnov (1933).
The probability, under the null hypothesis, of obtaining a value of the test statistic as extreme as that observed, is computed. If n100, an exact method given by Conover (1980) is used. Note that the method used is only exact for continuous theoretical distributions and does not include Conover's modification for discrete distributions. This method computes the one-sided probabilities. The two-sided probabilities are estimated by doubling the one-sided probability. This is a good estimate for small p, that is p0.10, but it becomes very poor for larger p. If n>100 then p is computed using the Kolmogorov–Smirnov limiting distributions; see Feller (1948), Kendall and Stuart (1973), Kolmogorov (1933), Smirnov (1933) and Smirnov (1948).

4
References

Conover W J (1980) Practical Nonparametric Statistics Wiley
Feller W (1948) On the Kolmogorov–Smirnov limit theorems for empirical distributions Ann. Math. Statist. 19 179–181
Kendall M G and Stuart A (1973) The Advanced Theory of Statistics (Volume 2) (3rd Edition) Griffin
Kolmogorov A N (1933) Sulla determinazione empirica di una legge di distribuzione Giornale dell' Istituto Italiano degli Attuari 4 83–91
Siegel S (1956) Non-parametric Statistics for the Behavioral Sciences McGraw–Hill
Smirnov N (1933) Estimate of deviation between empirical distribution functions in two independent samples Bull. Moscow Univ. 2(2) 3–16
Smirnov N (1948) Table for estimating the goodness of fit of empirical distributions Ann. Math. Statist. 19 279–281

5
Arguments

1:     n – IntegerInput
On entry: n, the number of observations in the sample.
Constraint: n1.
2:     xn – Real (Kind=nag_wp) arrayInput
On entry: the sample observations, x1,x2,,xn.
3:     cdf – real (Kind=nag_wp) Function, supplied by the user.External Procedure
cdf must return the value of the theoretical (null) cumulative distribution function for a given value of its argument.
The specification of cdf is:
Fortran Interface
Function cdf ( x)
Real (Kind=nag_wp):: cdf
Real (Kind=nag_wp), Intent (In):: x
C Header Interface
#include nagmk26.h
double  cdf ( const double *x)
1:     x – Real (Kind=nag_wp)Input
On entry: the argument for which cdf must be evaluated.
cdf must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which g08ccf is called. Arguments denoted as Input must not be changed by this procedure.
Note: cdf should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by g08ccf. If your code inadvertently does return any NaNs or infinities, g08ccf is likely to produce unexpected results.
Constraint: cdf must always return a value in the range 0.0,1.0 and cdf must always satify the condition that cdfx1cdfx2 for any x1x2.
4:     ntype – IntegerInput
On entry: the statistic to be calculated, i.e., the choice of alternative hypothesis.
ntype=1
Computes Dn, to test H0 against H1.
ntype=2
Computes Dn+, to test H0 against H2.
ntype=3
Computes Dn-, to test H0 against H3.
Constraint: ntype=1, 2 or 3.
5:     d – Real (Kind=nag_wp)Output
On exit: the Kolmogorov–Smirnov test statistic ( D n , D n +  or D n -  according to the value of ntype).
6:     z – Real (Kind=nag_wp)Output
On exit: a standardized value, Z, of the test statistic, D, without the continuity correction applied.
7:     p – Real (Kind=nag_wp)Output
On exit: the probability, p, associated with the observed value of D, where D may Dn, Dn+ or Dn- depending on the value of ntype (see Section 3).
8:     sxn – Real (Kind=nag_wp) arrayOutput
On exit: the sample observations, x1,x2,,xn, sorted in ascending order.
9:     ifail – IntegerInput/Output
On entry: ifail must be set to 0, -1​ or ​1. 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 -1​ or ​1 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 -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).
Errors or warnings detected by the routine:
ifail=1
On entry,n<1.
ifail=2
On entry,ntype1, 2 or 3.
ifail=3
The supplied theoretical cumulative distribution function returns a value less than 0.0 or greater than 1.0, thereby violating the definition of the cumulative distribution function.
ifail=4
The supplied theoretical cumulative distribution function is not a nondecreasing function thereby violating the definition of a cumulative distribution function, that is F0x>F0y for some x<y.
ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
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.
ifail=-999
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7
Accuracy

For most cases the approximation for p given when n>100 has a relative error of less than 0.01. The two-sided probability is approximated by doubling the one-sided probability. This is only good for small p, that is p<0.10, but very poor for large p. The error is always on the conservative side.

8
Parallelism and Performance

g08ccf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9
Further Comments

The time taken by g08ccf increases with n until n>100 at which point it drops and then increases slowly.
For a discrete theoretical cumulative distribution function F0x, Dn-=maxF0xi-Snxi,0. Thus if you wish to provide a discrete distribution function the following adjustment needs to be made,

10
Example

The following example performs the one sample Kolmogorov–Smirnov test to test whether a sample of 30 observations arise firstly from a uniform distribution U0,1 or secondly from a Normal distribution with mean 0.75 and standard deviation 0.5. The two-sided test statistic, Dn, the standardized test statistic, Z, and the upper tail probability, p, are computed and then printed for each test.

10.1
Program Text

Program Text (g08ccfe.f90)

10.2
Program Data

Program Data (g08ccfe.d)

10.3
Program Results

Program Results (g08ccfe.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017