# NAG FL Interfaceg01slf (prob_​hypergeom_​vector)

## 1Purpose

g01slf returns a number of the lower tail, upper tail and point probabilities for the hypergeometric distribution.

## 2Specification

Fortran Interface
 Subroutine g01slf ( ln, n, ll, l, lm, m, lk, k, plek, pgtk, peqk,
 Integer, Intent (In) :: ln, n(ln), ll, l(ll), lm, m(lm), lk, k(lk) Integer, Intent (Inout) :: ifail Integer, Intent (Out) :: ivalid(*) Real (Kind=nag_wp), Intent (Out) :: plek(*), pgtk(*), peqk(*)
C Header Interface
#include <nag.h>
 void g01slf_ (const Integer *ln, const Integer n[], const Integer *ll, const Integer l[], const Integer *lm, const Integer m[], const Integer *lk, const Integer k[], double plek[], double pgtk[], double peqk[], Integer ivalid[], Integer *ifail)
The routine may be called by the names g01slf or nagf_stat_prob_hypergeom_vector.

## 3Description

Let $X=\left\{{X}_{i}:i=1,2,\dots ,r\right\}$ denote a vector of random variables having a hypergeometric distribution with parameters ${n}_{i}$, ${l}_{i}$ and ${m}_{i}$. Then
 $Prob Xi = ki = mi ki ni - mi li - ki ni li ,$
where $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(0,{l}_{i}+{m}_{i}-{n}_{i}\right)\le {k}_{i}\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({l}_{i},{m}_{i}\right)$, $0\le {l}_{i}\le {n}_{i}$ and $0\le {m}_{i}\le {n}_{i}$.
The hypergeometric distribution may arise if in a population of size ${n}_{i}$ a number ${m}_{i}$ are marked. From this population a sample of size ${l}_{i}$ is drawn and of these ${k}_{i}$ are observed to be marked.
The mean of the distribution $\text{}=\frac{{l}_{i}{m}_{i}}{{n}_{i}}$, and the variance $\text{}=\frac{{l}_{i}{m}_{i}\left({n}_{i}-{l}_{i}\right)\left({n}_{i}-{m}_{i}\right)}{{{n}_{i}}^{2}\left({n}_{i}-1\right)}$.
g01slf computes for given ${n}_{i}$, ${l}_{i}$, ${m}_{i}$ and ${k}_{i}$ the probabilities: $\mathrm{Prob}\left\{{X}_{i}\le {k}_{i}\right\}$, $\mathrm{Prob}\left\{{X}_{i}>{k}_{i}\right\}$ and $\mathrm{Prob}\left\{{X}_{i}={k}_{i}\right\}$ using an algorithm similar to that described in Knüsel (1986) for the Poisson distribution.
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.
Knüsel L (1986) Computation of the chi-square and Poisson distribution SIAM J. Sci. Statist. Comput. 7 1022–1036

## 5Arguments

1: $\mathbf{ln}$Integer Input
On entry: the length of the array n.
Constraint: ${\mathbf{ln}}>0$.
2: $\mathbf{n}\left({\mathbf{ln}}\right)$Integer array Input
On entry: ${n}_{i}$, the parameter of the hypergeometric distribution with ${n}_{i}={\mathbf{n}}\left(j\right)$, , for $i=1,2,\dots ,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ln}},{\mathbf{ll}},{\mathbf{lm}},{\mathbf{lk}}\right)$.
Constraint: ${\mathbf{n}}\left(\mathit{j}\right)\ge 0$, for $\mathit{j}=1,2,\dots ,{\mathbf{ln}}$.
3: $\mathbf{ll}$Integer Input
On entry: the length of the array l.
Constraint: ${\mathbf{ll}}>0$.
4: $\mathbf{l}\left({\mathbf{ll}}\right)$Integer array Input
On entry: ${l}_{i}$, the parameter of the hypergeometric distribution with ${l}_{i}={\mathbf{l}}\left(j\right)$, .
Constraint: $0\le {l}_{i}\le {n}_{i}$.
5: $\mathbf{lm}$Integer Input
On entry: the length of the array m.
Constraint: ${\mathbf{lm}}>0$.
6: $\mathbf{m}\left({\mathbf{lm}}\right)$Integer array Input
On entry: ${m}_{i}$, the parameter of the hypergeometric distribution with ${m}_{i}={\mathbf{m}}\left(j\right)$, .
Constraint: $0\le {m}_{i}\le {n}_{i}$.
7: $\mathbf{lk}$Integer Input
On entry: the length of the array k.
Constraint: ${\mathbf{lk}}>0$.
8: $\mathbf{k}\left({\mathbf{lk}}\right)$Integer array Input
On entry: ${k}_{i}$, the integer which defines the required probabilities with ${k}_{i}={\mathbf{k}}\left(j\right)$, .
Constraint: $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(0,{l}_{i}+{m}_{i}-{n}_{i}\right)\le {k}_{i}\le \mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({l}_{i},{m}_{i}\right)$.
9: $\mathbf{plek}\left(*\right)$Real (Kind=nag_wp) array Output
Note: the dimension of the array plek must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ln}},{\mathbf{ll}},{\mathbf{lm}},{\mathbf{lk}}\right)$.
On exit: $\mathrm{Prob}\left\{{X}_{i}\le {k}_{i}\right\}$, the lower tail probabilities.
10: $\mathbf{pgtk}\left(*\right)$Real (Kind=nag_wp) array Output
Note: the dimension of the array pgtk must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ln}},{\mathbf{ll}},{\mathbf{lm}},{\mathbf{lk}}\right)$.
On exit: $\mathrm{Prob}\left\{{X}_{i}>{k}_{i}\right\}$, the upper tail probabilities.
11: $\mathbf{peqk}\left(*\right)$Real (Kind=nag_wp) array Output
Note: the dimension of the array peqk must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ln}},{\mathbf{ll}},{\mathbf{lm}},{\mathbf{lk}}\right)$.
On exit: $\mathrm{Prob}\left\{{X}_{i}={k}_{i}\right\}$, the point probabilities.
12: $\mathbf{ivalid}\left(*\right)$Integer array Output
Note: the dimension of the array ivalid must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ln}},{\mathbf{ll}},{\mathbf{lm}},{\mathbf{lk}}\right)$.
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, ${n}_{i}<0$.
${\mathbf{ivalid}}\left(i\right)=2$
On entry, ${l}_{i}<0$, or, ${l}_{i}>{n}_{i}$.
${\mathbf{ivalid}}\left(i\right)=3$
On entry, ${m}_{i}<0$, or, ${m}_{i}>{n}_{i}$.
${\mathbf{ivalid}}\left(i\right)=4$
On entry, ${k}_{i}<0$, or, ${k}_{i}>{l}_{i}$, or, ${k}_{i}>{m}_{i}$, or, ${k}_{i}<{l}_{i}+{m}_{i}-{n}_{i}$.
${\mathbf{ivalid}}\left(i\right)=5$
On entry, ${n}_{i}$ is too large to be represented exactly as a real number.
${\mathbf{ivalid}}\left(i\right)=6$
On entry, the variance (see Section 3) exceeds ${10}^{6}$.
13: $\mathbf{ifail}$Integer Input/Output
On entry: ifail must be set to $0$, $-1$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $-1$ means that an error message is printed while a value of $1$ means that it is not.
If halting is not appropriate, the value $-1$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $0$ is recommended. When the value $-\mathbf{1}$ or $\mathbf{1}$ 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:
${\mathbf{ifail}}=1$
On entry, at least one value of n, l, m or k was invalid, or the variance was too large.
Check ivalid for more information.
${\mathbf{ifail}}=2$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ln}}>0$.
${\mathbf{ifail}}=3$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ll}}>0$.
${\mathbf{ifail}}=4$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lm}}>0$.
${\mathbf{ifail}}=5$
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lk}}>0$.
${\mathbf{ifail}}=-99$
An unexpected error has been triggered by this routine. Please contact NAG.
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.

## 7Accuracy

Results are correct to a relative accuracy of at least ${10}^{-6}$ on machines with a precision of $9$ or more decimal digits (provided that the results do not underflow to zero).

## 8Parallelism and Performance

g01slf is not threaded in any implementation.

## 9Further Comments

The time taken by g01slf to calculate each probability depends on the variance (see Section 3) and on ${k}_{i}$. For given variance, the time is greatest when ${k}_{i}\approx {l}_{i}{m}_{i}/{n}_{i}$ ($=$ the mean), and is then approximately proportional to the square-root of the variance.

## 10Example

This example reads a vector of values for $n$, $l$, $m$ and $k$, and prints the corresponding probabilities.

### 10.1Program Text

Program Text (g01slfe.f90)

### 10.2Program Data

Program Data (g01slfe.d)

### 10.3Program Results

Program Results (g01slfe.r)