g01 Chapter Contents
g01 Chapter Introduction
NAG C Library Manual

# NAG Library Function Documentnag_prob_beta_vector (g01sec)

## 1  Purpose

nag_prob_beta_vector (g01sec) computes a number of lower or upper tail probabilities for the beta distribution.

## 2  Specification

 #include #include
 void nag_prob_beta_vector (Integer ltail, const Nag_TailProbability tail[], Integer lbeta, const double beta[], Integer la, const double a[], Integer lb, const double b[], double p[], Integer ivalid[], NagError *fail)

## 3  Description

The lower tail probability, $P\left({B}_{i}\le {\beta }_{i}:{a}_{i},{b}_{i}\right)$ is defined by
 $P Bi ≤ βi :ai,bi = Γ ai + bi Γ ai Γ bi ∫ 0 βi Bi ai-1 1-Bi bi-1 dBi = Iβi ai,bi , 0 ≤ βi ≤ 1 ; ai , bi > 0 .$
The function ${I}_{{\beta }_{i}}\left({a}_{i},{b}_{i}\right)$, also known as the incomplete beta function is calculated using nag_incomplete_beta (s14ccc).
The input arrays to this function 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.

## 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
Majumder K L and Bhattacharjee G P (1973) Algorithm AS 63. The incomplete beta integral Appl. Statist. 22 409–411

## 5  Arguments

1:     ltailIntegerInput
On entry: the length of the array tail.
Constraint: ${\mathbf{ltail}}>0$.
2:     tail[${\mathbf{ltail}}$]const Nag_TailProbabilityInput
On entry: indicates whether a lower or upper tail probabilities are required. For , for $\mathit{i}=1,2,\dots ,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ltail}},{\mathbf{lbeta}},{\mathbf{la}},{\mathbf{lb}}\right)$:
${\mathbf{tail}}\left[j\right]=\mathrm{Nag_LowerTail}$
The lower tail probability is returned, i.e., ${p}_{i}=P\left({B}_{i}\le {\beta }_{i}:{a}_{i},{b}_{i}\right)$.
${\mathbf{tail}}\left[j\right]=\mathrm{Nag_UpperTail}$
The upper tail probability is returned, i.e., ${p}_{i}=P\left({B}_{i}\ge {\beta }_{i}:{a}_{i},{b}_{i}\right)$.
Constraint: ${\mathbf{tail}}\left[\mathit{j}-1\right]=\mathrm{Nag_LowerTail}$ or $\mathrm{Nag_UpperTail}$, for $\mathit{j}=1,2,\dots ,{\mathbf{ltail}}$.
3:     lbetaIntegerInput
On entry: the length of the array beta.
Constraint: ${\mathbf{lbeta}}>0$.
4:     beta[lbeta]const doubleInput
On entry: ${\beta }_{i}$, the value of the beta variate with ${\beta }_{i}={\mathbf{beta}}\left[j\right]$, .
Constraint: $0.0\le {\mathbf{beta}}\left[\mathit{j}-1\right]\le 1.0$, for $\mathit{j}=1,2,\dots ,{\mathbf{lbeta}}$.
5:     laIntegerInput
On entry: the length of the array a.
Constraint: ${\mathbf{la}}>0$.
6:     a[la]const doubleInput
On entry: ${a}_{i}$, the first parameter of the required beta distribution with ${a}_{i}={\mathbf{a}}\left[j\right]$, .
Constraint: ${\mathbf{a}}\left[\mathit{j}-1\right]>0.0$, for $\mathit{j}=1,2,\dots ,{\mathbf{la}}$.
7:     lbIntegerInput
On entry: the length of the array b.
Constraint: ${\mathbf{lb}}>0$.
8:     b[lb]const doubleInput
On entry: ${b}_{i}$, the second parameter of the required beta distribution with ${b}_{i}={\mathbf{b}}\left[j\right]$, .
Constraint: ${\mathbf{b}}\left[\mathit{j}-1\right]>0.0$, for $\mathit{j}=1,2,\dots ,{\mathbf{lb}}$.
9:     p[$\mathit{dim}$]doubleOutput
Note: the dimension, dim, of the array p must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ltail}},{\mathbf{lbeta}},{\mathbf{la}},{\mathbf{lb}}\right)$.
On exit: ${p}_{i}$, the probabilities for the beta distribution.
10:   ivalid[$\mathit{dim}$]IntegerOutput
Note: the dimension, dim, of the array ivalid must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ltail}},{\mathbf{lbeta}},{\mathbf{la}},{\mathbf{lb}}\right)$.
On exit: ${\mathbf{ivalid}}\left[i-1\right]$ indicates any errors with the input arguments, with
${\mathbf{ivalid}}\left[i-1\right]=0$
No error.
${\mathbf{ivalid}}\left[i-1\right]=1$
 On entry, invalid value supplied in tail when calculating ${p}_{i}$.
${\mathbf{ivalid}}\left[i-1\right]=2$
 On entry, ${\beta }_{i}<0.0$, or ${\beta }_{i}>1.0$.
${\mathbf{ivalid}}\left[i-1\right]=3$
 On entry, ${a}_{i}\le 0.0$, or ${b}_{i}\le 0.0$,
11:   failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

## 6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_ARRAY_SIZE
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{la}}>0$.
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lb}}>0$.
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{lbeta}}>0$.
On entry, $\text{array size}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{ltail}}>0$.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NW_IVALID
On entry, at least one value of beta, a, b or tail was invalid.

## 7  Accuracy

The accuracy is limited by the error in the incomplete beta function. See Section 7 in nag_incomplete_beta (s14ccc) for further details.

None.

## 9  Example

This example reads values from a number of beta distributions and computes the associated lower tail probabilities.

### 9.1  Program Text

Program Text (g01sece.c)

### 9.2  Program Data

Program Data (g01sece.d)

### 9.3  Program Results

Program Results (g01sece.r)