Integer, Intent (In)	::	ltail, lp, ldf
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	ivalid(*)
Real (Kind=nag_wp), Intent (In)	::	p(lp), df(ldf)
Real (Kind=nag_wp), Intent (Out)	::	t(*)
Character (1), Intent (In)	::	tail(ltail)

C Header Interface

#include <nag.h>

void	g01tbf_ (const Integer ltail, const char tail[], const Integer lp, const double p[], const Integer ldf, const double df[], double t[], Integer ivalid[], Integer ifail, const Charlen length_tail)

The routine may be called by the names g01tbf or nagf_stat_inv_cdf_students_t_vector.

3 Description

The deviate,

t_{p_{i}}

associated with the lower tail probability,

p_{i}

, of the Student's

t

-distribution with

ν_{i}

degrees of freedom is defined as the solution to

P (T_{i} < t_{p_{i}} : ν_{i}) = p_{i} = \frac{Γ ((ν_{i} + 1) / 2)}{\sqrt{ν_{i} π} Γ (ν_{i} / 2)} \int_{- \infty}^{t_{p_{i}}} {(1 + \frac{{T_{i}}^{2}}{ν_{i}})}^{- (ν_{i} + 1) / 2} d T_{i}, ν_{i} \geq 1; ​ - \infty < t_{p_{i}} < \infty .

For

ν_{i} = 1

2

the integral equation is easily solved for

t_{p_{i}}

For other values of

ν_{i} < 3

a transformation to the beta distribution is used and the result obtained from g01fef.

For

ν_{i} \geq 3

an inverse asymptotic expansion of Cornish–Fisher type is used. The algorithm is described by Hill (1970).

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.

4 References

Hastings N A J and Peacock J B (1975) Statistical Distributions Butterworth

Hill G W (1970) Student's

t

-distribution Comm. ACM 13(10) 617–619

5 Arguments

1: $ltail$ – Integer Input

On entry: the length of the array tail.

Constraint:

ltail > 0

2: $tail (ltail)$ – Character(1) array Input

On entry: indicates which tail the supplied probabilities represent. For

j = ((i - 1) mod ltail) + 1

, for

i = 1, 2, \dots, \max (ltail, lp, ldf)

$tail (j) ='L'$: The lower tail probability, i.e., $p_{i} = P (T_{i} \leq t_{p_{i}} : ν_{i})$ .
$tail (j) ='U'$: The upper tail probability, i.e., $p_{i} = P (T_{i} \geq t_{p_{i}} : ν_{i})$ .
$tail (j) ='C'$: The two tail (confidence interval) probability,
i.e., $p_{i} = P (T_{i} \leq | t_{p_{i}} | : ν_{i}) - P (T_{i} \leq - | t_{p_{i}} | : ν_{i})$ .
$tail (j) ='S'$: The two tail (significance level) probability,
i.e., $p_{i} = P (T_{i} \geq | t_{p_{i}} | : ν_{i}) + P (T_{i} \leq - | t_{p_{i}} | : ν_{i})$ .

Constraint:

tail (j) ='L'

'U'

'C'

'S'

, for

j = 1, 2, \dots, ltail

3: $lp$ – Integer Input

On entry: the length of the array p.

Constraint:

lp > 0

4: $p (lp)$ – Real (Kind=nag_wp) array Input

On entry:

p_{i}

, the probability of the required Student's

t

-distribution as defined by tail with

p_{i} = p (j)

j = ((i - 1) mod lp) + 1

Constraint:

0.0 < p (j) < 1.0

, for

j = 1, 2, \dots, lp

5: $ldf$ – Integer Input

On entry: the length of the array df.

Constraint:

ldf > 0

6: $df (ldf)$ – Real (Kind=nag_wp) array Input

On entry:

ν_{i}

, the degrees of freedom of the Student's

t

-distribution with

ν_{i} = df (j)

j = ((i - 1) mod ldf) + 1

Constraint:

df (j) \geq 1.0

, for

j = 1, 2, \dots, ldf

7: $t (*)$ – Real (Kind=nag_wp) array Output

Note: the dimension of the array t must be at least

\max (ltail, lp, ldf)

On exit:

t_{p_{i}}

, the deviates for the Student's

t

-distribution.

8: $ivalid (*)$ – Integer array Output

Note: the dimension of the array ivalid must be at least

\max (ltail, lp, ldf)

On exit:

ivalid (i)

indicates any errors with the input arguments, with

$ivalid (i) = 0$: No error.
$ivalid (i) = 1$: On entry, invalid value supplied in tail when calculating $t_{p_{i}}$ .
$ivalid (i) = 2$: On entry, $p_{i} \leq 0.0$ , or, $p_{i} \geq 1.0$ .
$ivalid (i) = 3$: On entry, $ν_{i} < 1.0$ .
$ivalid (i) = 4$: The solution has failed to converge. The result returned should represent an approximation to the solution.

9: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

−1

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

1

is recommended. If message printing is undesirable, then the value

1

is recommended. Otherwise, the value

0

is recommended. 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

−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, at least one value of tail, p or df was invalid, or the solution failed to converge.
Check ivalid for more information.

$ifail = 2$: On entry, $array size = ⟨ value ⟩$ .
Constraint: $ltail > 0$ .

$ifail = 3$: On entry, $array size = ⟨ value ⟩$ .
Constraint: $lp > 0$ .

$ifail = 4$: On entry, $array size = ⟨ value ⟩$ .
Constraint: $ldf > 0$ .

$ifail = - 99$: 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.

$ifail = - 399$: 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.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

The results should be accurate to five significant digits, for most argument values. The error behaviour for various argument values is discussed in Hill (1970).

8 Parallelism and Performance

g01tbf is not threaded in any implementation.

9 Further Comments

The value

t_{p_{i}}

may be calculated by using a transformation to the beta distribution and calling g01tef. This routine allows you to set the required accuracy.

10 Example

This example reads the probability, the tail that probability represents and the degrees of freedom for a number of Student's

t

-distributions and computes the corresponding deviates.

g01tb: FL CL CPP AD

NAG FL Interfaceg01tbf (inv_​cdf_​students_​t_​vector)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
g01tbf (inv_cdf_students_t_vector)