Integer, Intent (In)	::	n, ldx, m, isx(m), ip
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (In)	::	x(ldx,), t(), off(), wt(), s, a, b(ip), cov(ip*(ip+1)/2)
Real (Kind=nag_wp), Intent (Out)	::	eta(n), seeta(n), pred(n), sepred(n)
Logical, Intent (In)	::	vfobs
Character (1), Intent (In)	::	errfn, link, mean, offset, weight

C Header Interface

#include <nag.h>

void

g02gpf_ (const char *errfn, const char *link, const char *mean, const char *offset, const char *weight, const Integer *n, const double x[], const Integer *ldx, const Integer *m, const Integer isx[], const Integer *ip, const double t[], const double off[], const double wt[], const double *s, const double *a, const double b[], const double cov[], const logical *vfobs, double eta[], double seeta[], double pred[], double sepred[], Integer *ifail, const Charlen length_errfn, const Charlen length_link, const Charlen length_mean, const Charlen length_offset, const Charlen length_weight)

The routine may be called by the names g02gpf or nagf_correg_glm_predict.

3 Description

A generalized linear model consists of the following elements:

(i)A suitable distribution for the dependent variable $y$ .
(ii)A linear model, with linear predictor $η = X β$ , where $X$ is a matrix of independent variables and $β$ a column vector of $p$ parameters.
(iii)A link function $g (.)$ between the expected value of $y$ and the linear predictor, that is $E (y) = μ = g (η)$ .

In order to predict from a generalized linear model, that is estimate a value for the dependent variable,

y

, given a set of independent variables

X

, the matrix

X

must be supplied, along with values for the parameters

β

and their associated variance-covariance matrix,

C

. Suitable values for

β

and

C

are usually estimated by first fitting the prediction model to a training dataset with known responses, using for example g02gaf, g02gbf, g02gcf or g02gdf. The predicted variable, and its standard error can then be obtained from:

\hat{y} = g^{- 1} (η), se (\hat{y}) = \sqrt{{(\frac{δ g^{- 1} (x)}{δ x})}_{η}} se (η) + I_{fobs} Var (y)

where

η = o + X β, se (η) = diag \sqrt{X C X^{T}},

o

is a vector of offsets and

I_{fobs} = 0

, if the variance of future observations is not taken into account, and

1

otherwise. Here

diag A

indicates the diagonal elements of matrix

A

If required, the variance for the

i

th future observation,

Var (y_{i})

, can be calculated as:

Var (y_{i}) = \frac{ϕ V (θ)}{w_{i}}

where

w_{i}

is a weight,

ϕ

is the scale (or dispersion) parameter, and

V (θ)

is the variance function. Both the scale parameter and the variance function depend on the distribution used for the

y

, with:

Poisson	$V (θ) = μ_{i}$ , $ϕ = 1$
binomial	$V (θ) = \frac{μ_{i} (t_{i} - μ_{i})}{t_{i}}$ , $ϕ = 1$
Normal	$V (θ) = 1$
gamma	$V (θ) = μ_{i}^{2}$

In the cases of a Normal and gamma error structure, the scale parameter (

ϕ

), is supplied by you. This value is usually obtained from the routine used to fit the prediction model. In many cases, for a Normal error structure,

ϕ = {\hat{σ}}^{2}

, i.e., the estimated variance.

4 References

McCullagh P and Nelder J A (1983) Generalized Linear Models Chapman and Hall

5 Arguments

1: $errfn$ – Character(1) Input

On entry: indicates the distribution used to model the dependent variable,

y

$errfn ='B'$: The binomial distribution is used.
$errfn ='G'$: The gamma distribution is used.
$errfn ='N'$: The Normal (Gaussian) distribution is used.
$errfn ='P'$: The Poisson distribution is used.

Constraint:

errfn ='B'

'G'

'N'

'P'

2: $link$ – Character(1) Input

On entry: indicates which link function is to be used.

$link ='C'$: A complementary log-log link is used.
$link ='E'$: An exponent link is used.
$link ='G'$: A logistic link is used.
$link ='I'$: An identity link is used.
$link ='L'$: A log link is used.
$link ='P'$: A probit link is used.
$link ='R'$: A reciprocal link is used.
$link ='S'$: A square root link is used.

Details on the functional form of the different links can be found in the G02 Chapter Introduction.

Constraints:

if $errfn ='B'$ , $link ='C'$ , $'G'$ or $'P'$ ;
otherwise $link ='E'$ , $'I'$ , $'L'$ , $'R'$ or $'S'$ .

3: $mean$ – Character(1) Input

On entry: indicates if a mean term is to be included.

$mean ='M'$: A mean term, intercept, will be included in the model.
$mean ='Z'$: The model will pass through the origin, zero-point.

Constraint:

mean ='M'

'Z'

4: $offset$ – Character(1) Input

On entry: indicates if an offset is required.

$offset ='Y'$: An offset must be supplied in off.
$offset ='N'$: off is not referenced.

Constraint:

offset ='Y'

'N'

5: $weight$ – Character(1) Input

On entry: if

vfobs = .TRUE.

indicates if weights are used, otherwise weight is not referenced.

$weight ='U'$: No weights are used.
$weight ='W'$: Weights are used and must be supplied in wt.

Constraint: if

vfobs = .TRUE.

weight ='U'

'W'

6: $n$ – Integer Input

On entry:

n

, the number of observations.

Constraint:

n \geq 1

7: $x (ldx *)$ – Real (Kind=nag_wp) array Input

Note: the second dimension of the array x must be at least

m

On entry:

x (i, j)

must contain the

i

th observation for the

j

th independent variable, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

8: $ldx$ – Integer Input

On entry: the first dimension of the array x as declared in the (sub)program from which g02gpf is called.

Constraint:

ldx \geq n

9: $m$ – Integer Input

On entry:

m

, the total number of independent variables.

Constraint:

m \geq 1

10: $isx (m)$ – Integer array Input

On entry: indicates which independent variables are to be included in the model.

isx (j) > 0

, the variable contained in the

j

th column of x is included in the regression model.

Constraints:

$isx (j) \geq 0$ , for $j = 1, 2, \dots, m$ ;
if $mean ='M'$ , exactly $ip - 1$ values of isx must be $> 0$ ;
if $mean ='Z'$ , exactly ip values of isx must be $> 0$ .

11: $ip$ – Integer Input

On entry: the number of independent variables in the model, including the mean or intercept if present.

Constraint:

ip > 0

12: $t (*)$ – Real (Kind=nag_wp) array Input

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

n

errfn ='B'

On entry: if

errfn ='B'

t (i)

must contain the binomial denominator,

t_{i}

, for the

i

th observation.

Otherwise t is not referenced.

Constraint: if

errfn ='B'

t (i) \geq 0.0

, for

i = 1, 2, \dots, n

13: $off (*)$ – Real (Kind=nag_wp) array Input

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

n

offset ='Y'

On entry: if

offset ='Y'

off (i)

must contain the offset

o_{i}

, for the

i

th observation.

Otherwise off is not referenced.

14: $wt (*)$ – Real (Kind=nag_wp) array Input

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

n

weight ='W'

and

vfobs = .TRUE.

On entry: if

weight ='W'

and

vfobs = .TRUE.

wt (i)

must contain the weight,

w_{i}

, for the

i

th observation.

If the variance of future observations is not included in the standard error of the predicted variable, wt is not referenced.

Constraint: if

vfobs = .TRUE.

and

weight ='W'

wt (i) \geq 0

., for

i = 1, 2, \dots, i

15: $s$ – Real (Kind=nag_wp) Input

On entry: if

errfn ='N'

'G'

and

vfobs = .TRUE.

, the scale parameter,

ϕ

Otherwise s is not referenced and

ϕ = 1

Constraint: if

errfn ='N'

'G'

and

vfobs = .TRUE.

s > 0.0

16: $a$ – Real (Kind=nag_wp) Input

On entry: if

link ='E'

, a must contain the power of the exponential.

link \neq'E'

, a is not referenced.

Constraint: if

link ='E'

a \neq 0.0

17: $b (ip)$ – Real (Kind=nag_wp) array Input

On entry: the model parameters,

β

mean ='M'

b (1)

must contain the mean parameter and

b (i + 1)

the coefficient of the variable contained in the

j

th independent x, where

isx (j)

is the

i

th positive value in the array isx.

mean ='Z'

b (i)

must contain the coefficient of the variable contained in the

j

th independent x, where

isx (j)

is the

i

th positive value in the array isx.

18: $cov (ip \times (ip + 1) / 2)$ – Real (Kind=nag_wp) array Input

On entry: the upper triangular part of the variance-covariance matrix,

C

, of the model parameters. This matrix should be supplied packed by column, i.e., the covariance between parameters

β_{i}

and

β_{j}

, that is the values stored in

b (i)

and

b (j)

, should be supplied in

cov (j \times (j - 1) / 2 + i)

, for

i = 1, 2, \dots, ip

and

j = i, \dots, ip

Constraint: the matrix represented in cov must be a valid variance-covariance matrix.

19: $vfobs$ – Logical Input

On entry: if

vfobs = .TRUE.

, the variance of future observations is included in the standard error of the predicted variable (i.e.,

I_{fobs} = 1

), otherwise

I_{fobs} = 0

20: $eta (n)$ – Real (Kind=nag_wp) array Output

On exit: the linear predictor,

η

21: $seeta (n)$ – Real (Kind=nag_wp) array Output

On exit: the standard error of the linear predictor,

se (η)

22: $pred (n)$ – Real (Kind=nag_wp) array Output

On exit: the predicted value,

\hat{y}

23: $sepred (n)$ – Real (Kind=nag_wp) array Output

On exit: the standard error of the predicted value,

se (\hat{y})

. If

pred (i)

could not be calculated, g02gpf returns

ifail = 22

, and

sepred (i)

is set to

- 99.0

24: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

- 1 or 1

. If you are unfamiliar with this argument you should refer to Section 4 in the Introduction to the NAG Library FL Interface 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 arguments may be useful even if

ifail \neq 0

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

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

Note: in some cases g02gpf may return useful information.

$ifail = 1$: On entry, $errfn = 〈value〉$ .
Constraint: $errfn ='B'$ , $'G'$ , $'N'$ or $'P'$ .

$ifail = 2$: On entry, $errfn = 〈value〉$ and $link = 〈value〉$ .
Constraint: if $errfn ='B'$ , $link ='C'$ , $'G'$ or $'P'$ ,
otherwise, $link ='E'$ , $'I'$ , $'L'$ , $'R'$ or $'S'$ .

$ifail = 3$: On entry, $mean = 〈value〉$ .
Constraint: $mean ='M'$ or $'Z'$ .

$ifail = 4$: On entry, $offset = 〈value〉$ .
Constraint: $offset ='Y'$ or $'N'$ .

$ifail = 5$: On entry, $weight = 〈value〉$ .
Constraint: if $vfobs = .TRUE.$ , $weight ='U'$ or $'W'$ .

$ifail = 6$: On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .

$ifail = 8$: On entry, $ldx = 〈value〉$ and $n = 〈value〉$ .
Constraint: $ldx \geq n$ .

$ifail = 9$: On entry, $m = 〈value〉$ .
Constraint: $m \geq 1$ .

$ifail = 10$: On entry, $isx (〈value〉) < 0$ .
Constraint: $isx (j) \geq 0.0$ , for $j = 1, 2, \dots, m$ .

$ifail = 11$: On entry, $ip = 〈value〉$ .
Constraint: $ip > 0$ .

$ifail = 12$: On entry, $t (〈value〉) = 〈value〉$ .
Constraint: $t (i) \geq 0.0$ , for all $i$ .

$ifail = 14$: On entry, $wt (〈value〉) = 〈value〉$ .
Constraint: $wt (i) \geq 0.0$ , for all $i$ .

$ifail = 15$: On entry, $s = 〈value〉$ .
Constraint: $s > 0.0$ .

$ifail = 16$: On entry, $a = 0.0$ .
Constraint: if $link ='E'$ , $a \neq 0.0$ .

$ifail = 18$: On entry, $cov (〈value〉) = 〈value〉$ .
Constraint: $cov (i) \geq 0.0$ for at least one diagonal element.

$ifail = 22$: At least one predicted value could not be calculated as required. sepred is set to $- 99.0$ for affected predicted values.

$ifail = - 99$: 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

Not applicable.

8 Parallelism and Performance

g02gpf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

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

None.

10 Example

The model

y = \frac{1}{β_{1} + β_{2} x} + ε

is fitted to a training dataset with five observations. The resulting model is then used to predict the response for two new observations.

Interfaces: FL CL AD

NAG FL Interface Introduction

G02 (Correg) Chapter Contents

G02 (Correg) Chapter Introduction

g02gp: FL CL AD

NAG FL Interfaceg02gpf (glm_​predict)

▸▿ 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
g02gpf (glm_predict)