Integer, Intent (In)	::	n, ldx, m, isx(m), ip, ldv, maxit, iprint
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	idf, irank
Real (Kind=nag_wp), Intent (In)	::	x(ldx,m), y(n), wt(*), a, tol, eps
Real (Kind=nag_wp), Intent (Inout)	::	s, v(ldv,ip+7)
Real (Kind=nag_wp), Intent (Out)	::	rss, b(ip), se(ip), cov(ip(ip+1)/2), wk((ipip+3*ip+22)/2)
Character (1), Intent (In)	::	link, mean, offset, weight

C Header Interface

#include nagmk26.h

void

g02gaf_ ( 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 y[], const double wt[], double *s, const double *a, double *rss, Integer *idf, double b[], Integer *irank, double se[], double cov[], double v[], const Integer *ldv, const double *tol, const Integer *maxit, const Integer *iprint, const double *eps, double wk[], Integer *ifail, const Charlen length_link, const Charlen length_mean, const Charlen length_offset, const Charlen length_weight)

3

Description

A generalized linear model with Normal errors consists of the following elements:

(a)

a set of

n

observations,

y_{i}

, from a Normal distribution with probability density function:

\frac{1}{\sqrt{2 π} σ} \exp (- \frac{{(y - μ)}^{2}}{2 σ^{2}}),

where

μ

is the mean and

σ^{2}

is the variance.

(b)

X

, a set of

p

independent variables for each observation,

x_{1}, x_{2}, \dots, x_{p}

(c)

a linear model:

η = \sum β_{j} x_{j} .

(d)

a link between the linear predictor,

η

, and the mean of the distribution,

μ

, i.e.,

η = g (μ)

. The possible link functions are:

(i)	exponent link: $η = μ^{a}$ , for a constant $a$ ,
(ii)	identity link: $η = μ$ ,
(iii)	log link: $η = \log μ$ ,
(iv)	square root link: $η = \sqrt{μ}$ ,
(v)	reciprocal link: $η = \frac{1}{μ}$ .

(e)

a measure of fit, the residual sum of squares

= \sum {(y_{i} - {\hat{μ}}_{i})}^{2}

The linear arguments are estimated by iterative weighted least squares. An adjusted dependent variable,

z

, is formed:

z = η + (y - μ) \frac{d η}{d μ}

and a working weight,

w

w = {(\frac{d η}{d μ})}^{2} .

At each iteration an approximation to the estimate of

β

\hat{β}

, is found by the weighted least squares regression of

z

X

with weights

w

g02gaf finds a

Q R

decomposition of

w^{\frac{1}{2}} X

, i.e.,

w^{\frac{1}{2}} X = Q R

where

R

is a

p

p

triangular matrix and

Q

is an

n

p

column orthogonal matrix.

R

is of full rank, then

\hat{β}

is the solution to

R \hat{β} = Q^{T} w^{\frac{1}{2}} z .

R

is not of full rank a solution is obtained by means of a singular value decomposition (SVD) of

R

R = Q_{*} (\begin{matrix} D & 0 \\ 0 & 0 \end{matrix}) P^{T},

where

D

is a

k

k

diagonal matrix with nonzero diagonal elements,

k

being the rank of

R

and

w^{\frac{1}{2}} X

This gives the solution

\hat{β} = P_{1} D^{- 1} (\begin{matrix} Q_{*} & 0 \\ 0 & I \end{matrix}) Q^{T} w^{\frac{1}{2}} z

P_{1}

being the first

k

columns of

P

, i.e.,

P = (P_{1} P_{0})

The iterations are continued until there is only a small change in the residual sum of squares.

The initial values for the algorithm are obtained by taking

\hat{η} = g (y) .

The fit of the model can be assessed by examining and testing the residual sum of squares, in particular comparing the difference in residual sums of squares between nested models, i.e., when one model is a sub-model of the other.

Let

{RSS}_{f}

be the residual sum of squares for the full model with degrees of freedom

ν_{f}

and let

{RSS}_{s}

be the residual sum of squares for the sub-model with degrees of freedom

ν_{s}

then:

F = \frac{({RSS}_{s} - {RSS}_{f}) / (ν_{s} - ν_{f})}{{RSS}_{f} / ν_{f}},

has, approximately, an

F

-distribution with (

ν_{s} - ν_{f}

ν_{f}

degrees of freedom.

The parameter estimates,

\hat{β}

, are asymptotically Normally distributed with variance-covariance matrix:

$C = R^{- 1} {R^{- 1}}^{T} σ^{2}$ in the full rank case,
otherwise $C = P_{1} D^{- 2} P_{1}^{T} σ^{2}$

The residuals and influence statistics can also be examined.

The estimated linear predictor

\hat{η} = X \hat{β}

, can be written as

H w^{\frac{1}{2}} z

for an

n

n

matrix

H

. The

i

th diagonal elements of

H

h_{i}

, give a measure of the influence of the

i

th values of the independent variables on the fitted regression model. These are sometimes known as leverages.

The fitted values are given by

\hat{μ} = g^{- 1} (\hat{η})

g02gaf also computes the residuals,

r

r_{i} = y_{i} - {\hat{μ}}_{i} .

An option allows prior weights

ω_{i}

to be used; this gives a model with:

σ_{i}^{2} = \frac{σ^{2}}{ω_{i}} .

In many linear regression models the first term is taken as a mean term or an intercept, i.e.,

x_{i, 1} = 1

, for

i = 1, 2, \dots, n

; this is provided as an option.

Often only some of the possible independent variables are included in a model, the facility to select variables to be included in the model is provided.

If part of the linear predictor can be represented by a variable with a known coefficient, then this can be included in the model by using an offset,

o

η = o + \sum β_{j} x_{j} .

If the model is not of full rank the solution given will be only one of the possible solutions. Other estimates may be obtained by applying constraints to the arguments. These solutions can be obtained by using g02gkf after using g02gaf. Only certain linear combinations of the arguments will have unique estimates; these are known as estimable functions and can be estimated and tested using g02gnf.

Details of the SVD are made available, in the form of the matrix

P^{*}

P^{*} = (\begin{matrix} D^{- 1} P_{1}^{T} \\ P_{0}^{T} \end{matrix}) .

4

References

Cook R D and Weisberg S (1982) Residuals and Influence in Regression Chapman and Hall

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

5

Arguments

1: $link$ – Character(1)Input

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

$link ='E'$: An exponent link is used.
$link ='I'$: An identity link is used. You are advised not to use g02gaf with an identity link as g02daf provides a more efficient way of fitting such a model.
$link ='L'$: A log link is used.
$link ='S'$: A square root link is used.
$link ='R'$: A reciprocal link is used.

Constraint:

link ='E'

'I'

'L'

'S'

'R'

2: $mean$ – 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'

3: $offset$ – Character(1)Input

On entry: indicates if an offset is required.

$offset ='Y'$: An offset is required and the offsets must be supplied in the seventh column of v.
$offset ='N'$: No offset is required.

Constraint:

offset ='Y'

'N'

4: $weight$ – Character(1)Input

On entry: indicates if prior weights are to be used.

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

Constraint:

weight ='U'

'W'

5: $n$ – IntegerInput

On entry:

n

, the number of observations.

Constraint:

n \geq 2

6: $x (ldx, m)$ – Real (Kind=nag_wp) arrayInput

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

7: $ldx$ – IntegerInput

On entry: the first dimension of the array x as declared in the (sub)program from which g02gaf is called.

Constraint:

ldx \geq n

8: $m$ – IntegerInput

On entry:

m

, the total number of independent variables.

Constraint:

m \geq 1

9: $isx (m)$ – Integer arrayInput

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 $> 0$ .

10: $ip$ – IntegerInput

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

Constraint:

ip > 0

11: $y (n)$ – Real (Kind=nag_wp) arrayInput

On entry: the observations on the dependent variable,

y_{i}

, for

i = 1, 2, \dots, n

12: $wt (*)$ – Real (Kind=nag_wp) arrayInput

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

n

weight ='W'

, and at least

1

otherwise.

On entry: if

weight ='W'

, wt must contain the weights to be used with the model,

ω_{i}

. If

wt (i) = 0.0

, the

i

th observation is not included in the model, in which case the effective number of observations is the number of observations with nonzero weights.

weight ='U'

, wt is not referenced and the effective number of observations is

n

Constraint: if

weight ='W'

wt (i) \geq 0.0

, for

i = 1, 2, \dots, n

13: $s$ – Real (Kind=nag_wp)Input/Output

On entry: the scale argument for the model,

σ^{2}

s = 0.0

, the scale argument is estimated with the routine using the residual mean square.

On exit: if on input

s = 0.0

, s contains the estimated value of the scale argument,

{\hat{σ}}^{2}

If on input

s \neq 0.0

, s is unchanged on exit.

Constraint:

s \geq 0.0

14: $a$ – 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

15: $rss$ – Real (Kind=nag_wp)Output

On exit: the residual sum of squares for the fitted model.

16: $idf$ – IntegerOutput

On exit: the degrees of freedom associated with the residual sum of squares for the fitted model.

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

On exit: the estimates of the parameters of the generalized linear model,

\hat{β}

mean ='M'

b (1)

will contain the estimate of the mean parameter and

b (i + 1)

will contain the coefficient of the variable contained in column

j

x

, where

isx (j)

is the

i

th positive value in the array isx.

mean ='Z'

b (i)

will contain the coefficient of the variable contained in column

j

x

, where

isx (j)

is the

i

th positive value in the array isx.

18: $irank$ – IntegerOutput

On exit: the rank of the independent variables.

If the model is of full rank,

irank = ip

If the model is not of full rank, irank is an estimate of the rank of the independent variables. irank is calculated as the number of singular values greater than

eps \times

(largest singular value). It is possible for the SVD to be carried out but for irank to be returned as ip.

19: $se (ip)$ – Real (Kind=nag_wp) arrayOutput

On exit: the standard errors of the linear parameters.

se (i)

contains the standard error of the parameter estimate in

b (i)

, for

i = 1, 2, \dots, ip

20: $cov (ip \times (ip + 1) / 2)$ – Real (Kind=nag_wp) arrayOutput

On exit: the upper triangular part of the variance-covariance matrix of the ip parameter estimates given in b. They are stored packed by column, i.e., the covariance between the parameter estimate given in

b (i)

and the parameter estimate given in

b (j)

j \geq i

, is stored in

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

21: $v (ldv, ip + 7)$ – Real (Kind=nag_wp) arrayInput/Output

On entry: if

offset ='N'

, v need not be set.

offset ='Y'

v (i, 7)

, for

i = 1, 2, \dots, n

, must contain the offset values

o_{i}

. All other values need not be set.

On exit: auxiliary information on the fitted model.

$v (i, 1)$	contains the linear predictor value, $η_{i}$ , for $i = 1, 2, \dots, n$ .
$v (i, 2)$	contains the fitted value, ${\hat{μ}}_{i}$ , for $i = 1, 2, \dots, n$ .
$v (i, 3)$	is only included for consistency with other routines. $v (i, 3) = 1.0$ , for $i = 1, 2, \dots, n$ .
$v (i, 4)$	contains the square root of the working weight, $w_{i}^{\frac{1}{2}}$ , for $i = 1, 2, \dots, n$ .
$v (i, 5)$	contains the residual, $r_{i}$ , for $i = 1, 2, \dots, n$ .
$v (i, 6)$	contains the leverage, $h_{i}$ , for $i = 1, 2, \dots, n$ .
$v (i, 7)$	contains the offset, for $i = 1, 2, \dots, n$ . If $offset ='N'$ , all values will be zero.
$v (i, j)$ ,	for $j = 8, \dots, ip + 7$ , contains the results of the $Q R$ decomposition or the singular value decomposition.

If the model is not of full rank, i.e.,

irank < ip

, the first ip rows of columns

8

ip + 7

contain the

P^{*}

matrix.

22: $ldv$ – IntegerInput

On entry: the first dimension of the array v as declared in the (sub)program from which g02gaf is called.

Constraint:

ldv \geq n

23: $tol$ – Real (Kind=nag_wp)Input

On entry: indicates the accuracy required for the fit of the model.

The iterative weighted least squares procedure is deemed to have converged if the absolute change in deviance between interactions is less than

tol \times (1.0 + current residual sum of squares)

. This is approximately an absolute precision if the residual sum of squares is small and a relative precision if the residual sum of squares is large.

0.0 \leq tol < machine precision

, g02gaf will use

10 \times machine precision

Constraint:

tol \geq 0.0

24: $maxit$ – IntegerInput

On entry: the maximum number of iterations for the iterative weighted least squares.

maxit = 0

, a default value of

10

is used.

Constraint:

maxit \geq 0

25: $iprint$ – IntegerInput

On entry: indicates if the printing of information on the iterations is required.

$iprint \leq 0$

There is no printing.

$iprint > 0$

Every iprint iteration, the following is printed:

	the deviance,
	the current estimates,
	and if the weighted least squares equations are singular then this is indicated.

When printing occurs the output is directed to the current advisory message unit (see x04abf).

26: $eps$ – Real (Kind=nag_wp)Input

On entry: the value of eps is used to decide if the independent variables are of full rank and, if not, what is the rank of the independent variables. The smaller the value of eps the stricter the criterion for selecting the singular value decomposition.

0.0 \leq eps < machine precision

, the routine will use machine precision instead.

Constraint:

eps \geq 0.0

27: $wk ((ip \times ip + 3 \times ip + 22) / 2)$ – Real (Kind=nag_wp) arrayWorkspace

28: $ifail$ – 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, 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).

Note: g02gaf may return useful information for one or more of the following detected errors or warnings.

Errors or warnings detected by the routine:

$ifail = 1$: On entry, $a = 0.0$ and $link ='E'$ .

On entry, $eps = 〈value〉$ .
Constraint: $eps \geq 0.0$ .

On entry, $ip = 〈value〉$ .
Constraint: $ip \geq 1$ .

On entry, $ldv = 〈value〉$ and $n = 〈value〉$ .
Constraint: $ldv \geq n$ .

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

On entry, $link = 〈value〉$ .
Constraint: $link ='E'$ , $'I'$ , $'L'$ , $'S'$ or $'R'$

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

On entry, $maxit = 〈value〉$ .
Constraint: $maxit \geq 0$ .

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

On entry, $n = 〈value〉$ .
Constraint: $n \geq 2$ .

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

On entry, $s = 〈value〉$ .
Constraint: $s \geq 0.0$ .

On entry, $tol = 〈value〉$ .
Constraint: $tol \geq 0.0$ .

On entry, $weight = 〈value〉$ .
Constraint: $weight ='W'$ or $'U'$ .

$ifail = 2$: On entry, $wt (〈value〉) < 0.0$ .
Constraint: $wt (i) \geq 0.0$ , for $i = 1, 2, \dots, n$ .

$ifail = 3$: Number of requested x-variables greater than n.

On entry, ip incompatible with number of nonzero values of isx: $ip = 〈value〉$ .

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

$ifail = 4$: A fitted value is at a boundary. This will only occur with $link ='L'$ , $'R'$ or $'E'$ . This may occur if there are small values of $y$ and the model is not suitable for the data. The model should be reformulated with, perhaps, some observations dropped.

$ifail = 5$: SVD solution failed to converge.

$ifail = 6$: The iterative weighted least squares has failed to converge in maxit (or default $10$ ) iterations. The value of maxit could be increased but it may be advantageous to examine the convergence using the iprint option. This may indicate that the convergence is slow because the solution is at a boundary in which case it may be better to reformulate the model.

$ifail = 7$: The rank of the model has changed during the weighted least squares iterations. The estimate for $β$ returned may be reasonable, but you should check how the deviance has changed during iterations.

$ifail = 8$: The degrees of freedom for error are $0$ . A saturated model has been fitted.

$ifail = - 99$: 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

The accuracy is determined by tol as described in Section 5. As the residual sum of squares is a function of

μ^{2}

the accuracy of the

\hat{β}

will depend on the link used and may be of the order

\sqrt{tol}

8

Parallelism and Performance

g02gaf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

g02gaf 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

for a sample of five observations.

NAG Library Routine Document

g02gaf (glm_normal)

▸▿ 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

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