g02gdc (Nag_Link link, Nag_IncludeMean mean, Integer n, const double x[], Integer tdx, Integer m, const Integer sx[], Integer ip, const double y[], const double wt[], double offset[], double *scale, double ex_power, double *dev, double *df, double b[], Integer *rank, double se[], double cov[], double v[], Integer tdv, double tol, Integer max_iter, Integer print_iter, const char *outfile, double eps, NagError *fail)

The function may be called by the names: g02gdc, nag_correg_glm_gamma or nag_glm_gamma.

3 Description

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

(a)a set of $n$ observations, $y_{i}$ , from a gamma distribution with probability density function:

$\frac{1}{Γ (ν)} {(\frac{ν y}{μ})}^{ν} \exp (- \frac{ν y}{μ}) \frac{1}{y} .$

$ν$ being constant for the sample.
(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, $μ$ , $η = g (μ)$ . The possible link functions are:
1. (i)power link: $η = μ^{a}$ , for a constant $a$ ,
2. (ii)identity link: $η = μ$ ,
3. (iii)log link: $η = \log μ$ ,
4. (iv)square root link: $η = \sqrt{μ}$ ,
(e)reciprocal link: $η = \frac{1}{μ}$ .

(f)a measure of fit, an adjusted deviance. This is a function related to the deviance, but defined for

y = 0

\sum_{i = 1}^{n} {dev}^{*} (y_{i}, {\hat{μ}}_{i}) = \sum_{i = 1}^{n} 2 {\log ({\hat{μ}}_{i}) + (\frac{y_{i}}{{\hat{μ}}_{i}})}

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 = \sqrt{τ \frac{d η}{d μ}}, where ​ τ = \frac{1}{μ} .

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

g02gdc finds a

Q R

decomposition of

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

, i.e.,

$w^{\frac{1}{2}} X = Q R$ where $R$ is a $p \times p$ triangular matrix and $Q$ is an $n \times p$ 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 \times 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 deviance.

The initial values for the algorithm are obtained by taking

\hat{η} = g (y)

The scale argument,

ν^{−1}

is estimated by a moment estimator:

{\hat{ν}}^{−1} = \sum_{i = 1}^{n} \frac{{[(y_{i} - {\hat{μ}}_{i}) / \hat{μ}]}^{2}}{(n - k)} .

The fit of the model can be assessed by examining and testing the deviance, in particular, by comparing the difference in deviance between nested models, i.e., when one model is a sub-model of the other. The difference in deviance or adjusted deviance between two nested models with known

ν

has, asymptotically, a

χ^{2}

distribution with degrees of freedom given by the difference in the degrees of freedom associated with the two deviances.

The arguments estimates,

\hat{β}

, are asymptotically Normally distributed with variance-covariance matrix:

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

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 \times 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 known as leverages.

The fitted values are given by

\hat{μ} = g^{−1} (\hat{η})

g02gdc also computes the Anscombe residuals,

r

r_{i} = \frac{3 (y_{i}^{\frac{1}{3}} - {\hat{μ}}_{i}^{\frac{1}{3}})}{{\hat{μ}}_{i}^{\frac{1}{3}}}

An option allows the use of prior weights,

ω_{i}

. This gives a model with:

ν_{i} = ν 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 g02gkc after using g02gdc.

Only certain linear combinations of the arguments will have unique estimates, these are known as estimable functions, these can be estimated and tested using g02gnc.

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: $link$ – Nag_Link Input

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

$link = Nag_Expo$: An exponent link is used.
$link = Nag_Iden$: An identity link is used.
$link = Nag_Log$: A log link is used.
$link = Nag_Sqrt$: A square root link is used.
$link = Nag_Reci$: A reciprocal link is used.

Constraint:

link = Nag_Expo

Nag_Iden

Nag_Log

Nag_Sqrt

Nag_Reci

2: $mean$ – Nag_IncludeMean Input

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

$mean = Nag_MeanInclude$: A mean term, (intercept), will be included in the model.
$mean = Nag_MeanZero$: The model will pass through the origin, zero point.

Constraint:

mean = Nag_MeanInclude

Nag_MeanZero

3: $n$ – Integer Input

On entry: the number of observations,

n

Constraint:

n \geq 2

4: $x [n \times tdx]$ – const double Input

On entry:

x [(i - 1) \times tdx + j - 1]

must contain the

i

th observation for the

j

th independent variable, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

5: $tdx$ – Integer Input

On entry: the stride separating matrix column elements in the array x.

Constraint:

tdx \geq m

6: $m$ – Integer Input

On entry: the total number of independent variables.

Constraint:

m \geq 1

7: $sx [m]$ – const Integer Input

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

sx [j - 1] > 0

, then the variable contained in the

j

th column of x is included in the regression model.

Constraints:

$sx [j - 1] \geq 0$ , for $j = 1, 2, \dots, m$ ;
if $mean = Nag_MeanInclude$ , then exactly $ip - 1$ values of sx must be $> 0$ ;
if $mean = Nag_MeanZero$ , then exactly ip values of sx must be $> 0$ .

8: $ip$ – Integer Input

On entry: the number

p

of independent variables in the model, including the mean or intercept if present.

Constraint: ip must be

> 0

9: $y [n]$ – const double Input

On entry: observations on the dependent variable,

y_{i}

, for

i = 1, 2, \dots, n

Constraint:

y [i - 1] \geq 0

, for

i = 1, 2, \dots, n

10: $wt [n]$ – const double Input

On entry: if weighted estimates are required, then wt must contain the weights to be used. Otherwise wt need not be defined and may be set to NULL.

wt [i - 1] = 0.0

, then the

i

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

If wt is NULL, then the effective number of observations is

n

Constraint:

wt is NULL

wt [i - 1] \geq 0.0

, for

i = 1, 2, \dots, n

11: $offset [n]$ – double Input

On entry: if an offset is required then offset must contain the values of the offset

o

. Otherwise offset must be supplied as NULL.

12: $scale$ – double * Input/Output

On entry: the scale argument for the gamma model,

ν^{−1}

scale = 0.0

, then the scale argument is estimated with the function using the formula described in Section 3.

On exit: if on input

scale = 0.0

, then scale contains the estimated value of the scale argument,

{\hat{ν}}^{−1}

. If on input

scale \neq 0.0

, then scale is unchanged on exit.

Constraint:

scale \geq 0.0

13: $ex_power$ – double Input

On entry: if

link = Nag_Expo

then ex_power must contain the power

a

of the exponential.

link \neq Nag_Expo

, ex_power is not referenced.

Constraint: if

link = Nag_Expo

ex_power \neq 0.0

14: $dev$ – double * Output

On exit: the adjusted deviance for the fitted model.

15: $df$ – double * Output

On exit: the degrees of freedom associated with the deviance for the fitted model.

16: $b [ip]$ – double Output

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

\hat{β}

mean = Nag_MeanInclude

, then

b [0]

will contain the estimate of the mean argument and

b [i]

will contain the coefficient of the variable contained in column

j

of x, where

sx [j - 1]

is the

i

th positive value in the array sx.

mean = Nag_MeanZero

, then

b [i - 1]

will contain the coefficient of the variable contained in column

j

of x, where

sx [j - 1]

is the

i

th positive value in the array sx.

17: $rank$ – Integer * Output

On exit: the rank of the independent variables.

If the model is of full rank, then

rank = ip

If the model is not of full rank, then rank is an estimate of the rank of the independent variables. rank 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 rank to be returned as ip.

18: $se [ip]$ – double Output

On exit: the standard errors of the linear arguments.

se [i - 1]

contains the standard error of the parameter estimate in

b [i - 1]

, for

i = 1, 2, \dots, ip

19: $cov [ip \times (ip + 1) / 2]$ – double Output

On exit: the

ip \times (ip + 1) / 2

elements of cov contain 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 (j + 1) / 2 + i]

, for

i = 0, 1, \dots, ip - 1

and

j = i, \dots, ip - 1

20: $v [n \times tdv]$ – double Output

On exit: auxiliary information on the fitted model.

v [(i - 1) \times tdv]

, contains the linear predictor value,

η_{i}

, for

i = 1, 2, \dots, n

v [(i - 1) \times tdv + 1]

, contains the fitted value,

{\hat{μ}}_{i}

, for

i = 1, 2, \dots, n

v [(i - 1) \times tdv + 2]

, contains the variance standardization,

τ_{i}

, for

i = 1, 2, \dots, n

v [(i - 1) \times tdv + 3]

, contains the working weight,

w_{i}

, for

i = 1, 2, \dots, n

v [(i - 1) \times tdv + 4]

, contains the Anscombe residual,

r_{i}

, for

i = 1, 2, \dots, n

v [(i - 1) \times tdv + 5]

, contains the leverage,

h_{i}

, for

i = 1, 2, \dots, n

v [(i - 1) \times tdv + j - 1]

, for

j = 7, 8, \dots, ip + 6

, contains the results of the

Q R

decomposition or the singular value decomposition.

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

rank < ip

, then the first ip rows of columns

7

ip + 6

contain the

P^{*}

matrix.

21: $tdv$ – Integer Input

On entry: the stride separating matrix column elements in the array v.

Constraint:

tdv \geq ip + 6

22: $tol$ – double 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 Deviance). This is approximately an absolute precision if the deviance is small and a relative precision if the deviance is large.

0.0 \leq tol <

machine precision, then the function will use

10 \times

machine precision.

Constraint:

tol \geq 0.0

23: $max_iter$ – Integer Input

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

max_iter = 0

, then a default value of 10 is used.

Constraint:

max_iter \geq 0

24: $print_iter$ – Integer Input

On entry: indicates if the printing of information on the iterations is required and the rate at which printing is produced.

$print_iter \leq 0$

There is no printing.

$print_iter > 0$

The following items are printed every print_iter iterations:

(i)the deviance,
(ii)the current estimates, and
(iii)if the weighted least squares equations are singular then this is indicated.

25: $outfile$ – const char * Input

On entry: a null terminated character string giving the name of the file to which results should be printed. If outfile is NULL or an empty string then the stdout stream is used. Note that the file will be opened in the append mode.

26: $eps$ – double Input

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

0.0 \leq eps <

machine precision, then the function will use machine precision instead.

Constraint:

eps \geq 0.0

27: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_2_INT_ARG_LT: On entry, $tdx = ⟨ value ⟩$ while $m = ⟨ value ⟩$ . These arguments must satisfy $tdx \geq m$ .
NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument link had an illegal value.

On entry, argument mean had an illegal value.
NE_INT_ARG_LT: On entry, $ip = ⟨ value ⟩$ .
Constraint: $ip \geq 1$ .

On entry, $m = ⟨ value ⟩$ .
Constraint: $m \geq 1$ .

On entry, max_iter must not be less than 0: $max_iter = ⟨ value ⟩$ .

On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 2$ .

On entry, $sx [⟨ value ⟩]$ must not be less than 0: $sx [⟨ value ⟩] = ⟨ value ⟩$ .

On entry, $tdv = ⟨ value ⟩$ while $ip = ⟨ value ⟩$ . These arguments must satisfy $tdv \geq ip + 6$ .
NE_IP_GT_OBSERV: ip is greater than the effective number of observations.
NE_IP_INCOMP_SX: ip is incompatible with mean and sx.
NE_LSQ_ITER_NOT_CONV: The iterative weighted least squares has failed to converge in $max_iter = ⟨ value ⟩$ iterations. The value of max_iter could be increased but it may be advantageous to examine the convergence using the print_iter 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.
NE_NOT_APPEND_FILE: Cannot open file $⟨ string ⟩$ for appending.
NE_NOT_CLOSE_FILE: Cannot close file $⟨ string ⟩$ .
NE_RANK_CHANGED: 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.
NE_REAL_ARG_LT: On entry, eps must not be less than 0.0: $eps = ⟨ value ⟩$ .

On entry, scale must not be less than 0.0: $scale = ⟨ value ⟩$ .

On entry, tol must not be less than 0.0: $tol = ⟨ value ⟩$ .

On entry, $wt [⟨ value ⟩]$ must not be less than 0.0: $wt [⟨ value ⟩] = ⟨ value ⟩$ .

On entry, $y [⟨ value ⟩]$ must not be less than 0.0: $y [⟨ value ⟩] = ⟨ value ⟩$ .
NE_REAL_ENUM_ARG_CONS: On entry, $ex_power = 0.0$ , $link = Nag_Expo$ . These arguments must satisfy $link = Nag_Expo$ and $ex_power \neq 0.0$ .
NE_SVD_NOT_CONV: The singular value decomposition has failed to converge.
NE_VALUE_AT_BOUNDARY_D: A fitted value is at a boundary, i.e., $\hat{μ} = 0.0$ . 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.
NE_ZERO_DOF_ERROR: The degrees of freedom for error are $0$ . A saturated model has been fitted.

7 Accuracy

The accuracy is determined by tol as described in Section 5. As the adjusted deviance is a function of

\log μ

the accuracy of the

\hat{β}

's will be a function of tol. tol should, therefore, be set to a smaller value than the accuracy required for

\hat{β}

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

g02gdc is not threaded in any implementation.

9 Further Comments

None.

10 Example

A set of 10 observations from two groups is input and a model for the two groups is fitted.

g02gd: FL CL CPP AD PY MB

NAG CL Interfaceg02gdc (glm_​gamma)

▸▿ 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 CL Interface
g02gdc (glm_gamma)