NAG Library Function Document

nag_glm_est_func (g02gnc)

+− 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

nag_glm_est_func (g02gnc) gives the estimate of an estimable function along with its standard error from the results from fitting a generalized linear model.

2 Specification

#include <nag.h>

#include <nagg02.h>

void	nag_glm_est_func (Integer ip, Integer rank, const double b[], const double cov[], const double v[], Integer tdv, const double f[], Nag_Boolean est, double stat, double sestat, double z, double tol, NagError *fail)

3 Description

nag_glm_est_func (g02gnc) computes the estimates of an estimable function for a general linear regression model which is not of full rank. It is intended for use after a call to nag_glm_normal (g02gac), nag_glm_binomial (g02gbc), nag_glm_poisson (g02gcc) or nag_glm_gamma (g02gdc). An estimable function is a linear combination of the arguments such that it has a unique estimate. For a full rank model all linear combinations of arguments are estimable.

In the case of a model not of full rank the functions use a singular value decomposition (SVD) to find the parameter estimates,

\hat{β}

, and their variance-covariance matrix. Given the upper triangular matrix

R

obtained from the

Q R

decomposition of the independent variables the SVD gives:

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

Q_{*}

and

P

are

p

p

orthogonal matrices. This leads to a solution:

\hat{β} = P_{1} D^{- 1} Q_{*_{1}}^{T} c_{1}

P_{1}

being the first

k

columns of

P

, i.e.,

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

;

Q_{*_{1}}

being the first

k

columns of

Q_{*}

and

c_{1}

being the first

p

elements of

c

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})

as given by nag_glm_normal (g02gac), nag_glm_binomial (g02gbc), nag_glm_poisson (g02gcc) and nag_glm_gamma (g02gdc).

A linear function of the arguments,

F = f^{T} β

, can be tested to see if it is estimable by computing

ζ = P_{0}^{T} f

. If

ζ

is zero, then the function is estimable, if not, the function is not estimable. In practice

|ζ|

is tested against some small quantity

η

Given that

F

is estimable it can be estimated by

f^{T} \hat{β}

and its standard error calculated from the variance-covariance matrix of

\hat{β}

C_{β}

, as

se (F) = \sqrt{f^{T} C_{β} f}

Also a

z

statistic:

z = \frac{f^{T} \hat{β}}{se (F)},

can be computed. The distribution of

z

will be approximately Normal.

4 References

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

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

Searle S R (1971) Linear Models Wiley

5 Arguments

1: ip – IntegerInput

On entry: the number of terms in the linear model,

p

Constraint:

ip \geq 1

2: rank – IntegerInput

On entry: the rank of the independent variables,

k

Constraint:

1 \leq rank \leq ip

3: b[ip] – const doubleInput

On entry: the ip values of the estimates of the arguments of the model,

\hat{β}

4: cov[ $ip \times (ip + 1) / 2$ ] – const doubleInput

On entry: 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

5: v[ $ip \times tdv$ ] – const doubleInput

Note: the

(i, j)

th element of the matrix

V

is stored in

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

On entry: v as returned by nag_glm_normal (g02gac), nag_glm_binomial (g02gbc), nag_glm_poisson (g02gcc) and nag_glm_gamma (g02gdc).

6: tdv – IntegerInput

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

Constraint:

tdv \geq ip + 6

tdv should be as supplied to nag_glm_normal (g02gac), nag_glm_binomial (g02gbc), nag_glm_poisson (g02gcc) or nag_glm_gamma (g02gdc).

7: f[ip] – const doubleInput

On entry: the linear function to be estimated,

f

8: est – Nag_Boolean *Output

On exit: est indicates if the function was estimable.

$est = Nag_TRUE$: The function is estimable.
$est = Nag_FALSE$: The function is not estimable and stat, sestat and z are not set.

9: stat – double *Output

On exit: if

est = Nag_TRUE

, stat contains the estimate of the function,

f^{T} \hat{β}

10: sestat – double *Output

On exit: if

est = Nag_TRUE

, sestat contains the standard error of the estimate of the function,

se (F)

11: z – double *Output

On exit: if

est = Nag_TRUE

, z contains the

z

statistic for the test of the function being equal to zero.

12: tol – doubleInput

On entry: tol is the tolerance value used in the check for estimability,

η

tol \leq 0.0

, then

\sqrt{machine precision}

is used instead.

13: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_2_INT_ARG_GT: On entry, $ip = ⟨value⟩$ while $rank = ⟨value⟩$ . These arguments must satisfy $rank \leq ip$ .
NE_2_INT_ARG_LT: On entry, $tdv = ⟨value⟩$ while $ip = ⟨value⟩$ . These arguments must satisfy $tdv \geq ip + 6$ .
NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_INT_ARG_LT: On entry, $ip = ⟨value⟩$ .
Constraint: $ip \geq 1$ .
On entry, $rank = ⟨value⟩$ .
Constraint: $rank \geq 1$ .
NE_RANK_EQ_IP: On entry, $rank = ip$ . In this case, the boolean variable est is returned as Nag_TRUE and all statistics are calculated.
NE_STDES_ZERO: sestat, the standard error of the estimate of the function, se $(F) = 0.0$ ; probably due to rounding error or due to incorrectly specified input values of cov and f.

7 Accuracy

The computations are believed to be stable.

8 Parallelism and Performance

Not applicable.

9 Further Comments

The value of estimable functions is independent of the solution chosen from the many possible solutions. While nag_glm_est_func (g02gnc) may be used to estimate functions of the arguments of the model as computed by nag_glm_tran_model (g02gkc),

β_{c}

, these must be expressed in terms of the original arguments,

β

. The relation between the two sets of arguments may not be straightforward.

10 Example

A loglinear model is fitted to a 3 by 5 contingency table by nag_glm_poisson (g02gcc). The model consists of terms for for rows and columns. The table is:

\begin{array}{r} 141 & 67 & 114 & 79 & 39 \\ 131 & 66 & 143 & 72 & 35 \\ 36 & 14 & 38 & 28 & 16 \end{array}

The number of functions to be tested is read in, then the linear functions themselves are read in and tested with nag_glm_est_func (g02gnc). The results of nag_glm_est_func (g02gnc) are printed.

NAG Library Function Documentnag_glm_est_func (g02gnc)

+− 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 Library Function Document

nag_glm_est_func (g02gnc)