NAG Library Routine Document

G02JCF

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Parameters

6 Error Indicators and Warnings

7 Accuracy

+− 8 Further Comments

8.1 Construction of the fixed effects design matrix, X

8.2 Construction of random effects design matrix, Z

8.3 The RNDM parameter

9 Example

1 Purpose

G02JCF preprocesses a dataset prior to fitting a linear mixed effects regression model of the following form via either G02JDF or G02JEF.

2 Specification

SUBROUTINE G02JCF (

WEIGHT, N, NCOL, DAT, LDDAT, LEVELS, Y, WT, FIXED, LFIXED, NRNDM, RNDM, LDRNDM, NFF, NLSV, NRF, RCOMM, LRCOMM, ICOMM, LICOMM, IFAIL)

INTEGER	N, NCOL, LDDAT, LEVELS(NCOL), FIXED(LFIXED), LFIXED, NRNDM, RNDM(LDRNDM,NRNDM), LDRNDM, NFF, NLSV, NRF, LRCOMM, ICOMM(LICOMM), LICOMM, IFAIL
REAL (KIND=nag_wp)	DAT(LDDAT,NCOL), Y(N), WT(*), RCOMM(LRCOMM)
CHARACTER(1)	WEIGHT

3 Description

G02JCF must be called prior to fitting a linear mixed effects regression model with either G02JDF or G02JEF.

The model fitting routines G02JDF and G02JEF fit a model of the following form:

y = X β + Z ν + ε

where	$y$ is a vector of $n$ observations on the dependent variable,
	$X$ is an $n$ by $p$ design matrix of fixed independent variables,
	$β$ is a vector of $p$ unknown fixed effects,
	$Z$ is an $n$ by $q$ design matrix of random independent variables,
	$ν$ is a vector of length $q$ of unknown random effects,
	$ε$ is a vector of length $n$ of unknown random errors,

and

ν

and

ε

are Normally distributed with expectation zero and variance/covariance matrix defined by

Var [\begin{matrix} ν \\ ε \end{matrix}] = [\begin{matrix} G & 0 \\ 0 & R \end{matrix}]

where

R = σ_{R}^{2} I

I

is the

n \times n

identity matrix and

G

is a diagonal matrix.

Case weights can be incorporated into the model by replacing

X

and

Z

with

W_{c}^{1 / 2} X

and

W_{c}^{1 / 2} Z

respectively where

W_{c}

is a diagonal weight matrix.

4 References

None.

5 Parameters

1: WEIGHT – CHARACTER(1)Input

On entry: indicates if weights are to be used.

$WEIGHT ='U'$: No weights are used.
$WEIGHT ='W'$: Case weights are used and must be supplied in array WT.

Constraint:

WEIGHT ='U'

'W'

2: N – INTEGERInput

On entry:

n

, the number of observations.

The effective number of observations, that is the number of observations with nonzero weight (see WT for more detail), must be greater than the number of fixed effects in the model (as returned in NFF).

Constraint:

N \geq 1

3: NCOL – INTEGERInput

On entry: the number of columns in the data matrix, DAT.

Constraint:

NCOL \geq 0

4: DAT(LDDAT,NCOL) – REAL (KIND=nag_wp) arrayInput

On entry: a matrix of data, with

DAT (i, j)

holding the

i

th observation on the

j

th variable. The two design matrices

X

and

Z

are constructed from DAT and the information given in FIXED (for

X

) and RNDM (for

Z

Constraint: if

LEVELS (j) \neq 1, 1 \leq DAT (i, j) \leq LEVELS (j)

5: LDDAT – INTEGERInput

On entry: the first dimension of the array DAT as declared in the (sub)program from which G02JCF is called.

Constraint:

LDDAT \geq N

6: LEVELS(NCOL) – INTEGER arrayInput

On entry:

LEVELS (i)

contains the number of levels associated with the

i

th variable held in DAT.

If the

i

th variable is continuous or binary (i.e., only takes the values zero or one) then

LEVELS (i)

must be set to

1

. Otherwise the

i

th variable is assumed to take an integer value between

1

and

LEVELS (i)

, (i.e., the

i

th variable is discrete with

LEVELS (i)

levels).

Constraint:

LEVELS (i) \geq 1

, for

i = 1, 2, \dots, NCOL

7: Y(N) – REAL (KIND=nag_wp) arrayInput

On entry:

y

, the vector of observations on the dependent variable.

8: WT( $*$ ) – REAL (KIND=nag_wp) arrayInput

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

N

WEIGHT ='W'

On entry: if

WEIGHT ='W'

, WT must contain the diagonal elements of the weight matrix

W_{c}

WT (i) = 0.0

, the

i

th observation is not included in the model and 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

9: FIXED(LFIXED) – INTEGER arrayInput

On entry: defines the structure of the fixed effects design matrix,

X

$FIXED (1)$: The number of variables, $N_{F}$ , to include as fixed effects (not including the intercept if present).
$FIXED (2)$: The fixed intercept flag which must contain $1$ if a fixed intercept is to be included and $0$ otherwise.
$FIXED (2 + i)$: The column of DAT holding the $i$ th fixed variable, for $i = 1, 2, \dots, FIXED (1)$ .

See Section 8.1 for more details on the construction of

X

Constraints:

$FIXED (1) \geq 0$ ;
$FIXED (2) = 0 or 1$ ;
$1 \leq FIXED (2 + i) \leq NCOL$ , for $i = 1, 2, \dots, FIXED (1)$ .

10: LFIXED – INTEGERInput

On entry: length of the vector FIXED.

Constraint:

LFIXED \geq 2 + FIXED (1)

11: NRNDM – INTEGERInput

On entry: the second dimension of the array RNDM as declared in the (sub)program from which G02JCF is called.

Constraint:

NRNDM > 0

12: RNDM(LDRNDM,NRNDM) – INTEGER arrayInput

On entry:

RNDM (i, j)

defines the structure of the random effects design matrix,

Z

. The

b

th column of RNDM defines a block of columns in the design matrix

Z

$RNDM (1, b)$: The number of variables, $N_{R_{b}}$ , to include as random effects in the $b$ th block (not including the random intercept if present).
$RNDM (2, b)$: The random intercept flag which must contain $1$ if block $b$ includes a random intercept and $0$ otherwise.
$RNDM (2 + i, b)$: The column of DAT holding the $i$ th random variable in the $b$ th block, for $i = 1, 2, \dots, RNDM (1, b)$ .
$RNDM (3 + N_{R_{b}}, b)$: The number of subject variables, $N_{S_{b}}$ , for the $b$ th block. The subject variables define the nesting structure for this block.
$RNDM (3 + N_{R_{b}} + i, b)$: The column of DAT holding the $i$ th subject variable in the $b$ th block, for $i = 1, 2, \dots, RNDM (3 + N_{R_{b}}, b)$ .

See Section 8.2 for more details on the construction of

Z

Constraints:

$RNDM (1, b) \geq 0$ ;
$RNDM (2, b) = 0 or 1$ ;
at least one random variable or random intercept must be specified in each block, i.e., $RNDM (1, b) + RNDM (2, b) > 0$ ;
the column identifiers associated with the random variables must be in the range $1$ to NCOL, i.e., $1 \leq RNDM (2 + i, b) \leq NCOL$ , for $i = 1, 2, \dots, RNDM (1, b)$ ;
$RNDM (3 + N_{R_{b}}, b) \geq 0$ ;
the column identifiers associated with the subject variables must be in the range $1$ to NCOL, i.e., $1 \leq RNDM (3 + N_{R_{b}} + i, b) \leq NCOL$ , for $i = 1, 2, \dots, RNDM (3 + N_{R_{b}}, b)$ .

13: LDRNDM – INTEGERInput

On entry: the first dimension of the array RNDM as declared in the (sub)program from which G02JCF is called.

Constraint:

LDRNDM \geq max_{b} (3 + N_{R_{b}} + N_{S_{b}})

14: NFF – INTEGEROutput

On exit:

p

, the number of fixed effects estimated, i.e., the number of columns in the design matrix

X

15: NLSV – INTEGEROutput

On exit: the number of levels for the overall subject variable (see Section 8.2 for a description of what this means). If there is no overall subject variable,

NLSV = 1

16: NRF – INTEGEROutput

On exit: the number of random effects estimated in each of the overall subject blocks. The number of columns in the design matrix

Z

is given by

q = NRF \times NLSV

17: RCOMM(LRCOMM) – REAL (KIND=nag_wp) arrayCommunication Array

On exit: communication array as required by the analysis routines G02JDF and G02JEF.

18: LRCOMM – INTEGERInput

On entry: the dimension of the array RCOMM as declared in the (sub)program from which G02JCF is called.

Constraint:

LRCOMM \geq NRF \times NLSV + NFF + NFF \times NLSV + NRF \times NLSV + NFF + 2

19: ICOMM(LICOMM) – INTEGER arrayCommunication Array

On exit: if

LICOMM = 2

ICOMM (1)

holds the minimum required value for LICOMM and

ICOMM (2)

holds the minimum required value for LRCOMM, otherwise ICOMM is a communication array as required by the analysis routines G02JDF and G02JEF.

20: LICOMM – INTEGERInput

On entry: the dimension of the array ICOMM as declared in the (sub)program from which G02JCF is called.

Constraint:

LICOMM = 2

LICOMM \geq 34 + N_{F} \times (MFL + 1) + NRNDM \times MNR \times MRL + (LRNDM + 2) \times NRNDM + NCOL + LDID \times LB,

where

$MNR = \max_{b} (N_{R_{b}})$ ,
$MFL = \max_{i} (LEVELS (FIXED (2 + i)))$ ,
$MRL = \max_{b, i} (LEVELS (RNDM (2 + i, b)))$ ,
$LDID = \max_{b} N_{S_{b}}$ ,
$LB = NFF + NRF \times NLSV$ , and
$LRNDM = max_{b} (3 + N_{R_{b}} + N_{S_{b}})$

21: IFAIL – INTEGERInput/Output

On entry: IFAIL must be set to

0

- 1 ​ or ​ 1

. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction 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, if you are not familiar with this parameter, the recommended value is

0

. 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, $WEIGHT \neq'W'$ or $'U'$ .

$IFAIL = 2$: On entry, $N < 1$ .

$IFAIL = 3$: On entry, $NCOL < 0$ .

$IFAIL = 4$: On entry, at least one data point for variable $j$ has data taking a value less than one or greater than $LEVELS (j)$ .

$IFAIL = 5$: On entry, $LDDAT < N$ .

$IFAIL = 6$: On entry, $LEVELS (i)$ is less than one for at least one $i$ .

$IFAIL = 8$: On entry, there is at least one negative weight.

$IFAIL = 9$: On entry, number of fixed parameters is less than zero.

$IFAIL = 10$: On entry, LFIXED is too small.

$IFAIL = 11$: On entry, $NRNDM < 1$ .

$IFAIL = 12$: On entry, number of random parameters for at least one random statement is less than zero.

$IFAIL = 13$: On entry, LDRNDM is too small.

$IFAIL = 18$: On entry, LRCOMM is too small.

$IFAIL = 20$: On entry, LICOMM is too small.

$IFAIL = 102$: On entry, more fixed factors than observations.

$IFAIL = 108$: On entry, no observations due to zero weights.

$IFAIL = 109$: On entry, invalid value for fixed intercept flag.

$IFAIL = 112$: On entry, invalid value for random intercept flag in at least one random statement.

$IFAIL = 209$: On entry, the identifier associated with at least one fixed variable, as supplied in FIXED is less than one or greater than NCOL.

$IFAIL = 212$: On entry, must be at least one parameter, or an intercept in each random statement.

$IFAIL = 312$: On entry, the identifier associated with random variable $j$ in random statement $i$ , is less than one or greater than NCOL.

$IFAIL = 412$: On entry, number of subject parameters for random statement $i < 0$ .

$IFAIL = 512$: On entry, nesting variable $j$ in random statement $i$ has less than two levels.

7 Accuracy

Not applicable.

8 Further Comments

8.1 Construction of the fixed effects design matrix, $X$

Let

$N_{F}$ denote the number of fixed variables, that is $FIXED (1) = N_{F}$ ;
$F_{j}$ denote the $j$ th fixed variable, that is the vector of values held in the $k$ th column of DAT when $FIXED (2 + j) = k$ ;
$F_{i j}$ denote the $i$ th element of $F_{j}$ ;
$L (F_{j})$ denote the number of levels for $F_{j}$ , that is $L (F_{j}) = LEVELS (FIXED (2 + j))$ ;
$D_{v} (F_{j})$ denoted an indicator function that returns a vector of values whose $i$ th element is $1$ if $F_{i j} = v$ and $0$ otherwise.

The design matrix for the fixed effects,

X

, is constructed as follows:

set $k$ to one and the flag $done_first$ to false;
if a fixed intercept is included, that is FIXED2=1,
- set the first column of $X$ to a vector of $1$ s;
- set $k = k + 1$ ;
- set $done_first$ to true;
loop over each fixed variable, so for each j=1,2,…,NF,
- if LFj=1,
  - set the $k$ th column of $X$ to be $F_{j}$ ;
  - set $k = k + 1$ ;
- else
  - if done_first is false then
    - set the $L (F_{j})$ columns, $k$ to $k + L (F_{j}) - 1$ , of $X$ to $D_{v} (F_{j})$ , for $v = 1, 2, \dots, L (F_{j})$ ;
    - set $k = k + L (F_{j})$ ;
    - set $done_first$ to true;
  - else
    - set the $L (F_{j}) - 1$ columns, $k$ to $k + L (F_{j}) - 2$ , of $X$ to $D_{v} (F_{j})$ , for $v = 2, 3, \dots, L (F_{j})$ ;
    - set $k = k + L (F_{j}) - 1$ .

The number of columns in the design matrix,

X

, is therefore given by

p = 1 + \sum_{j = 1}^{N_{F}} (LEVELS (FIXED (2 + j)) - 1) .

This quantity is returned in NFF.

In summary, G02JCF converts all non-binary categorical variables (i.e., where

L (F_{j}) > 1

) to dummy variables. If a fixed intercept is included in the model then the first level of all such variables is dropped. If a fixed intercept is not included in the model then the first level of all such variables, other than the first, is dropped. The variables are added into the model in the order they are specified in FIXED.

8.2 Construction of random effects design matrix, $Z$

Let

$N_{R_{b}}$ denote the number of random variables in the $b$ th random statement, that is $N_{R_{b}} = RNDM (1, b)$ ;
$R_{j b}$ denote the $j$ th random variable from the $b$ th random statement, that is the vector of values held in the $k$ th column of DAT when $RNDM (2 + j, b) = k$ ;
$R_{i j b}$ denote the $i$ th element of $R_{j b}$ ;
$L (R_{j b})$ denote the number of levels for $R_{j b}$ , that is $L (R_{j b}) = LEVELS (RNDM (2 + j, b))$ ;
$D_{v} (R_{j b})$ denoted an indicator function that returns a vector of values whose $i$ th element is $1$ if $R_{i j b} = v$ and $0$ otherwise;
$N_{S_{b}}$ denote the number of subject variables in the $b$ th random statement, that is $N_{S_{b}} = RNDM (3 + N_{R_{b}}, b)$ ;
$S_{j b}$ denote the $j$ th subject variable from the $b$ th random statement, that is the vector of values held in the $k$ th column of DAT when $RNDM (3 + N_{R_{b}} + j, b) = k$ ;
$S_{i j b}$ denote the $i$ th element of $S_{j b}$ ;
$L (S_{j b})$ denote the number of levels for $S_{j b}$ , that is $L (S_{j b}) = LEVELS (RNDM (3 + N_{R_{b}} + j, b))$ ;
$I_{b} (s_{1}, s_{2}, \dots, s_{N_{S_{b}}})$ denoted an indicator function that returns a vector of values whose $i$ th element is $1$ if $S_{i j b} = s_{j}$ for all $j = 1, 2, \dots, N_{S_{b}}$ and $0$ otherwise.

The design matrix for the random effects,

Z

, is constructed as follows:

set $k$ to one;
loop over each random statement, so for each b=1,2,…,NRNDM,
- loop over each level of the last subject variable, so for each sNSb=1,2,…,LRNSb b,
  - ⋮
    - loop over each level of the second subject variable, so for each s2=1,2,…,LR2b,
      - loop over each level of the first subject variable, so for each $s_{1} = 1, 2, \dots, L (R_{1 b})$ ,
        if a random intercept is included, that is $RNDM (2, b) = 1$ ,
        set the $k$ th column of $Z$ to $I_{b} (s_{1}, s_{2}, \dots, s_{N_{S_{b}}})$ ;
        set $k = k + 1$ ;
        loop over each random variable in the $b$ th random statement, so for each $j = 1, 2, \dots, N_{R_{b}}$ ,
        if $L (R_{j b}) = 1$ ,
        set the $k$ th column of $Z$ to $R_{j b} \times I_{b} (s_{1}, s_{2}, \dots, s_{N_{S_{b}}})$ where $\times$ indicates an element-wise multiplication between the two vectors, $R_{j b}$ and $I_{b} (\dots)$ ;
        set $k = k + 1$ ;
        else
        set the $L (R_{b j})$ columns, $k$ to $k + L (R_{b j})$ , of $Z$ to $D_{v} (R_{j b}) \times I_{b} (s_{1}, s_{2}, \dots, s_{N_{S_{b}}})$ , for $v = 1, 2, \dots, L (R_{j b})$ . As before, $\times$ indicates an element-wise multiplication between the two vectors, $D_{v} (\dots)$ and $I_{b} (\dots)$ ;
        set $k = k + L (R_{j b})$ .

In summary, each column of RNDM defines a block of consecutive columns in

Z

. G02JCF converts all non-binary categorical variables (i.e., where

L (R_{j b})

L (S_{j b}) > 1

) to dummy variables. All random variables defined within a column of RNDM are nested within all subject variables defined in the same column of RNDM. In addition each of the subject variables are nested within each other, starting with the first (i.e., each of the

R_{j b}, j = 1, 2, \dots, N_{R_{b}}

are nested within

S_{1 b}

which in turn is nested within

S_{2 b}

, which in turn is nested within

S_{3 b}

, etc.).

If the last subject variable in each column of RNDM are the same (i.e.,

S_{N_{S_{1}} 1} = S_{N_{S_{2}} 2} = \dots = S_{N_{S_{b}} b}

) then all random effects in the model are nested within this variable. In such instances the last subject variable (

S_{N_{S_{1}} 1}

) is called the overall subject variable. The fact that all of the random effects in the model are nested within the overall subject variable means that

Z^{T} Z

is block diagonal in structure. This fact can be utilised to improve the efficiency of the underlying computation and reduce the amount of internal storage required. The number of levels in the overall subject variable is returned in

NLSV = L (S_{N_{S_{1}} 1})

If the last

k

subject variables in each column of RNDM are the same, for

k > 1

then the overall subject variable is defined as the interaction of these

k

variables and

NLSV = \prod_{j = N_{S_{1}} - k + 1}^{N_{S_{1}}} L (S_{j 1}) .

If there is no overall subject variable then

NLSV = 1

The number of columns in the design matrix

Z

is given by

q = NRF \times NLSV

8.3 The RNDM parameter

To illustrate some additional points about the RNDM parameter, we assume that we have a dataset with three discrete variables,

V_{1}

V_{2}

and

V_{3}

, with

2, 4

and

3

levels respectively, and that

V_{1}

is in the first column of DAT,

V_{2}

in the second and

V_{3}

the third. Also assume that we wish to fit a model containing

V_{1}

along with

V_{2}

nested within

V_{3}

, as random effects. In order to do this the RNDM matrix requires two columns:

RNDM = (\begin{matrix} 1 & 1 \\ 0 & 0 \\ 1 & 2 \\ 0 & 1 \\ 0 & 3 \end{matrix})

The first column,

(1, 0, 1, 0, 0)

, indicates one random variable (

RNDM (1, 1) = 1

), no intercept (

RNDM (2, 1) = 0

), the random variable is in the first column of DAT (

RNDM (3, 1) = 1

), there are no subject variables; as no nesting is required for

V_{1}

(

RNDM (4, 1) = 0

). The last element in this column is ignored.

The second column,

(1, 0, 2, 1, 3)

, indicates one random variable (

RNDM (1, 2) = 1

), no intercept (

RNDM (2, 2) = 0

), the random variable is in the second column of DAT

(RNDM (3, 2) = 2)

, there is one subject variable (

RNDM (4, 2) = 1

), and the subject variable is in the third column of DAT

(RNDM (5, 2) = 3)

The corresponding

Z

matrix would have

14

columns, with

2

coming from

V_{1}

and

12

(

4 \times 3

) from

V_{2}

nested within

V_{3}

. The, symmetric,

Z^{T} Z

matrix has the form

(\begin{matrix} - & - & - & - & - & - & - & - & - & - & - & - & - & - \\ - & - & - & - & - & - & - & - & - & - & - & - & - & - \\ - & - & - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ - & - & - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ - & - & - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ - & - & - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ - & - & 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 \\ - & - & 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 \\ - & - & 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 \\ - & - & 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 \\ - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - \\ - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - \\ - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - \\ - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - \end{matrix})

where

0

indicates a structural zero, i.e., it always takes the value

0

, irrespective of the data, and

-

a value that is not a structural zero. The first two rows and columns of

Z^{T} Z

correspond to

V_{1}

. The block diagonal matrix in the 12 rows and columns in the bottom right correspond to

V_{2}

nested within

V_{3}

. With the

4 \times 4

blocks corresponding to the levels of

V_{2}

. There are three blocks as the subject variable (

V_{3}

) has three levels.

The model fitting routines, G02JDF and G02JEF, use the sweep algorithm to calculate the log likelihood function for a given set of variance components. This algorithm consists of moving down the diagonal elements (called pivots) of a matrix which is similar in structure to

Z^{T} Z

, and updating each element in that matrix. When using the

k

diagonal element of a matrix

A

, an element

a_{i j}, i \neq k, j \neq k

, is adjusted by an amount equal to

a_{i k} a_{i j} / a_{k k}

. This process can be referred to as sweeping on the

k

th pivot. As there are no structural zeros in the first row or column of the above

Z^{T} Z

, sweeping on the first pivot of

Z^{T} Z

would alter each element of the matrix and therefore destroy the structural zeros, i.e., we could no longer guarantee they would be zero.

Reordering the RNDM matrix to

RNDM = (\begin{matrix} 1 & 1 \\ 0 & 0 \\ 2 & 1 \\ 1 & 0 \\ 3 & 0 \end{matrix})

i.e., the swapping the two columns, results in a

Z^{T} Z

matrix of the form

(\begin{matrix} - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - \\ - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - \\ - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - \\ - & - & - & - & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - \\ 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 & - & - \\ 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 & - & - \\ 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 & - & - \\ 0 & 0 & 0 & 0 & - & - & - & - & 0 & 0 & 0 & 0 & - & - \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - & - & - \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - & - & - \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - & - & - \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - & - & - & - & - & - \\ - & - & - & - & - & - & - & - & - & - & - & - & - & - \\ - & - & - & - & - & - & - & - & - & - & - & - & - & - \end{matrix})

This matrix is identical to the previous one, except the first two rows and columns have become the last two rows and columns. Sweeping a matrix,

A = \{a_{i j}\}

, of this form on the first pivot will only affect those elements

a_{i j}

, where

a_{i 1} \neq 0 ​ and ​ a_{1 j} \neq 0

, which is only the

13

th and

14

th row and columns, and the top left hand block of

4

rows and columns. The block diagonal nature of the first

12

rows and columns therefore greatly reduces the amount of work the algorithm needs to perform.

G02JCF constructs the

Z^{T} Z

as specified by the RNDM matrix, and does not attempt to reorder it to improve performance. Therefore for best performance some thought is required on what ordering to use. In general it is more efficient to structure RNDM in such a way that the first row relates to the deepest level of nesting, the second to the next level, etc..