Integer, Intent (In)	::	n, m, ldx, isx(m), nvar, ng, popt, lprob, riter, sopt, lds, sds
Integer, Intent (Inout)	::	niter, ifail
Real (Kind=nag_wp), Intent (In)	::	x(ldx,m), tol
Real (Kind=nag_wp), Intent (Inout)	::	prob(lprob,ng), g(nvar,ng), s(lds,sds,*), f(n,ng)
Real (Kind=nag_wp), Intent (Out)	::	w(ng), loglik

C Header Interface

#include <nag.h>

void

g03gaf_ (const Integer *n, const Integer *m, const double x[], const Integer *ldx, const Integer isx[], const Integer *nvar, const Integer *ng, const Integer *popt, double prob[], const Integer *lprob, Integer *niter, const Integer *riter, double w[], double g[], const Integer *sopt, double s[], const Integer *lds, const Integer *sds, double f[], const double *tol, double *loglik, Integer *ifail)

The routine may be called by the names g03gaf or nagf_mv_gaussian_mixture.

3 Description

A Normal (Gaussian) mixture model is a weighted sum of

k

group Normal densities given by,

p (x ∣ w, μ, Σ) = \sum_{j = 1}^{k} w_{j} g (x ∣ μ_{j}, Σ_{j}), x \in ℝ^{p}

where:

$x$ is a $p$ -dimensional object of interest;
$w_{j}$ is the mixture weight for the $j$ th group and $\sum_{j = 1}^{k} w_{j} = 1$ ;
$μ_{j}$ is a $p$ -dimensional vector of means for the $j$ th group;
$Σ_{j}$ is the covariance structure for the $j$ th group;
$g (\cdot)$ is the $p$ -variate Normal density:
$g (x ∣ μ_{j}, Σ_{j}) = \frac{1}{{(2 π)}^{p / 2} {| Σ_{j} |}^{1 / 2}} \exp [- \frac{1}{2} (x - μ_{j}) Σ_{j}^{- 1} {(x - μ_{j})}^{T}] .$

Optionally, the (co)variance structure may be pooled (common to all groups) or calculated for each group, and may be full or diagonal.

4 References

Hartigan J A (1975) Clustering Algorithms Wiley

5 Arguments

1: $n$ – Integer Input

On entry:

n

, the number of objects. There must be more objects than parameters in the model.

Constraints:

if $sopt = 1$ , $n > ng \times (nvar \times nvar + nvar)$ ;
if $sopt = 2$ , $n > nvar \times (ng + nvar)$ ;
if $sopt = 3$ , $n > 2 \times ng \times nvar$ ;
if $sopt = 4$ , $n > nvar \times (ng + 1)$ ;
if $sopt = 5$ , $n > nvar \times ng + 1$ .

2: $m$ – Integer Input

On entry: the total number of variables in array x.

Constraint:

m \geq 1

3: $x (ldx, m)$ – Real (Kind=nag_wp) array Input

On entry:

x (i, j)

must contain the value of the

j

th variable for the

i

th object, for

i = 1, 2, \dots, n

and

j = 1, 2, \dots, m

4: $ldx$ – Integer Input

On entry: the first dimension of the array x as declared in the (sub)program from which g03gaf is called.

Constraint:

ldx \geq n

5: $isx (m)$ – Integer array Input

On entry: if

nvar = m

all available variables are included in the model and isx is not referenced; otherwise the

j

th variable will be included in the analysis if

isx (j) = 1

and excluded if

isx (j) = 0

, for

j = 1, 2, \dots, m

Constraint: if

nvar \neq m

isx (j) = 1

for nvar values of

j

and

isx (j) = 0

for the remaining

m - nvar

values of

j

, for

j = 1, 2, \dots, m

6: $nvar$ – Integer Input

On entry:

p

, the number of variables included in the calculations.

Constraint:

1 \leq nvar \leq m

7: $ng$ – Integer Input

On entry:

k

, the number of groups in the mixture model.

Constraint:

ng \geq 1

8: $popt$ – Integer Input

On entry: if

popt = 1

, the initial membership probabilities in prob are set internally; otherwise these probabilities must be supplied.

9: $prob (lprob, ng)$ – Real (Kind=nag_wp) array Input/Output

On entry: if

popt \neq 1

prob (i, j)

is the probability that the

i

th object belongs to the

j

th group. (These probabilities are normalised internally.)

On exit:

prob (i, j)

is the probability of membership of the

i

th object to the

j

th group for the fitted model.

10: $lprob$ – Integer Input

On entry: the first dimension of the array prob as declared in the (sub)program from which g03gaf is called.

Constraint:

lprob \geq n

11: $niter$ – Integer Input/Output

On entry: the maximum number of iterations.

Suggested value:

15

On exit: the number of completed iterations.

Constraint:

niter \geq 1

12: $riter$ – Integer Input

On entry: if

riter > 0

, membership probabilities are rounded to

0.0

1.0

after the completion of the first riter iterations.

Suggested value:

0

13: $w (ng)$ – Real (Kind=nag_wp) array Output

On exit:

w_{j}

, the mixing probability for the

j

th group.

14: $g (nvar, ng)$ – Real (Kind=nag_wp) array Output

On exit:

g (i, j)

gives the estimated mean of the

i

th variable in the

j

th group.

15: $sopt$ – Integer Input

On entry: determines the (co)variance structure:

$sopt = 1$: Groupwise covariance matrices.
$sopt = 2$: Pooled covariance matrix.
$sopt = 3$: Groupwise variances.
$sopt = 4$: Pooled variances.
$sopt = 5$: Overall variance.

Constraint:

sopt = 1

2

3

4

5

16: $s (lds, sds, *)$ – Real (Kind=nag_wp) array Output

Note: the last dimension of the array s must be at least

ng

sopt = 1

, and at least

1

otherwise.

On exit: if

sopt = 1

s (i, j, k)

gives the

(i, j)

th element of the

k

th group.

sopt = 2

s (i, j, 1)

gives the

(i, j)

th element of the pooled covariance.

sopt = 3

s (j, k, 1)

gives the

j

th variance in the

k

th group.

sopt = 4

s (j, 1, 1)

gives the

j

th pooled variance.

sopt = 5

s (1, 1, 1)

gives the overall variance.

17: $lds$ – Integer Input

On entry: the first dimension of the (co)variance structure s.

Constraints:

if $sopt = 5$ , $lds = 1$ ;
otherwise $lds = nvar$ .

18: $sds$ – Integer Input

On entry: the second dimension of the (co)variance structure s.

Constraints:

if $sopt = 1$ or $2$ , $sds \geq nvar$ ;
if $sopt = 3$ , $sds \geq ng$ ;
if $sopt = 4$ or $5$ , $sds \geq 1$ .

19: $f (n, ng)$ – Real (Kind=nag_wp) array Output

On exit:

f (i, j)

gives the

p

-variate Normal (Gaussian) density of the

i

th object in the

j

th group.

20: $tol$ – Real (Kind=nag_wp) Input

On entry: iterations cease the first time an improvement in log-likelihood is less than tol. If

tol \leq 0

a value of

10^{- 3}

is used.

21: $loglik$ – Real (Kind=nag_wp) Output

On exit: the log-likelihood for the fitted mixture model.

22: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

- 1

1

to set behaviour on detection of an error; these values have no effect when no error is detected.

A value of

0

causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of

- 1

means that an error message is printed while a value of

1

means that it is not.

If halting is not appropriate, the value

- 1

1

is recommended. If message printing is undesirable, then the value

1

is recommended. Otherwise, the value

0

is recommended. 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, $n = ⟨ value ⟩$ and $p = ⟨ value ⟩$ .
Constraint: $n > p$ , the number of parameters, i.e., too few objects have been supplied for the model.

$ifail = 2$: On entry, $m = ⟨ value ⟩$ .
Constraint: $m \geq 1$ .

$ifail = 4$: On entry, $ldx = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $ldx \geq n$ .

$ifail = 5$: On entry, $nvar = ⟨ value ⟩$ and $m = ⟨ value ⟩$ .
Constraint: $1 \leq nvar \leq m$ .

$ifail = 6$: On entry, $nvar \neq m$ and isx is invalid.

$ifail = 7$: On entry, $ng = ⟨ value ⟩$ .
Constraint: $ng \geq 1$ .

$ifail = 8$: On entry, $popt \neq 1$ or $2$ .

$ifail = 9$: On entry, row $⟨ value ⟩$ of supplied prob does not sum to $1$ .

$ifail = 10$: On entry, $lprob = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $lprob \geq n$ .

$ifail = 11$: On entry, $niter = ⟨ value ⟩$ .
Constraint: $niter \geq 1$ .

$ifail = 16$: On entry, $sopt \neq 1$ , $2$ , $3$ , $4$ or $5$ .

$ifail = 18$: On entry, $lds = ⟨ value ⟩$ was invalid.

$ifail = 19$: On entry, $sds = ⟨ value ⟩$ was invalid.

$ifail = 44$: A covariance matrix is not positive definite, try a different initial allocation.

$ifail = 45$: An iteration cannot continue due to an empty group, try a different initial allocation.

$ifail = - 99$: 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

g03gaf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

g03gaf 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

This example fits a Gaussian mixture model with pooled covariance structure to New Haven schools test data, see Table 5.1 (p. 118) in Hartigan (1975).

g03ga: FL CL CPP AD

NAG FL Interfaceg03gaf (gaussian_​mixture)

▸▿ 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
g03gaf (gaussian_mixture)