Documentation Example 14 for PROC GLIMMIX
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: gmxex14 */
/* TITLE: Documentation Example 14 for PROC GLIMMIX */
/* Generalized Poisson Mixed Model for Overdispersed */
/* Count Data */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: Generalized linear mixed models */
/* User-defined log likelihood */
/* Adaptive Gauss-Hermite quadrature */
/* PROCS: GLIMMIX */
/* DATA: Simulated counts */
/* */
/* SUPPORT: Oliver Schabenberger */
/* REF: Joe, H. and Zhu, R. (2005) */
/* Generalized Poisson Distribution: the Property of */
/* Mixture of Poisson and Comparison with Negative */
/* Binomial Distribution */
/* Biometrical Journal, 47, 219--229 */
/* MISC: */
/****************************************************************/
data counts;
input ni @@;
sub = _n_;
do i=1 to ni;
input x y @@;
output;
end;
datalines;
1 29 0
6 2 0 82 5 33 0 15 2 35 0 79 0
19 81 0 18 0 85 0 99 0 20 0 26 2 29 0 91 2 37 0 39 0 9 1 33 0
3 0 60 0 87 2 80 0 75 0 3 0 63 1
9 18 0 64 0 80 0 0 0 58 0 7 0 81 0 22 3 50 0
15 91 0 2 1 14 0 5 2 27 1 8 1 95 0 76 0 62 0 26 2 9 0 72 1
98 0 94 0 23 1
2 34 0 95 0
18 48 1 5 0 47 0 44 0 27 0 88 0 27 0 68 0 84 0 86 0 44 0 90 0
63 0 27 0 47 0 25 0 72 0 62 1
13 28 1 31 0 63 0 14 0 74 0 44 0 75 0 65 0 74 1 84 0 57 0 29 0
41 0
9 42 0 8 0 91 0 20 0 23 0 22 0 96 0 83 0 56 0
3 64 0 64 1 15 0
4 5 0 73 2 50 1 13 0
2 0 0 41 0
20 21 0 58 0 5 0 61 1 28 0 71 0 75 1 94 16 51 4 51 2 74 0 1 1
34 0 7 0 11 0 60 3 31 0 75 0 62 0 54 1
2 66 1 13 0
5 83 7 98 1 11 1 28 0 18 0
17 29 5 79 0 39 2 47 2 80 1 19 0 37 0 78 1 26 0 72 1 6 0 50 3
50 4 97 0 37 2 51 0 45 0
17 47 0 57 0 33 0 47 0 2 0 83 0 74 0 93 0 36 0 53 0 26 0 86 0
6 0 17 0 30 0 70 1 99 0
7 91 0 25 1 51 4 20 0 61 1 34 0 33 2
14 60 0 87 0 94 0 29 0 41 0 78 0 50 0 37 0 15 0 39 0 22 0 82 0
93 0 3 0
16 68 0 26 1 19 0 60 1 93 3 65 0 16 0 79 0 14 0 3 1 90 0 28 3
82 0 34 0 30 0 81 0
19 48 3 48 1 43 2 54 0 45 9 53 0 14 0 92 5 21 1 20 0 73 0 99 0
66 0 86 2 63 0 10 0 92 14 44 1 74 0
8 34 1 44 0 62 0 21 0 7 0 17 0 0 2 49 0
13 11 0 27 2 16 1 12 3 52 1 55 0 2 6 89 5 31 5 28 3 51 5 54 13
64 0
9 3 0 36 0 57 0 77 0 41 0 39 0 55 0 57 0 88 1
7 2 0 80 0 41 1 20 0 2 0 27 0 40 0
18 73 1 66 0 10 0 42 0 22 0 59 9 68 0 34 1 96 0 30 0 13 0 35 0
51 2 47 0 60 1 55 4 83 3 38 0
17 96 0 40 0 34 0 59 0 12 1 47 0 93 0 50 0 39 0 97 0 19 0 54 0
11 0 29 0 70 2 87 0 47 0
13 59 0 96 0 47 1 64 0 18 0 30 0 37 0 36 1 69 0 78 1 47 1 86 0
88 0
15 66 0 45 1 96 1 17 0 91 0 4 0 22 0 5 2 47 0 38 0 80 0 7 1
38 1 33 0 52 0
12 84 6 60 1 33 1 92 0 38 0 6 0 43 3 13 2 18 0 51 0 50 4 68 0
;
proc glimmix data=counts method=quad;
class sub;
model y = x / link=log s dist=poisson;
random int / subject=sub;
run;
proc glimmix data=counts method=quad;
class sub;
model y = x / link=log s;
random int / subject=sub;
xi = (1 - 1/exp(_phi_));
_variance_ = _mu_ / (1-xi)/(1-xi);
if (_mu_=.) or (_linp_ = .) then _logl_ = .;
else do;
mustar = _mu_ - xi*(_mu_ - y);
if (mustar < 1E-12) or (_mu_*(1-xi) < 1e-12) then
_logl_ = -1E20;
else do;
_logl_ = log(_mu_*(1-xi)) + (y-1)*log(mustar) -
mustar - lgamma(y+1);
end;
end;
run;
proc glimmix data=counts method=quad;
class sub;
model y = x / link=log s;
random int / subject=sub;
xi = (1 - 1/exp(_phi_));
_variance_ = _mu_ / (1-xi)/(1-xi);
if (_mu_=.) or (_linp_ = .) then _logl_ = .;
else do;
mustar = _mu_ - xi*(_mu_ - y);
if (mustar < 1E-12) or (_mu_*(1-xi) < 1e-12) then
_logl_ = -1E20;
else do;
_logl_ = log(_mu_*(1-xi)) + (y-1)*log(mustar) -
mustar - lgamma(y+1);
end;
end;
covtest 'H:phi = 0' . 0 / est;
run;
