Revision 1016 trunk/examples/admbre/glmmadmb/glmmadmb.tpl
glmmadmb.tpl (revision 1016)  

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// 20110324 version from Dave Fournier 

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// modified 20110427 Hans Skaug, BMB 

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DATA_SECTION 

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init_int n // Number of observations 

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init_int p_y // Dimension of y(i) (multivariate response) 

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init_matrix y(1,n,1,p_y) // Observation matrix 

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init_int p // Number of fixed effects 

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init_matrix X(1,n,1,p) // Design matrix for fixed effects 

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init_int M // Number of RE blocks (crossed terms) 

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init_ivector q(1,M) // Number of levels of the grouping variable per RE block; Can be skipped 

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init_ivector m(1,M) // Number of random effects parameters within each block 

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int sum_mq // sum(m*q), calculated below: should be read from R 

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init_int ncolZ 

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init_matrix Z(1,n,1,ncolZ) // Design matrix for random effects 

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init_imatrix I(1,n,1,ncolZ) // Index vectors into joint RE vector "u" for each 

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init_ivector cor_flag(1,M) // Indicator for whether each RE block should be correlated 

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init_ivector cor_block_start(1,M) // Not used: remove 

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init_ivector cor_block_stop(1,M) // Not used: remove 

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init_int numb_cor_params // Total number of correlation parameters to be estimated 

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init_int like_type_flag // 0 poisson 1 binomial 2 negative binomial 3 Gamma 4 beta 5 gaussian 6 truncated poisson 7 trunc NB 8 logistic 9 betabinomial 

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init_int link_type_flag // 0 log 1 logit 2 probit 3 inverse 4 cloglog 5 identity 

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init_int rlinkflag // robust link function? 

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init_int no_rand_flag // 0 have random effects 1 no random effects 

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init_int zi_flag // Zero inflation (zi) flag: 1=zi, 0=no zi 

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// init_int zi_model_flag // ZI varies among groups/covariates? 

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// init_matrix G(1,n,1,ncolG) // Design matrix for zeroinflation (fixed effects) 

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// TESTING: remove eventually? 

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init_int zi_kluge // apply zi=0.001? 

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init_int nbinom1_flag // 1=NBinom1, 0=NBinom2 

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init_int intermediate_maxfn // Not used 

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init_int has_offset // Offset in linear predictor: 0=no offset, 1=with offset 

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init_vector offset(1,n) // Offset vector 

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// Makes design matrix X orthogonal to improve numeric stability 

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matrix rr(1,n,1,6) 

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matrix phi(1,p,1,p) 

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LOC_CALCS 

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int i,j; 

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phi.initialize(); 

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for (i=1;i<=p;i++) 

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{ 

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phi(i,i)=1.0; 

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} 

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dmatrix trr=trans(rr); 

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trr(6).fill_seqadd(1,1); 

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rr=trans(trr); 

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dmatrix TX(1,p,1,n); 

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TX=trans(X); 

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for (i=1;i<=p;i++) 

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{ 

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double tmp=norm(TX(i)); 

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TX(i)/=tmp; 

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phi(i)/=tmp; 

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for (j=i+1;j<=p;j++) 

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{ 

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double a=TX(j)*TX(i); 

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TX(j)=a*TX(i); 

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phi(j)=a*phi(i); 

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} 

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} 

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X=trans(TX); 

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sum_mq = 0; 

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for (i=1;i<=M;i++) 

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sum_mq += m(i)*q(i); 

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ofstream ofs("phi.rep"); 

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for (i=1; i<=p; i++) 

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{ 

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for (j=1; j<=p; j++) 

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{ 

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ofs << phi(i,j) << " "; 

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} 

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ofs << endl; 

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} 

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ofs << endl; 

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INITIALIZATION_SECTION 

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tmpL 1.0 

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tmpL1 0.0 

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log_alpha 1 

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pz .001 

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PARAMETER_SECTION 

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LOC_CALCS 

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// BMB: FIXME: do we need this? formerly disallowed for binomial (was like_type_flag 2); 

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// would be problematic for binary data but otherwise OK. Should test in R code 

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// if(zi_flag && (like_type_flag>=2)) 

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// { 

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// cerr << "Zero inflation not allowed for this response type" << endl; 

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// ad_exit(1); 

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// } 
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