compilation error related to ifelse operator
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Charles Driver
Nov 1, 2016, 9:57:01 AM11/1/16
to Stan users mailing list
I haven't worked out what is wrong yet, but here is an R script to generate a compilation error. The problem is related to the conditional operator here:
mcholcor[rowi,coli] * (rowi==coli ? 0 : mat[rowi,coli-1]) * sqrt(1-square(mcholcor[rowi,coli-1]));
I have fixed my logic and script but so far as I understand you like these reported.
require(rstan)
sm2 <- '
functions{
row_vector vecpower(row_vector vec, real power){
row_vector[num_elements(vec)] out;
for(i in 1:num_elements(vec)){
out[i] = vec[i]^power;
}
return out;
}
vector vecsqrt(vector vec){
vector[num_elements(vec)] out;
for(i in 1:num_elements(vec)){
out[i]=sqrt(vec[i]);
}
return out;
}
matrix sdpcorr2cov_lp(matrix mat,int chol, real eta){ //converts from lower pcor and diag sd to chol or cov matrix
int ndim;
matrix[rows(mat),cols(mat)] mscale;
matrix[rows(mat),cols(mat)] mcholcor;
matrix[rows(mat),cols(mat)] out;
real tempsum;
real betad;
ndim = cols(mat);
mcholcor=diag_matrix(rep_vector(0,ndim));
mscale = diag_matrix(diagonal(mat));
betad = eta + (ndim-1)/2;
if(ndim > 1){
for(coli in 1:(ndim-1)){
betad = betad - 1/2;
for(rowi in coli:ndim){
if(rowi > coli){
mat[rowi,coli]/2+.5 ~ beta(betad,betad);
mcholcor[rowi,coli] = mat[rowi,coli];
mcholcor[coli,rowi] = 0;
}
if(coli > 1){
mcholcor[rowi,coli] = mcholcor[rowi,coli] * (rowi==coli ? 0 : mat[rowi,coli-1]) * sqrt(1-square(mcholcor[rowi,coli-1]));
}
}
}
}
//out = mscale * mcholcor; //cholesky
//if(chol == 0) out=multiply_lower_tri_self_transpose(out); //cov
return mcholcor;
} //end sdpcorr2cov
}
data{
int ndim;
real eta;
}
parameters{
vector<lower=-1,upper=1>[(ndim*ndim-ndim)/2] pcorrvec;
real dummy;
}
transformed parameters{
vector[ndim] scalevec;
matrix[ndim,ndim] sdpcorrmat;
matrix[ndim,ndim] cholcorrmat;
scalevec=rep_vector(1,ndim);
{
int counter;
counter=0;
sdpcorrmat=diag_matrix(scalevec);
for(rowi in 2:ndim){ // fill lower tri of sdpcorrmat
for(coli in 1:(rowi-1)){
counter=counter+1;
sdpcorrmat[rowi,coli]=pcorrvec[counter];
}
}
}
cholcorrmat = sdpcorr2cov_lp(sdpcorrmat,1,eta); //convert sd and lower tri partial cor matrix to cholesky factor cov
}
model{
dummy~normal(0,1); //just putting something here...
//pcorrvec~normal(0,1);
}
generated quantities{
matrix[ndim,ndim] corrmat_out;
corrmat_out = cholcorrmat * cholcorrmat\'; //the matrix of actual interest
}
'
ndim=6
eta=1
fit=stan(model_code=sm2,data=list(ndim=ndim,eta=eta),warmup=10,iter=150000,chains=3,cores=3)
mcholcor[rowi,coli] * (rowi==coli ? 0 : mat[rowi,coli-1]) * sqrt(1-square(mcholcor[rowi,coli-1]));
I have fixed my logic and script but so far as I understand you like these reported.
require(rstan)
sm2 <- '
functions{
row_vector vecpower(row_vector vec, real power){
row_vector[num_elements(vec)] out;
for(i in 1:num_elements(vec)){
out[i] = vec[i]^power;
}
return out;
}
vector vecsqrt(vector vec){
vector[num_elements(vec)] out;
for(i in 1:num_elements(vec)){
out[i]=sqrt(vec[i]);
}
return out;
}
matrix sdpcorr2cov_lp(matrix mat,int chol, real eta){ //converts from lower pcor and diag sd to chol or cov matrix
int ndim;
matrix[rows(mat),cols(mat)] mscale;
matrix[rows(mat),cols(mat)] mcholcor;
matrix[rows(mat),cols(mat)] out;
real tempsum;
real betad;
ndim = cols(mat);
mcholcor=diag_matrix(rep_vector(0,ndim));
mscale = diag_matrix(diagonal(mat));
betad = eta + (ndim-1)/2;
if(ndim > 1){
for(coli in 1:(ndim-1)){
betad = betad - 1/2;
for(rowi in coli:ndim){
if(rowi > coli){
mat[rowi,coli]/2+.5 ~ beta(betad,betad);
mcholcor[rowi,coli] = mat[rowi,coli];
mcholcor[coli,rowi] = 0;
}
if(coli > 1){
mcholcor[rowi,coli] = mcholcor[rowi,coli] * (rowi==coli ? 0 : mat[rowi,coli-1]) * sqrt(1-square(mcholcor[rowi,coli-1]));
}
}
}
}
//out = mscale * mcholcor; //cholesky
//if(chol == 0) out=multiply_lower_tri_self_transpose(out); //cov
return mcholcor;
} //end sdpcorr2cov
}
data{
int ndim;
real eta;
}
parameters{
vector<lower=-1,upper=1>[(ndim*ndim-ndim)/2] pcorrvec;
real dummy;
}
transformed parameters{
vector[ndim] scalevec;
matrix[ndim,ndim] sdpcorrmat;
matrix[ndim,ndim] cholcorrmat;
scalevec=rep_vector(1,ndim);
{
int counter;
counter=0;
sdpcorrmat=diag_matrix(scalevec);
for(rowi in 2:ndim){ // fill lower tri of sdpcorrmat
for(coli in 1:(rowi-1)){
counter=counter+1;
sdpcorrmat[rowi,coli]=pcorrvec[counter];
}
}
}
cholcorrmat = sdpcorr2cov_lp(sdpcorrmat,1,eta); //convert sd and lower tri partial cor matrix to cholesky factor cov
}
model{
dummy~normal(0,1); //just putting something here...
//pcorrvec~normal(0,1);
}
generated quantities{
matrix[ndim,ndim] corrmat_out;
corrmat_out = cholcorrmat * cholcorrmat\'; //the matrix of actual interest
}
'
ndim=6
eta=1
fit=stan(model_code=sm2,data=list(ndim=ndim,eta=eta),warmup=10,iter=150000,chains=3,cores=3)
Daniel Lee
Nov 1, 2016, 10:22:32 AM11/1/16
to stan-...@googlegroups.com
Can you include the error? I can't tell if the program is not compiling or if the generated code is not compiling.
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Charles Driver
Nov 1, 2016, 11:38:55 AM11/1/16
to Stan users mailing list
DIAGNOSTIC(S) FROM PARSER: Warning: integer division implicitly rounds to integer. Found int division: (ndim - 1) / 2 Positive values rounded down, negative values rounded up or down in platform-dependent way. Warning: integer division implicitly rounds to integer. Found int division: 1 / 2 Positive values rounded down, negative values rounded up or down in platform-dependent way. Warning: integer division implicitly rounds to integer. Found int division: ((ndim * ndim) - ndim) / 2 Positive values rounded down, negative values rounded up or down in platform-dependent way. In file included from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/mat/fun/promote_scalar.hpp:4:0, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/mat.hpp:133, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/rev/mat.hpp:9, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math.hpp:4, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/src/stan/model/model_header.hpp:4, from file21e0675830d8.cpp:8: C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/scal/fun/promote_scalar.hpp: In instantiation of 'static T stan::math::promote_scalar_struct<T, S>::apply(S) [with T = double; S = stan::math::var]': C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/scal/fun/promote_scalar.hpp:68:50: required from 'typename stan::math::promote_scalar_type<T, S>::type stan::math::promote_scalar(const S&) [with T = double; S = stan::math::var; typename stan::math::promote_scalar_type<T, S>::type = double]' file21e0675830d8.cpp:165:281: required from 'Eigen::Matrix<typename boost::math::tools::promote_args<RT1, RT2, A>::type, -1, -1> model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::sdpcorr2cov_lp(const Eigen::Matrix<LhsScalar, -1, -1, 0>&, const int&, const T2__&, T_lp__&, T_lp_accum__&, std::ostream*) [with T0__ = stan::math::var; T2__ = double; T_lp__ = stan::math::var; T_lp_accum__ = stan::math::accumulator<stan::math::var>; typename boost::math::tools::promote_args<RT1, RT2, A>::type = stan::math::var; std::ostream = std::basic_ostream<char>]' file21e0675830d8.cpp:380:105: required from 'T__ model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343::log_prob(std::vector<T2>&, std::vector<int>&, std::ostream*) const [with bool propto__ = true; bool jacobian__ = true; T__ = stan::math::var; std::ostream = std::basic_ostream<char>]' C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/src/stan/model/util.hpp:93:39: required from 'double stan::model::log_prob_grad(const M&, std::vector<double>&, std::vector<int>&, std::vector<double>&, std::ostream*) [with bool propto = true; bool jacobian_adjust_transform = true; M = model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343; std::ostream = std::basic_ostream<char>]' C:/Users/driver/Documents/R/win-library/3.3/rstan/include/rstan/stan_fit.hpp:1464:101: required from 'SEXPREC* rstan::stan_fit<Model, RNG_t>::grad_log_prob(SEXP, SEXP) [with Model = model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343; RNG_t = boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014u, 0u, 2147483563u>, boost::random::linear_congruential_engine<unsigned int, 40692u, 0u, 2147483399u> >; SEXP = SEXPREC*]' file21e0675830d8.cpp:736:172: required from here C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/scal/fun/promote_scalar.hpp:32:19: error: invalid cast from type 'stan::math::var' to type 'double' return T(x); ^ C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/scal/fun/promote_scalar.hpp: In static member function 'static T stan::math::promote_scalar_struct<T, S>::apply(S) [with T = double; S = stan::math::var]': C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/scal/fun/promote_scalar.hpp:33:7: warning: control reaches end of non-void function [-Wreturn-type] } ^ make: *** [file21e0675830d8.o] Error 1 Warning message: running command 'make -f "C:/PROGRA~1/R/R-33~1.0/etc/x64/Makeconf" -f "C:/PROGRA~1/R/R-33~1.0/share/make/winshlib.mk" -f "C:/Users/driver/Documents/.R/Makevars" SHLIB_LDFLAGS='$(SHLIB_CXXLDFLAGS)' SHLIB_LD='$(SHLIB_CXXLD)' SHLIB="file21e0675830d8.dll" WIN=64 TCLBIN=64 OBJECTS="file21e0675830d8.o"' had status 2 ERROR(s) during compilation: source code errors or compiler configuration errors! Program source: 1: 2: // includes from the plugin 3: 4: 5: // user includes 6: #define STAN__SERVICES__COMMAND_HPP// Code generated by Stan version 2.12 7: 8: #include <stan/model/model_header.hpp> 9: 10: namespace model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace { 11: 12: using std::istream; 13: using std::string; 14: using std::stringstream; 15: using std::vector; 16: using stan::io::dump; 17: using stan::math::lgamma; 18: using stan::model::prob_grad; 19: using namespace stan::math; 20: 21: typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d; 22: typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d; 23: typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d; 24: 25: static int current_statement_begin__; 26: 27: template <typename T0__, typename T1__> 28: inline 29: Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, 1,Eigen::Dynamic> 30: vecpower(const Eigen::Matrix<T0__, 1,Eigen::Dynamic>& vec, 31: const T1__& power, std::ostream* pstream__) { 32: typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__; 33: typedef fun_scalar_t__ fun_return_scalar_t__; 34: const static bool propto__ = true; 35: (void) propto__; 36: int current_statement_begin__ = -1; 37: try { 38: { 39: Eigen::Matrix<fun_scalar_t__,1,Eigen::Dynamic> out(static_cast<Eigen::VectorXd::Index>(num_elements(vec))); 40: (void) out; // dummy to suppress unused var warning 41: stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN()); 42: current_statement_begin__ = 6; 43: for (int i = 1; i <= num_elements(vec); ++i) { 44: current_statement_begin__ = 7; 45: stan::math::assign(get_base1_lhs(out,i,"out",1), pow(get_base1(vec,i,"vec",1),power)); 46: } 47: current_statement_begin__ = 9; 48: return stan::math::promote_scalar<fun_return_scalar_t__>(out); 49: } 50: } catch (const std::exception& e) { 51: stan::lang::rethrow_located(e,current_statement_begin__); 52: // Next line prevents compiler griping about no return 53: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 54: } 55: } 56: 57: 58: struct vecpower_functor__ { 59: template <typename T0__, typename T1__> 60: inline 61: Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, 1,Eigen::Dynamic> 62: operator()(const Eigen::Matrix<T0__, 1,Eigen::Dynamic>& vec, 63: const T1__& power, std::ostream* pstream__) const { 64: return vecpower(vec, power, pstream__); 65: } 66: }; 67: 68: template <typename T0__> 69: inline 70: Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1> 71: vecsqrt(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& vec, std::ostream* pstream__) { 72: typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__; 73: typedef fun_scalar_t__ fun_return_scalar_t__; 74: const static bool propto__ = true; 75: (void) propto__; 76: int current_statement_begin__ = -1; 77: try { 78: { 79: Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1> out(static_cast<Eigen::VectorXd::Index>(num_elements(vec))); 80: (void) out; // dummy to suppress unused var warning 81: stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN()); 82: current_statement_begin__ = 14; 83: for (int i = 1; i <= num_elements(vec); ++i) { 84: current_statement_begin__ = 15; 85: stan::math::assign(get_base1_lhs(out,i,"out",1), sqrt(get_base1(vec,i,"vec",1))); 86: } 87: current_statement_begin__ = 17; 88: return stan::math::promote_scalar<fun_return_scalar_t__>(out); 89: } 90: } catch (const std::exception& e) { 91: stan::lang::rethrow_located(e,current_statement_begin__); 92: // Next line prevents compiler griping about no return 93: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 94: } 95: } 96: 97: 98: struct vecsqrt_functor__ { 99: template <typename T0__> 100: inline 101: Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1> 102: operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& vec, std::ostream* pstream__) const { 103: return vecsqrt(vec, pstream__); 104: } 105: }; 106: 107: template <typename T0__, typename T2__, typename T_lp__, typename T_lp_accum__> 108: inline 109: Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T2__, T_lp__>::type, Eigen::Dynamic,Eigen::Dynamic> 110: sdpcorr2cov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,Eigen::Dynamic>& mat, 111: const int& chol, 112: const T2__& eta, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) { 113: typedef typename boost::math::tools::promote_args<T0__, T2__, T_lp__>::type fun_scalar_t__; 114: typedef fun_scalar_t__ fun_return_scalar_t__; 115: const static bool propto__ = true; 116: (void) propto__; 117: int current_statement_begin__ = -1; 118: try { 119: { 120: int ndim(0); 121: (void) ndim; // dummy to suppress unused var warning 122: Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic> mscale(static_cast<Eigen::VectorXd::Index>(rows(mat)),static_cast<Eigen::VectorXd::Index>(cols(mat))); 123: (void) mscale; // dummy to suppress unused var warning 124: Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic> mcholcor(static_cast<Eigen::VectorXd::Index>(rows(mat)),static_cast<Eigen::VectorXd::Index>(cols(mat))); 125: (void) mcholcor; // dummy to suppress unused var warning 126: Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic> out(static_cast<Eigen::VectorXd::Index>(rows(mat)),static_cast<Eigen::VectorXd::Index>(cols(mat))); 127: (void) out; // dummy to suppress unused var warning 128: fun_scalar_t__ tempsum; 129: (void) tempsum; // dummy to suppress unused var warning 130: fun_scalar_t__ betad; 131: (void) betad; // dummy to suppress unused var warning 132: stan::math::initialize(mscale, std::numeric_limits<double>::quiet_NaN()); 133: stan::math::initialize(mcholcor, std::numeric_limits<double>::quiet_NaN()); 134: stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN()); 135: stan::math::initialize(tempsum, std::numeric_limits<double>::quiet_NaN()); 136: stan::math::initialize(betad, std::numeric_limits<double>::quiet_NaN()); 137: current_statement_begin__ = 28; 138: stan::math::assign(ndim, cols(mat)); 139: current_statement_begin__ = 29; 140: stan::math::assign(mcholcor, diag_matrix(rep_vector(0,ndim))); 141: current_statement_begin__ = 30; 142: stan::math::assign(mscale, diag_matrix(diagonal(mat))); 143: current_statement_begin__ = 31; 144: stan::math::assign(betad, (eta + divide((ndim - 1),2))); 145: current_statement_begin__ = 33; 146: if (as_bool(logical_gt(ndim,1))) { 147: current_statement_begin__ = 34; 148: for (int coli = 1; coli <= (ndim - 1); ++coli) { 149: current_statement_begin__ = 35; 150: stan::math::assign(betad, (betad - divide(1,2))); 151: current_statement_begin__ = 36; 152: for (int rowi = coli; rowi <= ndim; ++rowi) { 153: current_statement_begin__ = 38; 154: if (as_bool(logical_gt(rowi,coli))) { 155: current_statement_begin__ = 39; 156: lp_accum__.add(beta_log<propto__>(((get_base1(mat,rowi,coli,"mat",1) / 2) + 0.5), betad, betad)); 157: current_statement_begin__ = 40; 158: stan::math::assign(get_base1_lhs(mcholcor,rowi,coli,"mcholcor",1), get_base1(mat,rowi,coli,"mat",1)); 159: current_statement_begin__ = 41; 160: stan::math::assign(get_base1_lhs(mcholcor,coli,rowi,"mcholcor",1), 0); 161: } 162: current_statement_begin__ = 43; 163: if (as_bool(logical_gt(coli,1))) { 164: current_statement_begin__ = 44; 165: stan::math::assign(get_base1_lhs(mcholcor,rowi,coli,"mcholcor",1), ((get_base1(mcholcor,rowi,coli,"mcholcor",1) * (logical_eq(rowi,coli) ? stan::math::promote_scalar<double>(0) : stan::math::promote_scalar<double>(get_base1(mat,rowi,(coli - 1),"mat",1)) )) * sqrt((1 - square(get_base1(mcholcor,rowi,(coli - 1),"mcholcor",1)))))); 166: } 167: } 168: } 169: } 170: current_statement_begin__ = 56; 171: return stan::math::promote_scalar<fun_return_scalar_t__>(mcholcor); 172: } 173: } catch (const std::exception& e) { 174: stan::lang::rethrow_located(e,current_statement_begin__); 175: // Next line prevents compiler griping about no return 176: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 177: } 178: } 179: 180: 181: struct sdpcorr2cov_lp_functor__ { 182: template <typename T0__, typename T2__, typename T_lp__, typename T_lp_accum__> 183: inline 184: Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T2__, T_lp__>::type, Eigen::Dynamic,Eigen::Dynamic> 185: operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,Eigen::Dynamic>& mat, 186: const int& chol, 187: const T2__& eta, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const { 188: return sdpcorr2cov_lp(mat, chol, eta, lp__, lp_accum__, pstream__); 189: } 190: }; 191: 192: class model21e05f842a3d_668201f656fa56a40310f15ffd0f7343 : public prob_grad { 193: private: 194: int ndim; 195: double eta; 196: public: 197: model21e05f842a3d_668201f656fa56a40310f15ffd0f7343(stan::io::var_context& context__, 198: std::ostream* pstream__ = 0) 199: : prob_grad(0) { 200: typedef boost::ecuyer1988 rng_t; 201: rng_t base_rng(0); // 0 seed default 202: ctor_body(context__, base_rng, pstream__); 203: } 204: 205: template <class RNG> 206: model21e05f842a3d_668201f656fa56a40310f15ffd0f7343(stan::io::var_context& context__, 207: RNG& base_rng__, 208: std::ostream* pstream__ = 0) 209: : prob_grad(0) { 210: ctor_body(context__, base_rng__, pstream__); 211: } 212: 213: template <class RNG> 214: void ctor_body(stan::io::var_context& context__, 215: RNG& base_rng__, 216: std::ostream* pstream__) { 217: current_statement_begin__ = -1; 218: 219: static const char* function__ = "model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343"; 220: (void) function__; // dummy call to supress warning 221: size_t pos__; 222: (void) pos__; // dummy call to supress warning 223: std::vector<int> vals_i__; 224: std::vector<double> vals_r__; 225: context__.validate_dims("data initialization", "ndim", "int", context__.to_vec()); 226: ndim = int(0); 227: vals_i__ = context__.vals_i("ndim"); 228: pos__ = 0; 229: ndim = vals_i__[pos__++]; 230: context__.validate_dims("data initialization", "eta", "double", context__.to_vec()); 231: eta = double(0); 232: vals_r__ = context__.vals_r("eta"); 233: pos__ = 0; 234: eta = vals_r__[pos__++]; 235: 236: // validate data 237: 238: double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN()); 239: (void) DUMMY_VAR__; // suppress unused var warning 240: 241: 242: // initialize transformed variables to avoid seg fault on val access 243: 244: try { 245: } catch (const std::exception& e) { 246: stan::lang::rethrow_located(e,current_statement_begin__); 247: // Next line prevents compiler griping about no return 248: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 249: } 250: 251: // validate transformed data 252: 253: // set parameter ranges 254: num_params_r__ = 0U; 255: param_ranges_i__.clear(); 256: num_params_r__ += divide(((ndim * ndim) - ndim),2); 257: ++num_params_r__; 258: } 259: 260: ~model21e05f842a3d_668201f656fa56a40310f15ffd0f7343() { } 261: 262: 263: void transform_inits(const stan::io::var_context& context__, 264: std::vector<int>& params_i__, 265: std::vector<double>& params_r__, 266: std::ostream* pstream__) const { 267: stan::io::writer<double> writer__(params_r__,params_i__); 268: size_t pos__; 269: (void) pos__; // dummy call to supress warning 270: std::vector<double> vals_r__; 271: std::vector<int> vals_i__; 272: 273: if (!(context__.contains_r("pcorrvec"))) 274: throw std::runtime_error("variable pcorrvec missing"); 275: vals_r__ = context__.vals_r("pcorrvec"); 276: pos__ = 0U; 277: context__.validate_dims("initialization", "pcorrvec", "vector_d", context__.to_vec(divide(((ndim * ndim) - ndim),2))); 278: vector_d pcorrvec(static_cast<Eigen::VectorXd::Index>(divide(((ndim * ndim) - ndim),2))); 279: for (int j1__ = 0U; j1__ < divide(((ndim * ndim) - ndim),2); ++j1__) 280: pcorrvec(j1__) = vals_r__[pos__++]; 281: try { 282: writer__.vector_lub_unconstrain(-(1),1,pcorrvec); 283: } catch (const std::exception& e) { 284: throw std::runtime_error(std::string("Error transforming variable pcorrvec: ") + e.what()); 285: } 286: 287: if (!(context__.contains_r("dummy"))) 288: throw std::runtime_error("variable dummy missing"); 289: vals_r__ = context__.vals_r("dummy"); 290: pos__ = 0U; 291: context__.validate_dims("initialization", "dummy", "double", context__.to_vec()); 292: double dummy(0); 293: dummy = vals_r__[pos__++]; 294: try { 295: writer__.scalar_unconstrain(dummy); 296: } catch (const std::exception& e) { 297: throw std::runtime_error(std::string("Error transforming variable dummy: ") + e.what()); 298: } 299: 300: params_r__ = writer__.data_r(); 301: params_i__ = writer__.data_i(); 302: } 303: 304: void transform_inits(const stan::io::var_context& context, 305: Eigen::Matrix<double,Eigen::Dynamic,1>& params_r, 306: std::ostream* pstream__) const { 307: std::vector<double> params_r_vec; 308: std::vector<int> params_i_vec; 309: transform_inits(context, params_i_vec, params_r_vec, pstream__); 310: params_r.resize(params_r_vec.size()); 311: for (int i = 0; i < params_r.size(); ++i) 312: params_r(i) = params_r_vec[i]; 313: } 314: 315: 316: template <bool propto__, bool jacobian__, typename T__> 317: T__ log_prob(vector<T__>& params_r__, 318: vector<int>& params_i__, 319: std::ostream* pstream__ = 0) const { 320: 321: T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN()); 322: (void) DUMMY_VAR__; // suppress unused var warning 323: 324: T__ lp__(0.0); 325: stan::math::accumulator<T__> lp_accum__; 326: 327: // model parameters 328: stan::io::reader<T__> in__(params_r__,params_i__); 329: 330: Eigen::Matrix<T__,Eigen::Dynamic,1> pcorrvec; 331: (void) pcorrvec; // dummy to suppress unused var warning 332: if (jacobian__) 333: pcorrvec = in__.vector_lub_constrain(-(1),1,divide(((ndim * ndim) - ndim),2),lp__); 334: else 335: pcorrvec = in__.vector_lub_constrain(-(1),1,divide(((ndim * ndim) - ndim),2)); 336: 337: T__ dummy; 338: (void) dummy; // dummy to suppress unused var warning 339: if (jacobian__) 340: dummy = in__.scalar_constrain(lp__); 341: else 342: dummy = in__.scalar_constrain(); 343: 344: 345: // transformed parameters 346: Eigen::Matrix<T__,Eigen::Dynamic,1> scalevec(static_cast<Eigen::VectorXd::Index>(ndim)); 347: (void) scalevec; // dummy to suppress unused var warning 348: Eigen::Matrix<T__,Eigen::Dynamic,Eigen::Dynamic> sdpcorrmat(static_cast<Eigen::VectorXd::Index>(ndim),static_cast<Eigen::VectorXd::Index>(ndim)); 349: (void) sdpcorrmat; // dummy to suppress unused var warning 350: Eigen::Matrix<T__,Eigen::Dynamic,Eigen::Dynamic> cholcorrmat(static_cast<Eigen::VectorXd::Index>(ndim),static_cast<Eigen::VectorXd::Index>(ndim)); 351: (void) cholcorrmat; // dummy to suppress unused var warning 352: 353: // initialize transformed variables to avoid seg fault on val access 354: stan::math::fill(scalevec,DUMMY_VAR__); 355: stan::math::fill(sdpcorrmat,DUMMY_VAR__); 356: stan::math::fill(cholcorrmat,DUMMY_VAR__); 357: 358: try { 359: current_statement_begin__ = 75; 360: stan::math::assign(scalevec, rep_vector(1,ndim)); 361: { 362: int counter(0); 363: (void) counter; // dummy to suppress unused var warning 364: current_statement_begin__ = 79; 365: stan::math::assign(counter, 0); 366: current_statement_begin__ = 80; 367: stan::math::assign(sdpcorrmat, diag_matrix(scalevec)); 368: current_statement_begin__ = 82; 369: for (int rowi = 2; rowi <= ndim; ++rowi) { 370: current_statement_begin__ = 83; 371: for (int coli = 1; coli <= (rowi - 1); ++coli) { 372: current_statement_begin__ = 84; 373: stan::math::assign(counter, (counter + 1)); 374: current_statement_begin__ = 85; 375: stan::math::assign(get_base1_lhs(sdpcorrmat,rowi,coli,"sdpcorrmat",1), get_base1(pcorrvec,counter,"pcorrvec",1)); 376: } 377: } 378: } 379: current_statement_begin__ = 90; 380: stan::math::assign(cholcorrmat, sdpcorr2cov_lp(sdpcorrmat,1,eta, lp__, lp_accum__, pstream__)); 381: } catch (const std::exception& e) { 382: stan::lang::rethrow_located(e,current_statement_begin__); 383: // Next line prevents compiler griping about no return 384: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 385: } 386: 387: // validate transformed parameters 388: for (int i0__ = 0; i0__ < ndim; ++i0__) { 389: if (stan::math::is_uninitialized(scalevec(i0__))) { 390: std::stringstream msg__; 391: msg__ << "Undefined transformed parameter: scalevec" << '[' << i0__ << ']'; 392: throw std::runtime_error(msg__.str()); 393: } 394: } 395: for (int i0__ = 0; i0__ < ndim; ++i0__) { 396: for (int i1__ = 0; i1__ < ndim; ++i1__) { 397: if (stan::math::is_uninitialized(sdpcorrmat(i0__,i1__))) { 398: std::stringstream msg__; 399: msg__ << "Undefined transformed parameter: sdpcorrmat" << '[' << i0__ << ']' << '[' << i1__ << ']'; 400: throw std::runtime_error(msg__.str()); 401: } 402: } 403: } 404: for (int i0__ = 0; i0__ < ndim; ++i0__) { 405: for (int i1__ = 0; i1__ < ndim; ++i1__) { 406: if (stan::math::is_uninitialized(cholcorrmat(i0__,i1__))) { 407: std::stringstream msg__; 408: msg__ << "Undefined transformed parameter: cholcorrmat" << '[' << i0__ << ']' << '[' << i1__ << ']'; 409: throw std::runtime_error(msg__.str()); 410: } 411: } 412: } 413: 414: const char* function__ = "validate transformed params"; 415: (void) function__; // dummy to suppress unused var warning 416: 417: // model body 418: try { 419: current_statement_begin__ = 95; 420: lp_accum__.add(normal_log<propto__>(dummy, 0, 1)); 421: } catch (const std::exception& e) { 422: stan::lang::rethrow_located(e,current_statement_begin__); 423: // Next line prevents compiler griping about no return 424: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 425: } 426: 427: lp_accum__.add(lp__); 428: return lp_accum__.sum(); 429: 430: } // log_prob() 431: 432: template <bool propto, bool jacobian, typename T_> 433: T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r, 434: std::ostream* pstream = 0) const { 435: std::vector<T_> vec_params_r; 436: vec_params_r.reserve(params_r.size()); 437: for (int i = 0; i < params_r.size(); ++i) 438: vec_params_r.push_back(params_r(i)); 439: std::vector<int> vec_params_i; 440: return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream); 441: } 442: 443: 444: void get_param_names(std::vector<std::string>& names__) const { 445: names__.resize(0); 446: names__.push_back("pcorrvec"); 447: names__.push_back("dummy"); 448: names__.push_back("scalevec"); 449: names__.push_back("sdpcorrmat"); 450: names__.push_back("cholcorrmat"); 451: names__.push_back("corrmat_out"); 452: } 453: 454: 455: void get_dims(std::vector<std::vector<size_t> >& dimss__) const { 456: dimss__.resize(0); 457: std::vector<size_t> dims__; 458: dims__.resize(0); 459: dims__.push_back(divide(((ndim * ndim) - ndim),2)); 460: dimss__.push_back(dims__); 461: dims__.resize(0); 462: dimss__.push_back(dims__); 463: dims__.resize(0); 464: dims__.push_back(ndim); 465: dimss__.push_back(dims__); 466: dims__.resize(0); 467: dims__.push_back(ndim); 468: dims__.push_back(ndim); 469: dimss__.push_back(dims__); 470: dims__.resize(0); 471: dims__.push_back(ndim); 472: dims__.push_back(ndim); 473: dimss__.push_back(dims__); 474: dims__.resize(0); 475: dims__.push_back(ndim); 476: dims__.push_back(ndim); 477: dimss__.push_back(dims__); 478: } 479: 480: template <typename RNG> 481: void write_array(RNG& base_rng__, 482: std::vector<double>& params_r__, 483: std::vector<int>& params_i__, 484: std::vector<double>& vars__, 485: bool include_tparams__ = true, 486: bool include_gqs__ = true, 487: std::ostream* pstream__ = 0) const { 488: vars__.resize(0); 489: stan::io::reader<double> in__(params_r__,params_i__); 490: static const char* function__ = "model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::write_array"; 491: (void) function__; // dummy call to supress warning 492: // read-transform, write parameters 493: vector_d pcorrvec = in__.vector_lub_constrain(-(1),1,divide(((ndim * ndim) - ndim),2)); 494: double dummy = in__.scalar_constrain(); 495: for (int k_0__ = 0; k_0__ < divide(((ndim * ndim) - ndim),2); ++k_0__) { 496: vars__.push_back(pcorrvec[k_0__]); 497: } 498: vars__.push_back(dummy); 499: 500: if (!include_tparams__) return; 501: // declare and define transformed parameters 502: double lp__ = 0.0; 503: (void) lp__; // dummy call to supress warning 504: stan::math::accumulator<double> lp_accum__; 505: 506: vector_d scalevec(static_cast<Eigen::VectorXd::Index>(ndim)); 507: (void) scalevec; // dummy to suppress unused var warning 508: matrix_d sdpcorrmat(static_cast<Eigen::VectorXd::Index>(ndim),static_cast<Eigen::VectorXd::Index>(ndim)); 509: (void) sdpcorrmat; // dummy to suppress unused var warning 510: matrix_d cholcorrmat(static_cast<Eigen::VectorXd::Index>(ndim),static_cast<Eigen::VectorXd::Index>(ndim)); 511: (void) cholcorrmat; // dummy to suppress unused var warning 512: 513: try { 514: current_statement_begin__ = 75; 515: stan::math::assign(scalevec, rep_vector(1,ndim)); 516: { 517: int counter(0); 518: (void) counter; // dummy to suppress unused var warning 519: current_statement_begin__ = 79; 520: stan::math::assign(counter, 0); 521: current_statement_begin__ = 80; 522: stan::math::assign(sdpcorrmat, diag_matrix(scalevec)); 523: current_statement_begin__ = 82; 524: for (int rowi = 2; rowi <= ndim; ++rowi) { 525: current_statement_begin__ = 83; 526: for (int coli = 1; coli <= (rowi - 1); ++coli) { 527: current_statement_begin__ = 84; 528: stan::math::assign(counter, (counter + 1)); 529: current_statement_begin__ = 85; 530: stan::math::assign(get_base1_lhs(sdpcorrmat,rowi,coli,"sdpcorrmat",1), get_base1(pcorrvec,counter,"pcorrvec",1)); 531: } 532: } 533: } 534: current_statement_begin__ = 90; 535: stan::math::assign(cholcorrmat, sdpcorr2cov_lp(sdpcorrmat,1,eta, lp__, lp_accum__, pstream__)); 536: } catch (const std::exception& e) { 537: stan::lang::rethrow_located(e,current_statement_begin__); 538: // Next line prevents compiler griping about no return 539: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 540: } 541: 542: // validate transformed parameters 543: 544: // write transformed parameters 545: for (int k_0__ = 0; k_0__ < ndim; ++k_0__) { 546: vars__.push_back(scalevec[k_0__]); 547: } 548: for (int k_1__ = 0; k_1__ < ndim; ++k_1__) { 549: for (int k_0__ = 0; k_0__ < ndim; ++k_0__) { 550: vars__.push_back(sdpcorrmat(k_0__, k_1__)); 551: } 552: } 553: for (int k_1__ = 0; k_1__ < ndim; ++k_1__) { 554: for (int k_0__ = 0; k_0__ < ndim; ++k_0__) { 555: vars__.push_back(cholcorrmat(k_0__, k_1__)); 556: } 557: } 558: 559: if (!include_gqs__) return; 560: // declare and define generated quantities 561: matrix_d corrmat_out(static_cast<Eigen::VectorXd::Index>(ndim),static_cast<Eigen::VectorXd::Index>(ndim)); 562: (void) corrmat_out; // dummy to suppress unused var warning 563: 564: double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN()); 565: (void) DUMMY_VAR__; // suppress unused var warning 566: 567: 568: // initialize transformed variables to avoid seg fault on val access 569: stan::math::fill(corrmat_out,DUMMY_VAR__); 570: 571: try { 572: current_statement_begin__ = 100; 573: stan::math::assign(corrmat_out, multiply(cholcorrmat,transpose(cholcorrmat))); 574: } catch (const std::exception& e) { 575: stan::lang::rethrow_located(e,current_statement_begin__); 576: // Next line prevents compiler griping about no return 577: throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***"); 578: } 579: 580: // validate generated quantities 581: 582: // write generated quantities 583: for (int k_1__ = 0; k_1__ < ndim; ++k_1__) { 584: for (int k_0__ = 0; k_0__ < ndim; ++k_0__) { 585: vars__.push_back(corrmat_out(k_0__, k_1__)); 586: } 587: } 588: 589: } 590: 591: template <typename RNG> 592: void write_array(RNG& base_rng, 593: Eigen::Matrix<double,Eigen::Dynamic,1>& params_r, 594: Eigen::Matrix<double,Eigen::Dynamic,1>& vars, 595: bool include_tparams = true, 596: bool include_gqs = true, 597: std::ostream* pstream = 0) const { 598: std::vector<double> params_r_vec(params_r.size()); 599: for (int i = 0; i < params_r.size(); ++i) 600: params_r_vec[i] = params_r(i); 601: std::vector<double> vars_vec; 602: std::vector<int> params_i_vec; 603: write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream); 604: vars.resize(vars_vec.size()); 605: for (int i = 0; i < vars.size(); ++i) 606: vars(i) = vars_vec[i]; 607: } 608: 609: static std::string model_name() { 610: return "model21e05f842a3d_668201f656fa56a40310f15ffd0f7343"; 611: } 612: 613: 614: void constrained_param_names(std::vector<std::string>& param_names__, 615: bool include_tparams__ = true, 616: bool include_gqs__ = true) const { 617: std::stringstream param_name_stream__; 618: for (int k_0__ = 1; k_0__ <= divide(((ndim * ndim) - ndim),2); ++k_0__) { 619: param_name_stream__.str(std::string()); 620: param_name_stream__ << "pcorrvec" << '.' << k_0__; 621: param_names__.push_back(param_name_stream__.str()); 622: } 623: param_name_stream__.str(std::string()); 624: param_name_stream__ << "dummy"; 625: param_names__.push_back(param_name_stream__.str()); 626: 627: if (!include_gqs__ && !include_tparams__) return; 628: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 629: param_name_stream__.str(std::string()); 630: param_name_stream__ << "scalevec" << '.' << k_0__; 631: param_names__.push_back(param_name_stream__.str()); 632: } 633: for (int k_1__ = 1; k_1__ <= ndim; ++k_1__) { 634: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 635: param_name_stream__.str(std::string()); 636: param_name_stream__ << "sdpcorrmat" << '.' << k_0__ << '.' << k_1__; 637: param_names__.push_back(param_name_stream__.str()); 638: } 639: } 640: for (int k_1__ = 1; k_1__ <= ndim; ++k_1__) { 641: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 642: param_name_stream__.str(std::string()); 643: param_name_stream__ << "cholcorrmat" << '.' << k_0__ << '.' << k_1__; 644: param_names__.push_back(param_name_stream__.str()); 645: } 646: } 647: 648: if (!include_gqs__) return; 649: for (int k_1__ = 1; k_1__ <= ndim; ++k_1__) { 650: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 651: param_name_stream__.str(std::string()); 652: param_name_stream__ << "corrmat_out" << '.' << k_0__ << '.' << k_1__; 653: param_names__.push_back(param_name_stream__.str()); 654: } 655: } 656: } 657: 658: 659: void unconstrained_param_names(std::vector<std::string>& param_names__, 660: bool include_tparams__ = true, 661: bool include_gqs__ = true) const { 662: std::stringstream param_name_stream__; 663: for (int k_0__ = 1; k_0__ <= divide(((ndim * ndim) - ndim),2); ++k_0__) { 664: param_name_stream__.str(std::string()); 665: param_name_stream__ << "pcorrvec" << '.' << k_0__; 666: param_names__.push_back(param_name_stream__.str()); 667: } 668: param_name_stream__.str(std::string()); 669: param_name_stream__ << "dummy"; 670: param_names__.push_back(param_name_stream__.str()); 671: 672: if (!include_gqs__ && !include_tparams__) return; 673: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 674: param_name_stream__.str(std::string()); 675: param_name_stream__ << "scalevec" << '.' << k_0__; 676: param_names__.push_back(param_name_stream__.str()); 677: } 678: for (int k_1__ = 1; k_1__ <= ndim; ++k_1__) { 679: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 680: param_name_stream__.str(std::string()); 681: param_name_stream__ << "sdpcorrmat" << '.' << k_0__ << '.' << k_1__; 682: param_names__.push_back(param_name_stream__.str()); 683: } 684: } 685: for (int k_1__ = 1; k_1__ <= ndim; ++k_1__) { 686: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 687: param_name_stream__.str(std::string()); 688: param_name_stream__ << "cholcorrmat" << '.' << k_0__ << '.' << k_1__; 689: param_names__.push_back(param_name_stream__.str()); 690: } 691: } 692: 693: if (!include_gqs__) return; 694: for (int k_1__ = 1; k_1__ <= ndim; ++k_1__) { 695: for (int k_0__ = 1; k_0__ <= ndim; ++k_0__) { 696: param_name_stream__.str(std::string()); 697: param_name_stream__ << "corrmat_out" << '.' << k_0__ << '.' << k_1__; 698: param_names__.push_back(param_name_stream__.str()); 699: } 700: } 701: } 702: 703: }; // model 704: 705: } // namespace 706: 707: typedef model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343 stan_model; 708: 709: #include <rstan/rstaninc.hpp> 710: /** 711: * Define Rcpp Module to expose stan_fit's functions to R. 712: */ 713: RCPP_MODULE(stan_fit4model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_mod){ 714: Rcpp::class_<rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, 715: boost::random::ecuyer1988> >("stan_fit4model21e05f842a3d_668201f656fa56a40310f15ffd0f7343") 716: // .constructor<Rcpp::List>() 717: .constructor<SEXP, SEXP>() 718: // .constructor<SEXP, SEXP>() 719: .method("call_sampler", 720: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::call_sampler) 721: .method("param_names", 722: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::param_names) 723: .method("param_names_oi", 724: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::param_names_oi) 725: .method("param_fnames_oi", 726: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::param_fnames_oi) 727: .method("param_dims", 728: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::param_dims) 729: .method("param_dims_oi", 730: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::param_dims_oi) 731: .method("update_param_oi", 732: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::update_param_oi) 733: .method("param_oi_tidx", 734: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::param_oi_tidx) 735: .method("grad_log_prob", 736: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::grad_log_prob) 737: .method("log_prob", 738: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::log_prob) 739: .method("unconstrain_pars", 740: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::unconstrain_pars) 741: .method("constrain_pars", 742: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::constrain_pars) 743: .method("num_pars_unconstrained", 744: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::num_pars_unconstrained) 745: .method("unconstrained_param_names", 746: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::unconstrained_param_names) 747: .method("constrained_param_names", 748: &rstan::stan_fit<model21e05f842a3d_668201f656fa56a40310f15ffd0f7343_namespace::model21e05f842a3d_668201f656fa56a40310f15ffd0f7343, boost::random::ecuyer1988>::constrained_param_names) 749: ; 750: } 751: 752: // declarations 753: extern "C" { 754: SEXP file21e0675830d8( ) ; 755: } 756: 757: // definition 758: 759: SEXP file21e0675830d8( ){ 760: return Rcpp::wrap("668201f656fa56a40310f15ffd0f7343"); 761: } 762: 763: Error in compileCode(f, code, language = language, verbose = verbose) : Compilation ERROR, function(s)/method(s) not created! In file included from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/mat/fun/promote_scalar.hpp:4:0, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/mat.hpp:133, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/rev/mat.hpp:9, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math.hpp:4, from C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/src/stan/model/model_header.hpp:4, from file21e0675830d8.cpp:8: C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/scal/fun/promote_scalar.hpp: In instantiation of 'static T stan::math::promote_scalar_struct<T, S>::apply(S) [with T = double; S = stan::math::var]': C:/Users/driver/Documents/R/win-library/3.3/StanHeaders/include/stan/math/prim/ In addition: Warning message: running command 'C:/PROGRA~1/R/R-33~1.0/bin/x64/R CMD SHLIB file21e0675830d8.cpp 2> file21e0675830d8.cpp.err.txt' had status 1
Daniel Lee
Nov 1, 2016, 2:21:55 PM11/1/16
to stan-...@googlegroups.com
Thanks. What version of RStan? That looks like a bug that was fixed for 2.12. (I'm replying on my phone underground.... I'd try to find the actual issue if I had reliable internet.)
Daniel
Daniel Lee
Nov 1, 2016, 2:29:46 PM11/1/16
to stan-...@googlegroups.com
It was fixed for 2.12. If you're using 2.12 and it's still broken, I'll get it fixed for our next release.
Daniel
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Charles Driver
Nov 1, 2016, 3:03:03 PM11/1/16
to Stan users mailing list
You made me wonder, but I checked on my laptop which is definitely 2.12 and it's also broken.
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