Hello
I am not able to get R.4.0.1 to work on my company’s Rstudio Server (a linux based machine).
The company IT team suggested that I put the following in the Makevars file:
CXX14=g++ -std=c++1y -fPIC
CXX14FLAGS=-O3 -mtune=native -march=native -Wno-unused-variable -Wno-unused-function -Wno-macro-redefined
I then installed Rstan. I can run the example with these outputs:
> example(stan_model, package = "rstan", run.dontrun = TRUE)
Loading required package: StanHeaders
Loading required package: ggplot2
RStudio Community is a great place to get help:
https://community.rstudio.com/c/tidyverse
rstan (Version 2.21.2, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
stn_md> stancode <- 'data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}'
stn_md> mod <- stan_model(model_code = stancode, verbose = TRUE)
TRANSLATING MODEL '16a540c6086086816528e4524def24d9' FROM Stan CODE TO C++ CODE NOW.
successful in parsing the Stan model '16a540c6086086816528e4524def24d9'.
COMPILING THE C++ CODE FOR MODEL '16a540c6086086816528e4524def24d9' NOW.
OS: x86_64, linux-gnu; rstan: 2.21.2; Rcpp: 1.0.6; inline: 0.3.18
>> setting environment variables:
PKG_LIBS = '/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/rstan/lib//libStanServices.a' -L'/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/lib/' -lStanHeaders -L'/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppParallel/lib/' -ltbb
PKG_CPPFLAGS = -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/Rcpp/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppEigen/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppEigen/include/unsupported" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/BH/include" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/include/src/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppParallel/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1
>> Program source :
1 :
2 : // includes from the plugin
3 : // [[Rcpp::plugins(cpp14)]]
4 :
5 :
6 : // user includes
7 : #include <Rcpp.h>
8 : #include <rstan/io/rlist_ref_var_context.hpp>
9 : #include <rstan/io/r_ostream.hpp>
10 : #include <rstan/stan_args.hpp>
11 : #include <boost/integer/integer_log2.hpp>
12 : // Code generated by Stan version 2.21.0
13 :
14 : #include <stan/model/model_header.hpp>
15 :
16 : namespace model8e35166d41a_16a540c6086086816528e4524def24d9_namespace {
17 :
18 : using std::istream;
19 : using std::string;
20 : using std::stringstream;
21 : using std::vector;
22 : using stan::io::dump;
23 : using stan::math::lgamma;
24 : using stan::model::prob_grad;
25 : using namespace stan::math;
26 :
27 : static int current_statement_begin__;
28 :
29 : stan::io::program_reader prog_reader__() {
30 : stan::io::program_reader reader;
31 : reader.add_event(0, 0, "start", "model8e35166d41a_16a540c6086086816528e4524def24d9");
32 : reader.add_event(3, 1, "end", "model8e35166d41a_16a540c6086086816528e4524def24d9");
33 : return reader;
34 : }
35 :
36 : class model8e35166d41a_16a540c6086086816528e4524def24d9
37 : : public stan::model::model_base_crtp<model8e35166d41a_16a540c6086086816528e4524def24d9> {
38 : private:
39 : double y_mean;
40 : public:
41 : model8e35166d41a_16a540c6086086816528e4524def24d9(rstan::io::rlist_ref_var_context& context__,
42 : std::ostream* pstream__ = 0)
43 : : model_base_crtp(0) {
44 : ctor_body(context__, 0, pstream__);
45 : }
46 :
47 : model8e35166d41a_16a540c6086086816528e4524def24d9(stan::io::var_context& context__,
48 : unsigned int random_seed__,
49 : std::ostream* pstream__ = 0)
50 : : model_base_crtp(0) {
51 : ctor_body(context__, random_seed__, pstream__);
52 : }
53 :
54 : void ctor_body(stan::io::var_context& context__,
55 : unsigned int random_seed__,
56 : std::ostream* pstream__) {
57 : typedef double local_scalar_t__;
58 :
59 : boost::ecuyer1988 base_rng__ =
60 : stan::services::util::create_rng(random_seed__, 0);
61 : (void) base_rng__; // suppress unused var warning
62 :
63 : current_statement_begin__ = -1;
64 :
65 : static const char* function__ = "model8e35166d41a_16a540c6086086816528e4524def24d9_namespace::model8e35166d41a_16a540c6086086816528e4524def24d9";
66 : (void) function__; // dummy to suppress unused var warning
67 : size_t pos__;
68 : (void) pos__; // dummy to suppress unused var warning
69 : std::vector<int> vals_i__;
70 : std::vector<double> vals_r__;
71 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
72 : (void) DUMMY_VAR__; // suppress unused var warning
73 :
74 : try {
75 : // initialize data block variables from context__
76 : current_statement_begin__ = 1;
77 : context__.validate_dims("data initialization", "y_mean", "double", context__.to_vec());
78 : y_mean = double(0);
79 : vals_r__ = context__.vals_r("y_mean");
80 : pos__ = 0;
81 : y_mean = vals_r__[pos__++];
82 :
83 :
84 : // initialize transformed data variables
85 : // execute transformed data statements
86 :
87 : // validate transformed data
88 :
89 : // validate, set parameter ranges
90 : num_params_r__ = 0U;
91 : param_ranges_i__.clear();
92 : current_statement_begin__ = 1;
93 : num_params_r__ += 1;
94 : } catch (const std::exception& e) {
95 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
96 : // Next line prevents compiler griping about no return
97 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
98 : }
99 : }
100 :
101 : ~model8e35166d41a_16a540c6086086816528e4524def24d9() { }
102 :
103 :
104 : void transform_inits(const stan::io::var_context& context__,
105 : std::vector<int>& params_i__,
106 : std::vector<double>& params_r__,
107 : std::ostream* pstream__) const {
108 : typedef double local_scalar_t__;
109 : stan::io::writer<double> writer__(params_r__, params_i__);
110 : size_t pos__;
111 : (void) pos__; // dummy call to supress warning
112 : std::vector<double> vals_r__;
113 : std::vector<int> vals_i__;
114 :
115 : current_statement_begin__ = 1;
116 : if (!(context__.contains_r("y")))
117 : stan::lang::rethrow_located(std::runtime_error(std::string("Variable y missing")), current_statement_begin__, prog_reader__());
118 : vals_r__ = context__.vals_r("y");
119 : pos__ = 0U;
120 : context__.validate_dims("parameter initialization", "y", "double", context__.to_vec());
121 : double y(0);
122 : y = vals_r__[pos__++];
123 : try {
124 : writer__.scalar_unconstrain(y);
125 : } catch (const std::exception& e) {
126 : stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: ") + e.what()), current_statement_begin__, prog_reader__());
127 : }
128 :
129 : params_r__ = writer__.data_r();
130 : params_i__ = writer__.data_i();
131 : }
132 :
133 : void transform_inits(const stan::io::var_context& context,
134 : Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
135 : std::ostream* pstream__) const {
136 : std::vector<double> params_r_vec;
137 : std::vector<int> params_i_vec;
138 : transform_inits(context, params_i_vec, params_r_vec, pstream__);
139 : params_r.resize(params_r_vec.size());
140 : for (int i = 0; i < params_r.size(); ++i)
141 : params_r(i) = params_r_vec[i];
142 : }
143 :
144 :
145 : template <bool propto__, bool jacobian__, typename T__>
146 : T__ log_prob(std::vector<T__>& params_r__,
147 : std::vector<int>& params_i__,
148 : std::ostream* pstream__ = 0) const {
149 :
150 : typedef T__ local_scalar_t__;
151 :
152 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
153 : (void) DUMMY_VAR__; // dummy to suppress unused var warning
154 :
155 : T__ lp__(0.0);
156 : stan::math::accumulator<T__> lp_accum__;
157 : try {
158 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
159 :
160 : // model parameters
161 : current_statement_begin__ = 1;
162 : local_scalar_t__ y;
163 : (void) y; // dummy to suppress unused var warning
164 : if (jacobian__)
165 : y = in__.scalar_constrain(lp__);
166 : else
167 : y = in__.scalar_constrain();
168 :
169 : // model body
170 :
171 : current_statement_begin__ = 1;
172 : lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
173 :
174 : } catch (const std::exception& e) {
175 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
176 : // Next line prevents compiler griping about no return
177 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
178 : }
179 :
180 : lp_accum__.add(lp__);
181 : return lp_accum__.sum();
182 :
183 : } // log_prob()
184 :
185 : template <bool propto, bool jacobian, typename T_>
186 : T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
187 : std::ostream* pstream = 0) const {
188 : std::vector<T_> vec_params_r;
189 : vec_params_r.reserve(params_r.size());
190 : for (int i = 0; i < params_r.size(); ++i)
191 : vec_params_r.push_back(params_r(i));
192 : std::vector<int> vec_params_i;
193 : return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
194 : }
195 :
196 :
197 : void get_param_names(std::vector<std::string>& names__) const {
198 : names__.resize(0);
199 : names__.push_back("y");
200 : }
201 :
202 :
203 : void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
204 : dimss__.resize(0);
205 : std::vector<size_t> dims__;
206 : dims__.resize(0);
207 : dimss__.push_back(dims__);
208 : }
209 :
210 : template <typename RNG>
211 : void write_array(RNG& base_rng__,
212 : std::vector<double>& params_r__,
213 : std::vector<int>& params_i__,
214 : std::vector<double>& vars__,
215 : bool include_tparams__ = true,
216 : bool include_gqs__ = true,
217 : std::ostream* pstream__ = 0) const {
218 : typedef double local_scalar_t__;
219 :
220 : vars__.resize(0);
221 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
222 : static const char* function__ = "model8e35166d41a_16a540c6086086816528e4524def24d9_namespace::write_array";
223 : (void) function__; // dummy to suppress unused var warning
224 :
225 : // read-transform, write parameters
226 : double y = in__.scalar_constrain();
227 : vars__.push_back(y);
228 :
229 : double lp__ = 0.0;
230 : (void) lp__; // dummy to suppress unused var warning
231 : stan::math::accumulator<double> lp_accum__;
232 :
233 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
234 : (void) DUMMY_VAR__; // suppress unused var warning
235 :
236 : if (!include_tparams__ && !include_gqs__) return;
237 :
238 : try {
239 : if (!include_gqs__ && !include_tparams__) return;
240 : if (!include_gqs__) return;
241 : } catch (const std::exception& e) {
242 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
243 : // Next line prevents compiler griping about no return
244 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
245 : }
246 : }
247 :
248 : template <typename RNG>
249 : void write_array(RNG& base_rng,
250 : Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
251 : Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
252 : bool include_tparams = true,
253 : bool include_gqs = true,
254 : std::ostream* pstream = 0) const {
255 : std::vector<double> params_r_vec(params_r.size());
256 : for (int i = 0; i < params_r.size(); ++i)
257 : params_r_vec[i] = params_r(i);
258 : std::vector<double> vars_vec;
259 : std::vector<int> params_i_vec;
260 : write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
261 : vars.resize(vars_vec.size());
262 : for (int i = 0; i < vars.size(); ++i)
263 : vars(i) = vars_vec[i];
264 : }
265 :
266 : std::string model_name() const {
267 : return "model8e35166d41a_16a540c6086086816528e4524def24d9";
268 : }
269 :
270 :
271 : void constrained_param_names(std::vector<std::string>& param_names__,
272 : bool include_tparams__ = true,
273 : bool include_gqs__ = true) const {
274 : std::stringstream param_name_stream__;
275 : param_name_stream__.str(std::string());
276 : param_name_stream__ << "y";
277 : param_names__.push_back(param_name_stream__.str());
278 :
279 : if (!include_gqs__ && !include_tparams__) return;
280 :
281 : if (include_tparams__) {
282 : }
283 :
284 : if (!include_gqs__) return;
285 : }
286 :
287 :
288 : void unconstrained_param_names(std::vector<std::string>& param_names__,
289 : bool include_tparams__ = true,
290 : bool include_gqs__ = true) const {
291 : std::stringstream param_name_stream__;
292 : param_name_stream__.str(std::string());
293 : param_name_stream__ << "y";
294 : param_names__.push_back(param_name_stream__.str());
295 :
296 : if (!include_gqs__ && !include_tparams__) return;
297 :
298 : if (include_tparams__) {
299 : }
300 :
301 : if (!include_gqs__) return;
302 : }
303 :
304 : }; // model
305 :
306 : } // namespace
307 :
308 : typedef model8e35166d41a_16a540c6086086816528e4524def24d9_namespace::model8e35166d41a_16a540c6086086816528e4524def24d9 stan_model;
309 :
310 : #ifndef USING_R
311 :
312 : stan::model::model_base& new_model(
313 : stan::io::var_context& data_context,
314 : unsigned int seed,
315 : std::ostream* msg_stream) {
316 : stan_model* m = new stan_model(data_context, seed, msg_stream);
317 : return *m;
318 : }
319 :
320 : #endif
321 :
322 :
323 :
324 : #include <rstan_next/stan_fit.hpp>
325 :
326 : struct stan_model_holder {
327 : stan_model_holder(rstan::io::rlist_ref_var_context rcontext,
328 : unsigned int random_seed)
329 : : rcontext_(rcontext), random_seed_(random_seed)
330 : {
331 : }
332 :
333 : //stan::math::ChainableStack ad_stack;
334 : rstan::io::rlist_ref_var_context rcontext_;
335 : unsigned int random_seed_;
336 : };
337 :
338 : Rcpp::XPtr<stan::model::model_base> model_ptr(stan_model_holder* smh) {
339 : Rcpp::XPtr<stan::model::model_base> model_instance(new stan_model(smh->rcontext_, smh->random_seed_), true);
340 : return model_instance;
341 : }
342 :
343 : Rcpp::XPtr<rstan::stan_fit_base> fit_ptr(stan_model_holder* smh) {
344 : return Rcpp::XPtr<rstan::stan_fit_base>(new rstan::stan_fit(model_ptr(smh), smh->random_seed_), true);
345 : }
346 :
347 : std::string model_name(stan_model_holder* smh) {
348 : return model_ptr(smh).get()->model_name();
349 : }
350 :
351 : RCPP_MODULE(stan_fit4model8e35166d41a_16a540c6086086816528e4524def24d9_mod){
352 : Rcpp::class_<stan_model_holder>("stan_fit4model8e35166d41a_16a540c6086086816528e4524def24d9")
353 : .constructor<rstan::io::rlist_ref_var_context, unsigned int>()
354 : .method("model_ptr", &model_ptr)
355 : .method("fit_ptr", &fit_ptr)
356 : .method("model_name", &model_name)
357 : ;
358 : }
359 :
360 :
361 : // declarations
362 : extern "C" {
363 : SEXP file8e3558ad5d40( ) ;
364 : }
365 :
366 : // definition
367 : SEXP file8e3558ad5d40() {
368 : return Rcpp::wrap("16a540c6086086816528e4524def24d9");
369 : }
make cmd is
make -f '/opt/R/4.0.1/lib64/R/etc/Makeconf' -f '/opt/R/4.0.1/lib64/R/share/make/shlib.mk' -f '/mnt/home/u062721/.R/Makevars' CXX='$(CXX14) $(CXX14STD)' CXXFLAGS='$(CXX14FLAGS)' CXXPICFLAGS='$(CXX14PICFLAGS)' SHLIB_LDFLAGS='$(SHLIB_CXX14LDFLAGS)' SHLIB_LD='$(SHLIB_CXX14LD)' SHLIB='file8e3558ad5d40.so' OBJECTS='file8e3558ad5d40.o'
make would use
g++ -std=c++1y -fPIC -I"/opt/R/4.0.1/lib64/R/include" -DNDEBUG -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/Rcpp/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppEigen/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppEigen/include/unsupported" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/BH/include" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/include/src/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppParallel/include/" -I"/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -O3 -mtune=native -march=native -Wno-unused-variable -Wno-unused-function -Wno-macro-redefined -c file8e3558ad5d40.cpp -o file8e3558ad5d40.o
if test "zfile8e3558ad5d40.o" != "z"; then \
echo g++ -std=c++1y -fPIC -shared -L"/opt/R/4.0.1/lib64/R/lib" -L/usr/local/lib64 -o file8e3558ad5d40.so file8e3558ad5d40.o '/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/rstan/lib//libStanServices.a' -L'/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/lib/' -lStanHeaders -L'/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppParallel/lib/' -ltbb -L"/opt/R/4.0.1/lib64/R/lib" -lR; \
g++ -std=c++1y -fPIC -shared -L"/opt/R/4.0.1/lib64/R/lib" -L/usr/local/lib64 -o file8e3558ad5d40.so file8e3558ad5d40.o '/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/rstan/lib//libStanServices.a' -L'/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/StanHeaders/lib/' -lStanHeaders -L'/mnt/home/u062721/R/x86_64-pc-linux-gnu-library/4.0/RcppParallel/lib/' -ltbb -L"/opt/R/4.0.1/lib64/R/lib" -lR; \
fi
stn_md> fit <- sampling(mod, data = list(y_mean = 0))
SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.01 seconds (Warm-up)
Chain 1: 0.02 seconds (Sampling)
Chain 1: 0.03 seconds (Total)
Chain 1:
SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 2:
Chain 2: Elapsed Time: 0.01 seconds (Warm-up)
Chain 2: 0.01 seconds (Sampling)
Chain 2: 0.02 seconds (Total)
Chain 2:
SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
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SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 4).
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Chain 4: Adjust your expectations accordingly!
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stn_md> fit2 <- sampling(mod, data = list(y_mean = 5))
SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 2).
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SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 3).
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SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 4).
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Chain 4: Gradient evaluation took 0 seconds
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But then I cannot run my own stan model (that works in a previous R). Specifically, Rstudio would just crash! I reported the problem to the company IT team but they ignore me now.
Can someone here please advise if you have any suggestion? Thank you very much.