The model takes a long time to run. I have tried to vectorise it as far as I can, but it's still taking about a day to compute. I'm wondering if it's possible to speed it up.Maybe the problem isn't even with the code, but with the model being improper/bad priors/...? Or maybe the data set is simply very large and supposed to take hours to compute. Either way, for now, I dread adding another predictor.# data is in wide format, one row with one outcome and a range of predictorsset_cppo(mode = fast)code =                     // the model itselfdata        {       int<lower=1> nrow;   // 72000 (3 items per W*N*R)       int W;               // n words; 24       int N;               // n subjects; 100       int R;               // n raters; 10       int<lower=1,upper=nrow> ind[nrow]; // this vector is used to index fields per row       int<lower=0,upper=1> responses[nrow];  // outcomes       int subjects[nrow];       int words[nrow];       int sex[nrow];       int languages[nrow];       int raters[nrow];}parameters  {real intercept;               // fixedreal beta_language;real beta_sex;real interaction_s_l;real beta_rater[R];           // randomreal beta_word[W];real beta_subject[N];} model       {real theta[nrow]; beta_word ~ normal(0,100);         // priors beta_subject ~ normal(0,100); beta_language ~ normal(0,100); beta_sex ~ normal(0,100); interaction_s_l ~ normal(0,100);for (i in 1:nrow){                    // for each row, construct thetatheta[i] <- intercept          + beta_sex * sex[ind[i]]          + beta_language * languages[ind[i]]          + interaction_s_l * languages[ind[i]] * sex[ind[i]]          + beta_subject[subjects[ind[i]]]          + beta_rater[raters[ind[i]]];}responses ~ bernoulli_logit(theta);   // sample}generated quantities {               // get odds ratiosreal odds_intercept;real odds_sex;real odds_language;real odds_interaction;odds_intercept <- exp(intercept);odds_sex <- exp(beta_sex);odds_language <- exp(beta_language);odds_interaction <- exp(interaction_s_l);}fit <- stan(model_code = code, data = dat,     // run the model            iter = 4000, chains = 3)
Data model code running too slowly
performance;r;stan