remove some redundant nmixture code

R/add_nmixture.R

@@ -114,10 +114,6 @@ add_nmixture = function(model_file,
            "Y_max[k] = max(flat_ys[K_inds[k, K_starts[k] : K_stops[k]]]);\n",
            "N_max[k] = max(cap[K_inds[k, K_starts[k] : K_stops[k]]]);\n",
            "}")
-  } else {
-    model_file[grep("transformed data {", model_file, fixed = TRUE)] <-
-      paste0("transformed data {\n",
-             "matrix[total_obs, num_basis] X_ordered = X[ytimes_array,  : ];")
   }
   model_file <- readLines(textConnection(model_file), n = -1)
 
@@ -379,26 +375,6 @@ add_nmix_model = function(model_file,
                                    model_file, fixed = TRUE)]
     model_file <- readLines(textConnection(model_file), n = -1)
 
-  } else {
-    model_file[grep('vector[n_nonmissing] flat_trends;',
-                    model_file, fixed = TRUE)] <- paste0('vector[n_nonmissing] flat_trends;\n',
-                                                         'vector[n_nonmissing] flat_ps;\n',
-                                                         'int flat_caps[n_nonmissing];')
-    model_file <- readLines(textConnection(model_file), n = -1)
-
-    model_file[grep('flat_trends = (to_vector(trend))[obs_ind];',
-                    model_file, fixed = TRUE)] <- paste0('flat_trends = (to_vector(trend))[obs_ind];\n',
-                                                         'flat_ps = p[obs_ind];\n',
-                                                         'flat_caps = cap[obs_ind];')
-    model_file <- readLines(textConnection(model_file), n = -1)
-
-    model_file[grep('flat_ys ~ poisson_log_glm(append_col(flat_xs, flat_trends),',
-                    model_file, fixed = TRUE)] <- paste0('for (i in 1:n_nonmissing){\n',
-                                                         'target += pb_lpmf(flat_ys[i] | flat_caps[i], flat_trends[i], flat_ps[i]);\n',
-                                                         '}')
-    model_file <- model_file[-grep('0.0,append_row(b, 1.0));',
-                                   model_file, fixed = TRUE)]
-    model_file <- readLines(textConnection(model_file), n = -1)
   }
 
   return(model_file)
@@ -407,69 +383,6 @@ add_nmix_model = function(model_file,
 add_nmix_functions = function(model_file,
                               trend_map,
                               nmix_trendmap){
-  if(nmix_trendmap){
-    # If trend_map supplied, no modifications needed
-  } else {
-    if(any(grepl('functions {', model_file, fixed = TRUE))){
-      model_file[grep('functions {', model_file, fixed = TRUE)] <-
-        paste0('functions {\n',
-               '/* Functions to return the log probability of a Poisson Binomial Mixture */\n',
-               '/* see Bollen et al 2023 for details (https://doi.org/10.1002/ece3.10595)*/\n',
-               'real poisbin_lpmf(int count, int k, real lambda, real p) {\n',
-               'if (count > k) {\n',
-               'return negative_infinity();\n',
-               '}\n',
-               'return poisson_log_lpmf(k | lambda) + binomial_logit_lupmf(count | k, p);\n',
-               '}\n',
-               'vector pb_logp(int count, int max_k,\n',
-               'real lambda, real p) {\n',
-               'if (max_k < count)\n',
-               'reject("cap variable max_k must be >= observed counts");\n',
-               'vector[max_k + 1] lp;\n',
-               'for (k in 0:(count - 1))\n',
-               'lp[k + 1] = negative_infinity();\n',
-               'for (k in count:max_k)\n',
-               'lp[k + 1] = poisbin_lpmf(count | k, lambda, p);\n',
-               'return lp;\n',
-               '}\n',
-               'real pb_lpmf(int count, int max_k,\n',
-               'real lambda, real p) {\n',
-               'vector[max_k + 1] lp;\n',
-               'lp = pb_logp(count, max_k, lambda, p);\n',
-               'return log_sum_exp(lp);\n',
-               '}')
-    } else {
-      model_file[grep('Stan model code', model_file)] <-
-        paste0('// Stan model code generated by package mvgam\n',
-               'functions {\n',
-               '/* Functions to return the log probability of a Poisson Binomial Mixture */\n',
-               '/* see Bollen et al 2023 for details (https://doi.org/10.1002/ece3.10595)*/\n',
-               'real poisbin_lpmf(int count, int k, real lambda, real p) {\n',
-               'if (count > k) {\n',
-               'return negative_infinity();\n',
-               '}\n',
-               'return poisson_log_lpmf(k | lambda) + binomial_logit_lupmf(count | k, p);\n',
-               '}\n',
-               'vector pb_logp(int count, int max_k,\n',
-               'real lambda, real p) {\n',
-               'if (max_k < count)\n',
-               'reject("cap variable max_k must be >= observed counts");\n',
-               'vector[max_k + 1] lp;\n',
-               'for (k in 0:(count - 1))\n',
-               'lp[k + 1] = negative_infinity();\n',
-               'for (k in count:max_k)\n',
-               'lp[k + 1] = poisbin_lpmf(count | k, lambda, p);\n',
-               'return lp;\n',
-               '}\n',
-               'real pb_lpmf(int count, int max_k,\n',
-               'real lambda, real p) {\n',
-               'vector[max_k + 1] lp;\n',
-               'lp = pb_logp(count, max_k, lambda, p);\n',
-               'return log_sum_exp(lp);\n',
-               '}\n}\n')
-    }
-  }
-
   model_file <- readLines(textConnection(model_file), n = -1)
   return(model_file)
 }