fix L_random to J_random, use prior in get_data, run-extended

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: dynamite
 Title: Bayesian Modeling and Causal Inference for Multivariate
     Longitudinal Data
-Version: 1.4.4
+Version: 1.4.5
 Authors@R: c(
     person("Santtu", "Tikka", email = "[email protected]", 
            role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4039-4342")),

NEWS.md

@@ -1,3 +1,8 @@
+# dynamite 1.4.5
+
+  * `get_data` method for `dynamitefit` object now correctly uses the previously defined priors instead of the default ones.
+  * Fixed a bug in indexing of random effect terms.
+
 # dynamite 1.4.4
 
   * Example of the `lfo()` method now uses a single chain and core to avoid a compatibility issue with CRAN.

R/getters.R

@@ -197,6 +197,7 @@ get_data.dynamitefit <- function(x, ...) {
     group = x$group_var,
     debug = list(no_compile = TRUE, stan_input = TRUE, model_code = FALSE),
     verbose = FALSE,
+    priors = x$priors,
     ...
   )
   out$stan_input$sampling_vars

R/prepare_stan_input.R

@@ -314,8 +314,7 @@ initialize_univariate_channel <- function(dformula, specials, fixed_pars,
     J = as.array(c(fixed_pars, varying_pars)),
     J_random = as.array(random_pars),
     L_fixed = as.array(seq_along(fixed_pars)),
-    L_varying = as.array(length(fixed_pars) + seq_along(varying_pars)),
-    L_random = as.array(seq_along(random_pars))
+    L_varying = as.array(length(fixed_pars) + seq_along(varying_pars))
   )
   channel <- c(channel, indices)
   sampling <- setNames(indices, paste0(names(indices), "_", y_name))
@@ -465,8 +464,7 @@ initialize_multivariate_channel <- function(y, y_cg, y_name, cg_idx,
       "J",
       "J_random",
       "L_fixed",
-      "L_varying",
-      "L_random"
+      "L_varying"
     )
     copy_channel <- setdiff(
       names(channel_vars[[z]]),

R/stanblocks_families.R

@@ -159,11 +159,11 @@ intercept_lines <- function(y, obs, family, has_varying, has_fixed, has_random,
     ifelse_(
       has_random_intercept,
       paste0(
-        glue::glue(" + rows_dot_product(X[t][{obs}, L_random_{ydim}], "),
+        glue::glue(" + rows_dot_product(X[t][{obs}, J_random_{ydim}], "),
         glue::glue("nu_{y}[{obs}, 2:K_random_{ydim}])")
       ),
       paste0(
-        glue::glue(" + rows_dot_product(X[t][{obs}, L_random_{ydim}], "),
+        glue::glue(" + rows_dot_product(X[t][{obs}, J_random_{ydim}], "),
         glue::glue("nu_{y}[{obs}, ])")
       )
     ),
@@ -279,7 +279,7 @@ loglik_lines_default <- function(y, idt, obs, family, has_missing,
       onlyif(
         has_random,
         c(
-          stan_array_arg(backend, "int", "L_random_{y}", 0L, TRUE),
+          stan_array_arg(backend, "int", "J_random_{y}", 0L, TRUE),
           "int K_random_{y}"
         )
       ),
@@ -616,7 +616,7 @@ loglik_categorical <- function(y, idt, obs, family, has_missing,
       onlyif(
         has_random,
         c(
-          stan_array_arg(backend, "int", "L_random_{y}", 0L, TRUE),
+          stan_array_arg(backend, "int", "J_random_{y}", 0L, TRUE),
           "int K_random_{y}"
         )
       ),
@@ -804,7 +804,7 @@ loglik_mvgaussian <- function(idt, cvars, cgvars, backend,
       onlyif(
         cvars[[i]]$has_random,
         c(
-          stan_array_arg(backend, "int", "L_random_{yi}", 0L, TRUE),
+          stan_array_arg(backend, "int", "J_random_{yi}", 0L, TRUE),
           "int K_random_{yi}"
         )
       ),
@@ -1117,10 +1117,6 @@ data_lines_default <- function(y, idt, has_random_intercept,
       K_varying > 0L,
       stan_array(backend, "int", "L_varying_{y}", "K_varying_{y}")
     ),
-    onlyif(
-      K_random > re_icpt,
-      stan_array(backend, "int", "L_random_{y}", "K_random_{y}{icpt}")
-    ),
     .indent = idt(1)
   )
 }
@@ -1999,7 +1995,7 @@ loglik_fun_args <- function(y, has_fixed, has_varying, has_missing,
     ),
     "y_{y}",
     onlyif(has_fixed_intercept || has_varying_intercept, "alpha_{y}"),
-    onlyif(has_random, c("L_random_{y}", "K_random_{y}")),
+    onlyif(has_random, c("J_random_{y}", "K_random_{y}")),
     onlyif(has_random || has_random_intercept, "nu_{y}"),
     onlyif(has_lfactor, c("lambda_{y}", "psi_{y}")),
     onlyif(has_fixed, c("{LJ}_fixed_{y}", "beta_{y}")),
@@ -2059,7 +2055,7 @@ model_lines_categorical <- function(y, idt, obs, family, priors,
       "y_{y}",
       "S_{y}",
       fun_args,
-      onlyif(has_random, c("L_random_{y}", "K_random_{y}")),
+      onlyif(has_random, c("J_random_{y}", "K_random_{y}")),
       onlyif(has_fixed, "{LJ}_fixed_{y}"),
       onlyif(has_varying, "{LJ}_varying_{y}"),
       onlyif(has_fixed || has_varying, c("J_{y}", "K_{y}")),
@@ -2169,7 +2165,7 @@ model_lines_mvgaussian <- function(cvars, cgvars, idt, backend, threading, ...)
           cvars[[i]]$has_fixed_intercept || cvars[[i]]$has_varying_intercept,
           "alpha_{yi}"
         ),
-        onlyif(cvars[[i]]$has_random, c("L_random_{yi}", "K_random_{yi}")),
+        onlyif(cvars[[i]]$has_random, c("J_random_{yi}", "K_random_{yi}")),
         onlyif(
           cvars[[i]]$has_random || cvars[[i]]$has_random_intercept,
           "nu_{yi}"