Generalize

vignettes/sbc/R/prior.R

@@ -19,12 +19,16 @@ random_prior <- function(data, formula) {
     prior$prior[prior$class == "cortime"] <- lkj$stan
     prior$r[prior$class == "cortime"] <- lkj$r
   }
-  if ("ar" %in% prior$class) {
-    beta <- random_beta()
-    prior$prior[prior$class == "ar"] <- beta$stan
-    prior$r[prior$class == "ar"] <- beta$r
-    prior$lb[prior$class == "ar"] <- 0
-    prior$ub[prior$class == "ar"] <- 1
+  for (name in c("ar", "ma")) {
+    if (name %in% prior$class) {
+      for (index in which(prior$class == name)) {
+        beta <- random_beta()
+        prior$prior[index] <- beta$stan
+        prior$r[index] <- beta$r
+        prior$lb[index] <- 0
+        prior$ub[index] <- 1
+      }
+    }
   }
   if ("cosy" %in% prior$class) {
     beta <- random_beta()

vignettes/sbc/R/response.R

@@ -33,7 +33,7 @@ simulate_unstructured <- function(data, formula, prior) {
   list(data = data, parameters = parameters)
 }
 
-simulate_autoregressive <- function(data, formula, prior) {
+simulate_ar1 <- function(data, formula, prior) {
   beta <- simulate_beta(data = data, formula = formula, prior = prior)
   x_beta <- derive_x_beta(
     data = data,
@@ -51,6 +51,7 @@ simulate_autoregressive <- function(data, formula, prior) {
   n_time <- length(unique(data[[attr(data, "brm_time")]]))
   n_patient <- nrow(data) / n_time
   ar <- eval(parse(text = prior[prior$class == "ar", "r"]))
+  stopifnot(length(ar) == 1L)
   exponent <- abs(
     matrix(
       seq_len(n_time),

vignettes/sbc/R/scenarios.R

@@ -89,7 +89,7 @@ autoregressive <- function() {
     correlation = "autoregressive",
     autoregressive_order = 1L
   )
-  list(data = data, formula = formula, simulate = simulate_autoregressive)
+  list(data = data, formula = formula, simulate = simulate_ar1)
 }
 
 compound_symmetry <- function() {