initial

R/draws.R

@@ -478,10 +478,6 @@ extract_data_nmar_as_na <- function(longdata) {
 #' @export
 draws.bayes <- function(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) {
 
-    if (!is.na(method$seed)) {
-        set.seed(method$seed)
-    }
-
     longdata <- longDataConstructor$new(data, vars)
     longdata$set_strategies(data_ice)
 

R/mcmc.R

@@ -61,7 +61,6 @@ fit_mcmc <- function(
 
     n_imputations <- method$n_samples
     burn_in <- method$burn_in
-    seed <- method$seed
     burn_between <- method$burn_between
     same_cov <- method$same_cov
 
@@ -114,13 +113,7 @@ fit_mcmc <- function(
         )
     )
 
-    assert_that(
-        !is.na(seed),
-        !is.null(seed),
-        is.numeric(seed),
-        msg = "mcmc seed is invalid"
-    )
-    sampling_args$seed <- seed
+    sampling_args$seed <- sample.int(.Machine$integer.max, 1)
 
     stan_fit <- record({
         do.call(sampling, sampling_args)

R/methods.R

@@ -44,10 +44,7 @@
 #' @param type a character string that specifies the resampling method used to perform inference
 #' when a conditional mean imputation approach (set via `method_condmean()`) is used. Must be one of `"bootstrap"` or `"jackknife"`.
 #'
-#' @param seed a numeric that specifies the seed to be used in the call to Stan. This
-#' argument is passed onto the `seed` argument of [rstan::sampling()]. Note that
-#' this is only required for `method_bayes()`, for all other methods you can achieve
-#' reproducible results by setting the seed via `set.seed()`. See details.
+#' @param seed deprecated. Please use `set.seed()` instead.
 #'
 #' @details
 #'
@@ -93,14 +90,20 @@ method_bayes <- function(
     burn_between = 50,
     same_cov = TRUE,
     n_samples = 20,
-    seed = sample.int(.Machine$integer.max, 1)
+    seed = NULL
 ) {
+    if (!is.null(seed)) {
+        warning(paste0(
+            "The `seed` argument to `method_bayes()` has been deprecated",
+            " please use `set.seed()` instead"
+        ))
+    }
+
     x <- list(
         burn_in = burn_in,
         burn_between = burn_between,
         same_cov = same_cov,
-        n_samples = n_samples,
-        seed = seed
+        n_samples = n_samples
     )
     return(as_class(x, c("method", "bayes")))
 }

data-raw/create_print_test_data.R

@@ -107,14 +107,14 @@ set.seed(413)
 dobj <- get_data(40)
 
 suppressWarnings({
+    set.seet(859)
     drawobj_b <- draws(
         data = dobj$dat,
         data_ice = dobj$dat_ice,
         vars = dobj$vars,
         method = method_bayes(
             n_samples = 50,
-            burn_between = 1,
-            seed = 859
+            burn_between = 1
         )
     )
 })

tests/testthat/_snaps/print.md

@@ -255,7 +255,6 @@
           burn_between: 1
           same_cov: TRUE
           n_samples: 50
-          seed: 859
       
 
 ---

tests/testthat/test-mcmc.R

@@ -529,8 +529,7 @@ test_that("fit_mcmc can recover known values with same_cov = FALSE", {
         n_samples = 250,
         burn_in = 100,
         burn_between = 3,
-        same_cov = FALSE,
-        seed = 8931
+        same_cov = FALSE
     )
 
     ### No missingness
@@ -604,36 +603,17 @@ test_that("fit_mcmc can recover known values with same_cov = FALSE", {
 })
 
 
-test_that("invalid seed throws an error", {
-
-    set.seed(301)
-    sigma <- as_vcov(c(6, 4, 4), c(0.5, 0.2, 0.3))
-    dat <- get_sim_data(50, sigma)
-
-    dat_ice <- dat %>%
-        group_by(id) %>%
-        arrange(desc(visit)) %>%
-        slice(1) %>%
-        ungroup() %>%
-        mutate(strategy = "MAR")
-
-    vars <- set_vars(
-        visit = "visit",
-        subjid = "id",
-        group = "group",
-        covariates = "sex",
-        strategy = "strategy",
-        outcome = "outcome"
-    )
-
-    expect_error(
-        draws(
-            dat,
-            dat_ice,
-            vars,
-            method_bayes(n_samples = 2, seed = NA),
-            quiet = TRUE
-        ),
-        regexp = "mcmc seed is invalid"
+test_that("seed argument to method_bayes is depreciated", {
+    expect_warning(
+        {
+            method <- method_bayes(
+                n_samples = 250,
+                burn_in = 100,
+                burn_between = 3,
+                same_cov = FALSE,
+                seed = 1234
+            )
+        },
+        regexp = "seed.*deprecated"
     )
 })

tests/testthat/test-print.R

@@ -104,8 +104,7 @@ test_that("print - bayesian", {
             vars = dobj$vars,
             method = method_bayes(
                 n_samples = 50,
-                burn_between = 1,
-                seed = 859
+                burn_between = 1
             ),
             quiet = TRUE
         )

tests/testthat/test-reproducibility.R

@@ -86,7 +86,7 @@ test_that("Results are Reproducible", {
 
 
 
-test_that("bayes - seed argument works without set.seed", {
+test_that("bayes - set.seed produces identical results", {
 
     sigma <- as_vcov(c(2, 1, 0.7), c(0.5, 0.3, 0.2))
     dat <- get_sim_data(200, sigma, trt = 8) %>%
@@ -111,17 +111,16 @@ test_that("bayes - seed argument works without set.seed", {
     )
 
     meth <- method_bayes(
-        seed = 1482,
         burn_between = 5,
         burn_in = 200,
-        n_samples = 2
+        n_samples = 6
     )
 
-    set.seed(49812)
+    set.seed(1234)
     x <- suppressWarnings({
         draws(dat, dat_ice, vars, meth, quiet = TRUE)
     })
-    set.seed(2414)
+    set.seed(1234)
     y <- suppressWarnings({
         draws(dat, dat_ice, vars, meth, quiet = TRUE)
     })

vignettes/advanced.html

@@ -714,7 +714,7 @@ <h1><span class="header-section-number">6</span> Custom imputation strategies</h
 <span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a><span class="co">#&gt;     pars &lt;- list(mu = mu, sigma = sigma)</span></span>
 <span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co">#&gt;     return(pars)</span></span>
 <span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a><span class="co">#&gt; }</span></span>
-<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#&gt; &lt;bytecode: 0x7ff37e6af218&gt;</span></span>
+<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#&gt; &lt;bytecode: 0x7f86686ebac0&gt;</span></span>
 <span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="co">#&gt; &lt;environment: namespace:rbmi&gt;</span></span></code></pre></div>
 <p>To further illustrate this for a simple example, assume that a new strategy is to be implemented as follows:
 - The marginal mean of the imputation distribution is equal to the marginal mean trajectory for the subject according to their assigned group and covariates up to the ICE.

vignettes/quickstart.Rmd

@@ -117,8 +117,7 @@ vars <- set_vars(
 method <- method_bayes(
     burn_in = 200,
     burn_between = 5,
-    n_samples = 150,
-    seed = 675442751
+    n_samples = 150
 )
 
 # Create samples for the imputation parameters by running the draws() function
@@ -347,8 +346,7 @@ vars <- set_vars(
 method <- method_bayes(
     burn_in = 200,
     burn_between = 5,
-    n_samples = 150,
-    seed = 675442751
+    n_samples = 150
 )