Merge pull request #4 from openpharma/3-remove-seed-argument-in-metho…

…d_bayes-from-vignette remove ref to method_bayes seed
openpharma · Dec 4, 2024 · 36a6d2b · 36a6d2b
2 parents 4592819 + 004c188
commit 36a6d2b
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Showing 4 changed files with 7 additions and 10 deletions.
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
@@ -27,8 +27,7 @@ test_that("positive test get_imputed_data (from vignettes)", {
   method <- rbmi::method_bayes(
     n_samples = N_IMPUTATIONS,
     burn_in = 200,
-    burn_between = 5,
-    seed = 675442751
+    burn_between = 5
   )
 
   # Subset relevant columns

diff --git a/vignettes/Analyse2_with_ADMI.Rmd b/vignettes/Analyse2_with_ADMI.Rmd
@@ -29,9 +29,9 @@ library(readr)
 library(purrr)
 library(rbmi)
 library(beeca)
-
 library(rbmiUtils)
 
+set.seed(1974)
 data("ADEFF")
 
 ## Global constants
@@ -97,8 +97,7 @@ vars <- rbmi::set_vars(
 method <- rbmi::method_bayes(
     n_samples = N_IMPUTATIONS,
     burn_in = 200,
-    burn_between = 5,
-    seed = 675442751
+    burn_between = 5
 )
 
 # Create a dataset with the necessary columns

diff --git a/vignettes/Single_analysis_workflow.Rmd b/vignettes/Single_analysis_workflow.Rmd
@@ -26,6 +26,7 @@ This is an example of the {rbmi} only workflow. The analysis is as follows: Rubi
 This example exists to compare with the workflow which saves an ADMI data set, then uses {rbmiUtils} to complete the intended analysis. 
 
 ```{r}
+set.seed(1974)
 data("ADEFF")
 
 ## Global constants
@@ -96,12 +97,11 @@ vars <- rbmi::set_vars(
     covariates = c("BASE", "STRATA", "REGION")
 )
 
-# Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets)
+# Define which imputation method to use (here: Bayesian multiple imputation)
 method <- rbmi::method_bayes(
     n_samples = N_IMPUTATIONS,
     burn_in = 200,
-    burn_between = 5,
-    seed = 675442751
+    burn_between = 5
 )
 
 # Create a dataset with the necessary columns

diff --git a/vignettes/delta_analyse_mi_data.Rmd b/vignettes/delta_analyse_mi_data.Rmd
@@ -60,8 +60,7 @@ vars <- rbmi::set_vars(
 method <- rbmi::method_bayes(
     n_samples = N_IMPUTATIONS,
     burn_in = 200,
-    burn_between = 5,
-    seed = 675442751
+    burn_between = 5
 )
 
 # Subset relevant columns