fix parallel error with bayes_cen

moving geweke test within the parallel loop
Kuan-Liu-Lab · Nov 5, 2024 · ccb3f2d · ccb3f2d
1 parent 650643c
commit ccb3f2d
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Showing 6 changed files with 21 additions and 18 deletions.
diff --git a/R/bayesweight_cen.R b/R/bayesweight_cen.R
@@ -37,9 +37,9 @@
 #'                                                     C3 ~ L11 + L21 + A1 +
 #'                                                          L12 + L22 + A2),
 #'                                data = simdat_cen,
-#'                                n.iter = 2500,
-#'                                n.burnin = 1500,
-#'                                n.thin = 5,
+#'                                n.iter = 1500,
+#'                                n.burnin = 500,
+#'                                n.thin = 1,
 #'                                parallel = FALSE,
 #'                                n.chains = 1,
 #'                                seed = 890123)
@@ -318,6 +318,7 @@ bayesweight_cen <- function(trtmodel.list = list(A1 ~ L11 + L21,
                            # Combine MCMC output from multiple chains
                            out.mcmc <- as.mcmc(jagsfit)
                            return(do.call(rbind, lapply(out.mcmc, as.matrix)))
+
                          }
 
     parallel::stopCluster(cl)
@@ -337,14 +338,15 @@ bayesweight_cen <- function(trtmodel.list = list(A1 ~ L11 + L21,
 
     out.mcmc <- as.mcmc(jagsfit)
     posterior <- as.matrix(out.mcmc[[1]])
-  }
 
-  diagnostics <- geweke.diag(out.mcmc)
+    diagnostics <- geweke.diag(out.mcmc)
+
+    # Check diagnostics for convergence issues
+    significant_indices <- which(abs(diagnostics[[1]]$z) > 1.96)
+    if (length(significant_indices) > 0) {
+      warning("Some parameters have not converged with Geweke index > 1.96. More iterations may be needed.")
+    }
 
-  # Check diagnostics for convergence issues
-  significant_indices <- which(abs(diagnostics[[1]]$z) > 1.96)
-  if (length(significant_indices) > 0) {
-    warning("Some parameters have not converged with Geweke index > 1.96. More iterations may be needed.")
   }
 
   expit <- function(x){exp(x) / (1+exp(x))}

diff --git a/R/globals.R b/R/globals.R
@@ -1,3 +1,4 @@
 if (getRversion() >= "2.15.1") {
   utils::globalVariables(c("chain_idx"))
+  utils::globalVariables(c("x", "y", "Treatment", "Mean", "LowerCI", "UpperCI"))
 }
diff --git a/man/bayesweight_cen.Rd b/man/bayesweight_cen.Rd
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
diff --git a/tests/testthat/test-bayesweight_cen.R b/tests/testthat/test-bayesweight_cen.R
@@ -10,11 +10,11 @@ test_that("bayesweight_cen works with no errors", {
                                                       C2 ~ L11 + L21 + A1,
                                                       C3 ~ L11 + L21 + A1 + L12 + L22 + A2),
                                  data = simdat_cen,
-                                 n.iter = 2500,
-                                 n.burnin = 1500,
-                                 n.thin = 5,
-                                 parallel = FALSE,
-                                 n.chains = 1,
+                                 n.iter = 1500,
+                                 n.burnin = 500,
+                                 n.thin = 1,
+                                 parallel = TRUE,
+                                 n.chains = 2,
                                  seed = 890123)
 
   # Check that the weights_cen object has the expected dimensions (length or rows matching the data size)

diff --git a/vignettes/bayesmsm-censoring.Rmd b/vignettes/bayesmsm-censoring.Rmd
@@ -3,7 +3,7 @@ title: "Longitudinal causal analysis with informative right-censoring"
 author: "Xiao Yan, Kuan Liu"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{`bayesmsm` for longitudinal data without right-censoring}
+  %\VignetteIndexEntry{`bayesmsm` for longitudinal data with informative right-censoring}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---