Last updated on 2026-10-31 01:51:59 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 25.71 | 540.33 | 566.04 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.50 | 353.51 | 372.01 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 369.17 | 390.17 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 360.17 | 380.17 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 44.00 | 514.00 | 558.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 25.00 | 520.70 | 545.70 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.07 | 526.02 | 551.09 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 44.00 | 428.00 | 472.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 10.00 | 91.00 | 101.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 54.00 | 578.00 | 632.00 | OK |
Version: 1.0.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘advent.Rmd’ using rmarkdown
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = future, rows = future_treatment, values = impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend requested_backend <- backend caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) } validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) backend <- execution$backend workers <- execution$workers stream_seed <- NULL if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } on.exit({ do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) } }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) } else { trial_streams <- NULL } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] } else { "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" } else if (backend == "psock") { "caller_derived_psock" } else { "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") } else { survival_adapt } survival_adapt_wrapper <- function(x) { tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result) }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) }) } trial_index <- seq_len(N_trials) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) error$failures <- failures class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) } successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] })) out <- list(sims = sims, traces = traces, failures = failures, call = Call) } else { sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) } attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) } return(out)})(N_total = 750, lambda = c(0.0666666666666667, 0.166666666666667, 0.333333333333333, 0.5, 0.666666666666667, 0.833333333333333, 1, 1.1), lambda_time = c(30L, 60L, 90L, 120L, 150L, 180L, 210L), interim_look = c(350, 450, 550, 650), end_of_study = 360L, prior_surv = c(0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 5, 10000), prior_surv_final = c(shape = 0.5, rate = 0.001 ), prior_bin = c(0.5, 0.5), bin_method = "quadrature", block = 2, rand_ratio = c(control = 1, treatment = 1), alternative = "less", Fn = c(0.05, 0.1, 0.1, 0.1), Sn = c(0.95, 0.9, 0.85, 0.8), N_impute = 30, empty_interval = "prior", method = "bayes-bin", imputed_final = TRUE, ncores = 2, cutpoints = c(90, 104, 150, 210), prop_loss = 0.075, N_trials = 500, hazard_treatment = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), hazard_control = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)
23: do.call(sim_trials, c(advent_effectiveness_args, list(N_trials = 500, hazard_treatment = eff_hazard_per_day, hazard_control = eff_hazard_per_day, h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)))
24: eval(expr, envir)
25: eval(expr, envir)
26: withVisible(eval(expr, envir))
27: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
28: eval(call)
29: eval(call)
30: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
31: doWithOneRestart(return(expr), restart)
32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpTa7ET4/file9f5015ea748e.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = future, rows = future_treatment, values = impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend requested_backend <- backend caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) } validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) backend <- execution$backend workers <- execution$workers stream_seed <- NULL if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } on.exit({ do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) } }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) } else { trial_streams <- NULL } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] } else { "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" } else if (backend == "psock") { "caller_derived_psock" } else { "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") } else { survival_adapt } survival_adapt_wrapper <- function(x) { tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result) }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) }) } trial_index <- seq_len(N_trials) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) error$failures <- failures class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) } successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] })) out <- list(sims = sims, traces = traces, failures = failures, call = Call) } else { sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) } attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) } return(out)})(N_total = 750, lambda = c(0.0666666666666667, 0.166666666666667, 0.333333333333333, 0.5, 0.666666666666667, 0.833333333333333, 1, 1.1), lambda_time = c(30L, 60L, 90L, 120L, 150L, 180L, 210L), interim_look = c(350, 450, 550, 650), end_of_study = 360L, prior_surv = c(0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 5, 10000), prior_surv_final = c(shape = 0.5, rate = 0.001 ), prior_bin = c(0.5, 0.5), bin_method = "quadrature", block = 2, rand_ratio = c(control = 1, treatment = 1), alternative = "less", Fn = c(0.05, 0.1, 0.1, 0.1), Sn = c(0.95, 0.9, 0.85, 0.8), N_impute = 30, empty_interval = "prior", method = "bayes-bin", imputed_final = TRUE, ncores = 2, cutpoints = c(90, 104, 150, 210), prop_loss = 0.075, N_trials = 500, hazard_treatment = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), hazard_control = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)
23: do.call(sim_trials, c(advent_effectiveness_args, list(N_trials = 500, hazard_treatment = eff_hazard_per_day, hazard_control = eff_hazard_per_day, h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)))
24: eval(expr, envir)
25: eval(expr, envir)
26: withVisible(eval(expr, envir))
27: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
28: eval(call)
29: eval(call)
30: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
31: doWithOneRestart(return(expr), restart)
32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpTa7ET4/file9f5015ea748e.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-10-31 11:42:36.123 R[42283:364491] XType: Using static font registry.
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend requested_backend <- backend
Traceback:
caller_rng_kind <- RNGkind() 1: validate_positive_integer_scalar(N_trials, "N_trials")impute_predictive_arm(time = data_in$time[current_treatment], single_arm <- is.null(hazard_control) hazards = treatment_hazards, random_input = random_inputs$current_treatment, if (!single_arm) { end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) rand_ratio <- validate_randomization_args(N_total, block,
rand_ratio, allocation_name = "rand_ratio") 2: Arguments$rand_ratio <- rand_ratiofill_predictive_imputations(imputations = current, rows = current_treatment, } values = impute_predictive_arm(time = data_in$time[current_treatment], prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) hazards = treatment_hazards, random_input = random_inputs$current_treatment, Arguments$prop_loss <- prop_loss end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) validate_logical_scalar(return_trace, "return_trace")
validate_positive_integer_scalar(N_impute, "N_impute") 3: validate_positive_integer_scalar(N_mcmc, "N_mcmc")impute_predictive_draws(data_in = data_interim, hazards = post_lambda, validate_single_probability(mc_conf_level, "mc_conf_level", end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, upper_open = TRUE) binary_imputation = predictive_binary_imputation, check_futility = check_futility) if (mc_conf_level <= 0.5) {
stop("'mc_conf_level' must be greater than 0.5 and less than 1") 4: }evaluate_interim_decision(data_interim = data_interim, look = i, validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, 0), N_impute = N_impute) if (identical(method, "rmst")) { active_followup = active_followup_at(data_total, look_time), validate_endpoint_time(end_of_study, cutpoints, "end_of_study") end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, validate_rmst_args(rmst_tau, end_of_study, h0) single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, validate_analysis_configuration(method, alternative, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, is.null(hazard_control), imputed_final) h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, } N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, validate_positive_integer_scalar(ncores, "ncores") method = method, binary_imputation = binary_imputation, check_futility = check_futility, execution <- resolve_sim_execution(backend = backend, ncores = ncores, Qn = Qn[i]) N_trials = N_trials) backend <- execution$backend
workers <- execution$workers 5: stream_seed <- NULLsurvival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, if (!is.null(seed)) { cutpoints = cutpoints, N_total = N_total, lambda = lambda, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, !is.finite(seed) || seed != floor(seed)) { prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, stop("'seed' must be NULL or a single integer value") binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, } prop_loss = prop_loss, alternative = alternative, h0 = h0, old_kind <- RNGkind() Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, inherits = FALSE) if (old_seed_exists) { imputed_final = imputed_final, empty_interval = empty_interval, old_seed <- get(".Random.seed", envir = .GlobalEnv, return_trace = return_trace, prior_surv_final = prior_surv_final, inherits = FALSE) } generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) on.exit({
do.call(RNGkind, as.list(old_kind)) 6: if (old_seed_exists) {doTryCatch(return(expr), name, parentenv, handler) assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv,
inherits = FALSE)) { 7: rm(".Random.seed", envir = .GlobalEnv) }tryCatchOne(expr, names, parentenv, handlers[[1L]]) }, add = TRUE) stream_seed <- seed
trial_streams <- make_rng_streams(stream_seed, N_trials) 8: }tryCatchList(expr, classes, parentenv, handlers) else if (backend == "psock") {
stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) 9: trial_streams <- make_rng_streams(stream_seed, N_trials)tryCatch({ } if (!is.null(trial_streams)) { else { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) trial_streams <- NULL } } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { hazard_control = hazard_control, cutpoints = cutpoints, caller_rng_kind[1] N_total = N_total, lambda = lambda, lambda_time = lambda_time, } else { interim_look = interim_look, end_of_study = end_of_study, "L'Ecuyer-CMRG" prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, }, seed_policy = if (!is.null(seed)) { binary_imputation = binary_imputation, block = block, "explicit_preserve_caller" } else if (backend == "psock") { rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, "caller_derived_psock" } else { h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, "caller_state" N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) method = method, imputed_final = imputed_final, empty_interval = empty_interval, survival_adapt_fn <- if (backend == "psock") { return_trace = return_trace, prior_surv_final = prior_surv_final, make_psock_callable("survival_adapt") generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) } attr(result, "arguments") <- NULL else { if (inherits(result, "goldilocks_trial")) { survival_adapt } attr(result$summary, "arguments") <- NULL survival_adapt_wrapper <- function(x) { } tryCatch({ list(trial = as.integer(x), result = result) if (!is.null(trial_streams)) {}, error = function(error) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, message = conditionMessage(error)) N_total = N_total, lambda = lambda, lambda_time = lambda_time, }) interim_look = interim_look, end_of_study = end_of_study,
prior_surv = prior_surv, prior_bin = prior_bin, 10: bin_method = bin_method, binary_imputation = binary_imputation, FUN(X[[i]], ...) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss,
alternative = alternative, h0 = h0, Fn = Fn, 11: Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, lapply(X = S, FUN = FUN, ...) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level,
method = method, imputed_final = imputed_final, 12: doTryCatch(return(expr), name, parentenv, handler) empty_interval = empty_interval, return_trace = return_trace,
prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, 13: rmst_tau = rmst_tau)tryCatchOne(expr, names, parentenv, handlers[[1L]]) attr(result, "arguments") <- NULL
if (inherits(result, "goldilocks_trial")) {14: attr(result$summary, "arguments") <- NULLtryCatchList(expr, classes, parentenv, handlers) }
15: list(trial = as.integer(x), result = result)tryCatch(expr, error = function(e) { }, error = function(error) { call <- conditionCall(e) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) }) if (!is.null(call)) { } trial_index <- seq_len(N_trials) if (identical(call[[1L]], quote(doTryCatch))) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, call <- sys.call(-4L) survival_adapt_wrapper, mc.cores = workers), psock = { dcall <- deparse(call, nlines = 1L) active_cluster <- make_sim_cluster(workers) prefix <- paste("Error in", dcall, ": ") on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) LONG <- 75L run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") }) if (is.na(w)) failed <- vapply(trial_results, function(x) is.null(x$result), w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], logical(1)) type = "b") failed_results <- trial_results[failed] if (w > LONG) failures <- data.frame(trial = vapply(failed_results, `[[`, prefix <- paste0(prefix, "\n ") integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, } `[[`, character(1), "message"), stringsAsFactors = FALSE) else prefix <- "Error : " if (all(failed)) { msg <- paste0(prefix, conditionMessage(e), "\n") error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", .Internal(seterrmessage(msg[1L])) failures$message[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { error$failures <- failures cat(msg, file = outFile) class(error) <- c("goldilocks_all_trials_failed", class(error)) .Internal(printDeferredWarnings()) stop(error) } } invisible(structure(msg, class = "try-error", condition = e)) successful_trials <- trial_results[!failed]}) successful_results <- lapply(successful_trials, `[[`, "result")
if (return_trace) {16: sims <- bind_rows(lapply(successful_results, function(x) x$summary))try(lapply(X = S, FUN = FUN, ...), silent = TRUE) traces <- bind_rows(lapply(seq_along(successful_results),
function(i) {17: trace <- successful_results[[i]]$tracesendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace))
trace[c("trial", setdiff(names(trace), "trial"))]18: }))FUN(X[[i]], ...) out <- list(sims = sims, traces = traces, failures = failures,
19: call = Call)lapply(seq_len(cores), inner.do) }
else { sims <- bind_rows(successful_results)20: out <- list(sims = sims, failures = failures, call = Call) }mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, attr(out$sims, "arguments") <- NULL mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look,
end_of_study = end_of_study)21: attr(out, "decision_design") <- attr(successful_results[[1]], pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]],
"prior_design", exact = TRUE)22: attr(out, "rng_metadata") <- rng_metadata(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, attr(out, "arguments") <- Arguments N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, backend = backend, selection_reason = execution$reason, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, requested_ncores = as.integer(ncores), workers = as.integer(workers), treatment = 1), prop_loss = 0, alternative = "greater", tasks = as.integer(N_trials)) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, if (nrow(failures) > 0L) { N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) imputed_final = FALSE, empty_interval = c("prior", "propagate", } "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", return(out) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, Qn = 1, rmst_tau = end_of_study) lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, { 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, Call <- match.call() 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, Arguments <- capture_arguments(sim_trials, environment()) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, method <- normalize_analysis_method(method) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, Arguments$method <- method treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, empty_interval <- match.arg(empty_interval) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, Arguments$empty_interval <- empty_interval mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", Arguments$binary_imputation <- binary_imputation imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 Arguments$backend <- backend ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, requested_backend <- backend seed = 3425430) caller_rng_kind <- RNGkind()
validate_positive_integer_scalar(N_trials, "N_trials")23: single_arm <- is.null(hazard_control)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, if (!single_arm) { hazard_control = hazard_control_week, N_trials = 20, ncores = 2, rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") seed = 3425430))) Arguments$rand_ratio <- rand_ratio
24: eval(expr, envir) }
prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm)25: Arguments$prop_loss <- prop_losseval(expr, envir) validate_logical_scalar(return_trace, "return_trace")
26: validate_positive_integer_scalar(N_impute, "N_impute")withVisible(eval(expr, envir)) validate_positive_integer_scalar(N_mcmc, "N_mcmc")
27: validate_single_probability(mc_conf_level, "mc_conf_level", withCallingHandlers(code, error = function (e) upper_open = TRUE)rlang::entrace(e), message = function (cnd) if (mc_conf_level <= 0.5) {{ stop("'mc_conf_level' must be greater than 0.5 and less than 1") watcher$capture_plot_and_output() } if (on_message$capture) { validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > watcher$push(cnd) 0), N_impute = N_impute) } if (identical(method, "rmst")) { if (on_message$silence) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") invokeRestart("muffleMessage") validate_rmst_args(rmst_tau, end_of_study, h0) } validate_analysis_configuration(method, alternative, }, warning = function (cnd) is.null(hazard_control), imputed_final){ } if (getOption("warn") >= 2 || getOption("warn") < 0) { validate_positive_integer_scalar(ncores, "ncores") return() execution <- resolve_sim_execution(backend = backend, ncores = ncores, } N_trials = N_trials) watcher$capture_plot_and_output() if (on_warning$capture) { backend <- execution$backend cnd <- sanitize_call(cnd) workers <- execution$workers watcher$push(cnd) stream_seed <- NULL } if (on_warning$silence) { if (!is.null(seed)) { invokeRestart("muffleWarning") if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || } !is.finite(seed) || seed != floor(seed)) {}, error = function (cnd) stop("'seed' must be NULL or a single integer value"){ } watcher$capture_plot_and_output() old_kind <- RNGkind() cnd <- sanitize_call(cnd) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, watcher$push(cnd) inherits = FALSE) switch(on_error, continue = invokeRestart("eval_continue"), if (old_seed_exists) { stop = invokeRestart("eval_stop"), error = NULL)}) old_seed <- get(".Random.seed", envir = .GlobalEnv,
inherits = FALSE)28: }eval(call) on.exit({
29: do.call(RNGkind, as.list(old_kind))eval(call) if (old_seed_exists) {
assign(".Random.seed", old_seed, envir = .GlobalEnv)30: } else if (exists(".Random.seed", envir = .GlobalEnv, with_handlers({ inherits = FALSE)) { for (expr in tle$exprs) { rm(".Random.seed", envir = .GlobalEnv) ev <- withVisible(eval(expr, envir)) } watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) }, add = TRUE) } stream_seed <- seed TRUE trial_streams <- make_rng_streams(stream_seed, N_trials)}, handlers) }
31: else if (backend == "psock") {doWithOneRestart(return(expr), restart) stream_seed <- sample.int(.Machine$integer.max - 1L,
size = 1L)32: trial_streams <- make_rng_streams(stream_seed, N_trials)withOneRestart(expr, restarts[[1L]]) }
else {33: trial_streams <- NULLwithRestartList(expr, restarts[-nr]) }
rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {34: caller_rng_kind[1]doWithOneRestart(return(expr), restart) } else {
"L'Ecuyer-CMRG"35: }, seed_policy = if (!is.null(seed)) {withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) "explicit_preserve_caller"
} else if (backend == "psock") {36: "caller_derived_psock"withRestartList(expr, restarts)
} else {37: withRestarts(with_handlers({ "caller_state" for (expr in tle$exprs) { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), ev <- withVisible(eval(expr, envir)) stream_seed = stream_seed) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) survival_adapt_fn <- if (backend == "psock") { } make_psock_callable("survival_adapt") TRUE }}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
else {38: survival_adaptevaluate::evaluate(...) }
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, survival_adapt_wrapper <- function(x) { keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { tryCatch({ if (options$error && options$include) 0L if (!is.null(trial_streams)) { else 2L }, output_handler = knit_handlers(options$render, options)) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)
}40: result <- survival_adapt_fn(hazard_treatment = hazard_treatment, in_dir(input_dir(), expr) hazard_control = hazard_control, cutpoints = cutpoints,
41: N_total = N_total, lambda = lambda, lambda_time = lambda_time, in_input_dir(evaluate(code, envir = env, new_device = FALSE, interim_look = interim_look, end_of_study = end_of_study, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, prior_surv = prior_surv, prior_bin = prior_bin, keep_message = if (is.numeric(options$message)) TRUE else options$message, bin_method = bin_method, binary_imputation = binary_imputation, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, 0L alternative = alternative, h0 = h0, Fn = Fn, else 2L Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, }, output_handler = knit_handlers(options$render, options))) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level,
method = method, imputed_final = imputed_final, 42: empty_interval = empty_interval, return_trace = return_trace, eng_r(options) prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints,
rmst_tau = rmst_tau)43: attr(result, "arguments") <- NULLblock_exec(params) if (inherits(result, "goldilocks_trial")) {
attr(result$summary, "arguments") <- NULL44: call_block(x) }
list(trial = as.integer(x), result = result)45: }, error = function(error) {process_group(group) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L],
message = conditionMessage(error))46: })withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), } error = function(e) { if (progress && is.function(pb$interrupt)) trial_index <- seq_len(N_trials) pb$interrupt() trial_results <- switch(backend, sequential = lapply(trial_index, if (is_R_CMD_build() || is_R_CMD_check()) survival_adapt_wrapper), fork = pbmclapply(trial_index, error <<- format(e) survival_adapt_wrapper, mc.cores = workers), psock = { }) active_cluster <- make_sim_cluster(workers)
on.exit(stop_sim_cluster(active_cluster), add = TRUE)47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), initialize_sim_cluster(active_cluster) error = function(e) { run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) if (progress && is.function(pb$interrupt)) pb$interrupt() }) failed <- vapply(trial_results, function(x) is.null(x$result), if (is_R_CMD_build() || is_R_CMD_check()) logical(1)) error <<- format(e) }), list(rlang_trace_top_env = knit_global())) failed_results <- trial_results[failed]
failures <- data.frame(trial = vapply(failed_results, `[[`, 48: integer(1), "trial"), error_class = vapply(failed_results, xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), `[[`, character(1), "error_class"), message = vapply(failed_results, error = function(e) { `[[`, character(1), "message"), stringsAsFactors = FALSE) if (progress && is.function(pb$interrupt)) if (all(failed)) { pb$interrupt() error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", if (is_R_CMD_build() || is_R_CMD_check()) failures$message[1L])) error <<- format(e) error$failures <- failures }), list(rlang_trace_top_env = knit_global())), function(loc) { class(error) <- c("goldilocks_all_trials_failed", class(error)) setwd(wd) stop(error) write_utf8(res, output %n% stdout()) } paste0("\nQuitting from ", loc, if (!is.null(error)) successful_trials <- trial_results[!failed] paste0("\n", rule(), error, "\n", rule())) successful_results <- lapply(successful_trials, `[[`, "result")}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) if (return_trace) {
49: sims <- bind_rows(lapply(successful_results, function(x) x$summary))process_file(text, output) traces <- bind_rows(lapply(seq_along(successful_results),
function(i) {50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) trace <- successful_results[[i]]$trace
trial <- successful_trials[[i]]$trial51: trace$trial <- rep.int(trial, nrow(trace))rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...) trace[c("trial", setdiff(names(trace), "trial"))] }))
out <- list(sims = sims, traces = traces, failures = failures, 52: call = Call)vweave_rmarkdown(...) }
else {53: sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call)engine$weave(file, quiet = quiet, encoding = enc) }
attr(out$sims, "arguments") <- NULL54: attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, doTryCatch(return(expr), name, parentenv, handler) N_total = N_total, lambda_time = lambda_time, interim_look = interim_look,
55: end_of_study = end_of_study)tryCatchOne(expr, names, parentenv, handlers[[1L]])
attr(out, "decision_design") <- attr(successful_results[[1]], 56: "decision_design", exact = TRUE)tryCatchList(expr, classes, parentenv, handlers)
attr(out, "prior_design") <- attr(successful_results[[1]], 57: "prior_design", exact = TRUE)tryCatch({ attr(out, "rng_metadata") <- rng_metadata engine$weave(file, quiet = quiet, encoding = enc) attr(out, "arguments") <- Arguments setwd(startdir) attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, output <- find_vignette_product(name, by = "weave", engine = engine) backend = backend, selection_reason = execution$reason, if (!have.makefile && vignette_is_tex(output)) { requested_ncores = as.integer(ncores), workers = as.integer(workers), texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", tasks = as.integer(N_trials)) engine = engine) if (nrow(failures) > 0L) { } warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", }, error = function(e) { "for details.", call. = FALSE) OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", } file, conditionMessage(e))) return(out)})})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000,
lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, 58: 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpTa7ET4/file9f503c1d7e93.rds") prior_surv = c(1, 144.927536231884, 1, 144.927536231884,
1, 285.714285714286), block = 3, rand_ratio = c(control = 1, An irrecoverable exception occurred. R is aborting now ...
treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, seed = 3425430)
23: do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, seed = 3425430)))
24: eval(expr, envir)
25: eval(expr, envir)
26: withVisible(eval(expr, envir))
27: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
28: eval(call)
29: eval(call)
30: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
31: doWithOneRestart(return(expr), restart)
32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpTa7ET4/file9f503c1d7e93.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-10-31 11:42:42.798 R[43241:366516] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-31 11:42:44.532 R[43489:367107] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-10-31 11:42:56.139 R[44866:369633] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-31 11:42:58.019 R[45033:370061] XType: Using static font registry.
--- finished re-building ‘two-arm.Rmd’
SUMMARY: processing the following files failed:
‘advent.Rmd’ ‘anthem-hfref.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-macos-arm64