Last updated on 2026-09-24 02:52:17 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 28.66 | 538.98 | 567.64 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.78 | 363.72 | 382.50 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 352.03 | 373.03 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 358.85 | 378.85 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 43.00 | 522.00 | 565.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 27.41 | 519.49 | 546.90 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.16 | 518.28 | 543.44 | 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 | 47.00 | 437.00 | 484.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 8.00 | 88.00 | 96.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 52.00 | 576.00 | 628.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpFwIV3A/file7df05f2b6ba9.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpFwIV3A/file7df05f2b6ba9.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
2026-09-24 08:36:25.262 R[35723:354850] 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$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
Traceback:
20: 1: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, impute_predictive_arm(time = data_in$time[current_treatment], mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) hazards = treatment_hazards, random_input = random_inputs$current_treatment,
end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)21:
pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) 2:
fill_predictive_imputations(imputations = current, rows = current_treatment, 22: values = impute_predictive_arm(time = data_in$time[current_treatment], (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, hazards = treatment_hazards, random_input = random_inputs$current_treatment, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1,
1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, 3: treatment = 1), prop_loss = 0, alternative = "greater", impute_predictive_draws(data_in = data_interim, hazards = post_lambda, h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", binary_imputation = predictive_binary_imputation, check_futility = check_futility) imputed_final = FALSE, empty_interval = c("prior", "propagate",
"error"), return_trace = FALSE, ncores = 1L, backend = c("auto", 4: "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", evaluate_interim_decision(data_interim = data_interim, look = i, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, Qn = 1, rmst_tau = end_of_study) active_followup = active_followup_at(data_total, look_time), { Call <- match.call() end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, Arguments <- capture_arguments(sim_trials, environment()) single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, method <- normalize_analysis_method(method) prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, Arguments$method <- method h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, empty_interval <- match.arg(empty_interval) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, binary_imputation <- match.arg(binary_imputation) method = method, binary_imputation = binary_imputation, check_futility = check_futility, backend <- match.arg(backend) Qn = Qn[i]) Arguments$empty_interval <- empty_interval
Arguments$binary_imputation <- binary_imputation 5: Arguments$backend <- backendsurvival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, requested_backend <- backend cutpoints = cutpoints, N_total = N_total, lambda = lambda, caller_rng_kind <- RNGkind() lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, validate_positive_integer_scalar(N_trials, "N_trials") prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, single_arm <- is.null(hazard_control) binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, if (!single_arm) { prop_loss = prop_loss, alternative = alternative, h0 = h0, rand_ratio <- validate_randomization_args(N_total, block, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, rand_ratio, allocation_name = "rand_ratio") N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, Arguments$rand_ratio <- rand_ratio imputed_final = imputed_final, empty_interval = empty_interval, } return_trace = return_trace, prior_surv_final = prior_surv_final, prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) Arguments$prop_loss <- prop_loss
validate_logical_scalar(return_trace, "return_trace") 6: validate_positive_integer_scalar(N_impute, "N_impute")doTryCatch(return(expr), name, parentenv, handler) validate_positive_integer_scalar(N_mcmc, "N_mcmc")
validate_single_probability(mc_conf_level, "mc_conf_level", 7: upper_open = TRUE)tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (mc_conf_level <= 0.5) {
8: stop("'mc_conf_level' must be greater than 0.5 and less than 1")tryCatchList(expr, classes, parentenv, handlers)
} 9: validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > tryCatch({ 0), N_impute = N_impute) if (!is.null(trial_streams)) { if (identical(method, "rmst")) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) validate_endpoint_time(end_of_study, cutpoints, "end_of_study") } validate_rmst_args(rmst_tau, end_of_study, h0) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, validate_analysis_configuration(method, alternative, hazard_control = hazard_control, cutpoints = cutpoints, is.null(hazard_control), imputed_final) N_total = N_total, lambda = lambda, lambda_time = lambda_time, } interim_look = interim_look, end_of_study = end_of_study, validate_positive_integer_scalar(ncores, "ncores") prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, execution <- resolve_sim_execution(backend = backend, ncores = ncores, binary_imputation = binary_imputation, block = block, N_trials = N_trials) rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, backend <- execution$backend h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, workers <- execution$workers N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, stream_seed <- NULL method = method, imputed_final = imputed_final, empty_interval = empty_interval, if (!is.null(seed)) { return_trace = return_trace, prior_surv_final = prior_surv_final, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) !is.finite(seed) || seed != floor(seed)) { attr(result, "arguments") <- NULL stop("'seed' must be NULL or a single integer value") if (inherits(result, "goldilocks_trial")) { } attr(result$summary, "arguments") <- NULL old_kind <- RNGkind() } old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, list(trial = as.integer(x), result = result) inherits = FALSE) if (old_seed_exists) {}, error = function(error) { old_seed <- get(".Random.seed", envir = .GlobalEnv, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], inherits = FALSE) message = conditionMessage(error)) }}) on.exit({
do.call(RNGkind, as.list(old_kind))10: if (old_seed_exists) {FUN(X[[i]], ...) assign(".Random.seed", old_seed, envir = .GlobalEnv)
} else if (exists(".Random.seed", envir = .GlobalEnv, 11: inherits = FALSE)) {lapply(X = S, FUN = FUN, ...) rm(".Random.seed", envir = .GlobalEnv)
}12: }, add = TRUE)doTryCatch(return(expr), name, parentenv, handler) stream_seed <- seed
trial_streams <- make_rng_streams(stream_seed, N_trials)13: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) else if (backend == "psock") {
stream_seed <- sample.int(.Machine$integer.max - 1L, 14: size = 1L)tryCatchList(expr, classes, parentenv, handlers) trial_streams <- make_rng_streams(stream_seed, N_trials)
}15: else {tryCatch(expr, error = function(e) { trial_streams <- NULL call <- conditionCall(e) } if (!is.null(call)) { rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] if (identical(call[[1L]], quote(doTryCatch))) } else { call <- sys.call(-4L) "L'Ecuyer-CMRG" dcall <- deparse(call, nlines = 1L) }, seed_policy = if (!is.null(seed)) { prefix <- paste("Error in", dcall, ": ") "explicit_preserve_caller" LONG <- 75L } else if (backend == "psock") { sm <- strsplit(conditionMessage(e), "\n")[[1L]] "caller_derived_psock" } else { w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") "caller_state" if (is.na(w)) }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], stream_seed = stream_seed) type = "b") survival_adapt_fn <- if (backend == "psock") { if (w > LONG) make_psock_callable("survival_adapt") prefix <- paste0(prefix, "\n ") } } else { else prefix <- "Error : " survival_adapt msg <- paste0(prefix, conditionMessage(e), "\n") } .Internal(seterrmessage(msg[1L])) survival_adapt_wrapper <- function(x) { if (!silent && isTRUE(getOption("show.error.messages"))) { tryCatch({ cat(msg, file = outFile) if (!is.null(trial_streams)) { .Internal(printDeferredWarnings()) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } invisible(structure(msg, class = "try-error", condition = e)) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, }) hazard_control = hazard_control, cutpoints = cutpoints,
N_total = N_total, lambda = lambda, lambda_time = lambda_time, 16: interim_look = interim_look, end_of_study = end_of_study, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) prior_surv = prior_surv, prior_bin = prior_bin,
bin_method = bin_method, binary_imputation = binary_imputation, 17: block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) alternative = alternative, h0 = h0, Fn = Fn,
Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, 18: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, FUN(X[[i]], ...) method = method, imputed_final = imputed_final,
empty_interval = empty_interval, return_trace = return_trace, 19: prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, lapply(seq_len(cores), inner.do) rmst_tau = rmst_tau)
attr(result, "arguments") <- NULL20: if (inherits(result, "goldilocks_trial")) {mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, attr(result$summary, "arguments") <- NULL mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) } list(trial = as.integer(x), result = result)
}, error = function(error) {21: list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) message = conditionMessage(error))
})22: }(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, trial_index <- seq_len(N_trials) N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, trial_results <- switch(backend, sequential = lapply(trial_index, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, survival_adapt_wrapper), fork = pbmclapply(trial_index, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, survival_adapt_wrapper, mc.cores = workers), psock = { treatment = 1), prop_loss = 0, alternative = "greater", active_cluster <- make_sim_cluster(workers) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) imputed_final = FALSE, empty_interval = c("prior", "propagate", }) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", failed <- vapply(trial_results, function(x) is.null(x$result), "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", logical(1)) failed_results <- trial_results[failed] "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, failures <- data.frame(trial = vapply(failed_results, `[[`, Qn = 1, rmst_tau = end_of_study) integer(1), "trial"), error_class = vapply(failed_results, { `[[`, character(1), "error_class"), message = vapply(failed_results, Call <- match.call() `[[`, character(1), "message"), stringsAsFactors = FALSE) Arguments <- capture_arguments(sim_trials, environment()) if (all(failed)) { method <- normalize_analysis_method(method) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", Arguments$method <- method failures$message[1L])) empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) error$failures <- failures backend <- match.arg(backend) class(error) <- c("goldilocks_all_trials_failed", class(error)) Arguments$empty_interval <- empty_interval stop(error) Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend } requested_backend <- backend successful_trials <- trial_results[!failed] caller_rng_kind <- RNGkind() successful_results <- lapply(successful_trials, `[[`, "result") validate_positive_integer_scalar(N_trials, "N_trials") if (return_trace) { single_arm <- is.null(hazard_control) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) if (!single_arm) { traces <- bind_rows(lapply(seq_along(successful_results), rand_ratio <- validate_randomization_args(N_total, block, function(i) { rand_ratio, allocation_name = "rand_ratio") trace <- successful_results[[i]]$trace Arguments$rand_ratio <- rand_ratio trial <- successful_trials[[i]]$trial } trace$trial <- rep.int(trial, nrow(trace)) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss trace[c("trial", setdiff(names(trace), "trial"))] validate_logical_scalar(return_trace, "return_trace") })) validate_positive_integer_scalar(N_impute, "N_impute") out <- list(sims = sims, traces = traces, failures = failures, validate_positive_integer_scalar(N_mcmc, "N_mcmc") call = Call) validate_single_probability(mc_conf_level, "mc_conf_level", } upper_open = TRUE) else { if (mc_conf_level <= 0.5) { sims <- bind_rows(successful_results) stop("'mc_conf_level' must be greater than 0.5 and less than 1") out <- list(sims = sims, failures = failures, call = Call) } } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > attr(out$sims, "arguments") <- NULL 0), N_impute = N_impute) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, if (identical(method, "rmst")) { N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, validate_endpoint_time(end_of_study, cutpoints, "end_of_study") end_of_study = end_of_study) validate_rmst_args(rmst_tau, end_of_study, h0) attr(out, "decision_design") <- attr(successful_results[[1]], validate_analysis_configuration(method, alternative, "decision_design", exact = TRUE) is.null(hazard_control), imputed_final) attr(out, "prior_design") <- attr(successful_results[[1]], } "prior_design", exact = TRUE) validate_positive_integer_scalar(ncores, "ncores") attr(out, "rng_metadata") <- rng_metadata execution <- resolve_sim_execution(backend = backend, ncores = ncores, attr(out, "arguments") <- Arguments N_trials = N_trials) attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend <- execution$backend backend = backend, selection_reason = execution$reason, workers <- execution$workers requested_ncores = as.integer(ncores), workers = as.integer(workers), stream_seed <- NULL tasks = as.integer(N_trials)) if (!is.null(seed)) { if (nrow(failures) > 0L) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", !is.finite(seed) || seed != floor(seed)) { "for details.", call. = FALSE) stop("'seed' must be NULL or a single integer value") } } return(out) old_kind <- RNGkind()})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, inherits = FALSE) 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, if (old_seed_exists) { 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, old_seed <- get(".Random.seed", envir = .GlobalEnv, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, inherits = FALSE) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, } treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, on.exit({ Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, do.call(RNGkind, as.list(old_kind)) 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, if (old_seed_exists) { mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", assign(".Random.seed", old_seed, envir = .GlobalEnv) imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 } else if (exists(".Random.seed", envir = .GlobalEnv, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, inherits = FALSE)) { seed = 3425430) rm(".Random.seed", envir = .GlobalEnv)
}23: }, add = TRUE)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, stream_seed <- seed hazard_control = hazard_control_week, N_trials = 20, ncores = 2, trial_streams <- make_rng_streams(stream_seed, N_trials) seed = 3425430))) }
else if (backend == "psock") {24: stream_seed <- sample.int(.Machine$integer.max - 1L, eval(expr, envir) size = 1L)
trial_streams <- make_rng_streams(stream_seed, N_trials)25: }eval(expr, envir) else {
trial_streams <- NULL26: }withVisible(eval(expr, envir)) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {
caller_rng_kind[1]27: } else {withCallingHandlers(code, error = function (e) "L'Ecuyer-CMRG"rlang::entrace(e), message = function (cnd) }, seed_policy = if (!is.null(seed)) {{ "explicit_preserve_caller" watcher$capture_plot_and_output() } else if (backend == "psock") { if (on_message$capture) { "caller_derived_psock" watcher$push(cnd) } else { } "caller_state" if (on_message$silence) { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), invokeRestart("muffleMessage") stream_seed = stream_seed) } survival_adapt_fn <- if (backend == "psock") {}, warning = function (cnd) make_psock_callable("survival_adapt"){ } if (getOption("warn") >= 2 || getOption("warn") < 0) { else { return() survival_adapt } } watcher$capture_plot_and_output() survival_adapt_wrapper <- function(x) { if (on_warning$capture) { tryCatch({ cnd <- sanitize_call(cnd) if (!is.null(trial_streams)) { watcher$push(cnd) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } if (on_warning$silence) { result <- survival_adapt_fn(hazard_treatment = hazard_treatment, invokeRestart("muffleWarning") hazard_control = hazard_control, cutpoints = cutpoints, } N_total = N_total, lambda = lambda, lambda_time = lambda_time, }, error = function (cnd) interim_look = interim_look, end_of_study = end_of_study, { prior_surv = prior_surv, prior_bin = prior_bin, watcher$capture_plot_and_output() bin_method = bin_method, binary_imputation = binary_imputation, cnd <- sanitize_call(cnd) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, watcher$push(cnd) alternative = alternative, h0 = h0, Fn = Fn, switch(on_error, continue = invokeRestart("eval_continue"), Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, stop = invokeRestart("eval_stop"), error = NULL) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }) method = method, imputed_final = imputed_final,
empty_interval = empty_interval, return_trace = return_trace, 28: prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, eval(call) rmst_tau = rmst_tau)
attr(result, "arguments") <- NULL29: if (inherits(result, "goldilocks_trial")) {eval(call) attr(result$summary, "arguments") <- NULL
}30: list(trial = as.integer(x), result = result)with_handlers({ }, error = function(error) { for (expr in tle$exprs) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], ev <- withVisible(eval(expr, envir)) message = conditionMessage(error)) watcher$capture_plot_and_output() }) watcher$print_value(ev$value, ev$visible, envir) } } trial_index <- seq_len(N_trials) TRUE trial_results <- switch(backend, sequential = lapply(trial_index, }, handlers) survival_adapt_wrapper), fork = pbmclapply(trial_index,
survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers)31: on.exit(stop_sim_cluster(active_cluster), add = TRUE)doWithOneRestart(return(expr), restart) initialize_sim_cluster(active_cluster)
run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper)32: })withOneRestart(expr, restarts[[1L]]) failed <- vapply(trial_results, function(x) is.null(x$result),
logical(1))33: failed_results <- trial_results[failed]withRestartList(expr, restarts[-nr]) failures <- data.frame(trial = vapply(failed_results, `[[`,
integer(1), "trial"), error_class = vapply(failed_results, 34: `[[`, character(1), "error_class"), message = vapply(failed_results, doWithOneRestart(return(expr), restart) `[[`, character(1), "message"), stringsAsFactors = FALSE)
if (all(failed)) {35: error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) failures$message[1L]))
error$failures <- failures36: class(error) <- c("goldilocks_all_trials_failed", class(error))withRestartList(expr, restarts) stop(error)
}37: successful_trials <- trial_results[!failed]withRestarts(with_handlers({ successful_results <- lapply(successful_trials, `[[`, "result") for (expr in tle$exprs) { if (return_trace) { ev <- withVisible(eval(expr, envir)) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) watcher$capture_plot_and_output() traces <- bind_rows(lapply(seq_along(successful_results), watcher$print_value(ev$value, ev$visible, envir) function(i) { } trace <- successful_results[[i]]$trace TRUE trial <- successful_trials[[i]]$trial}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) trace$trial <- rep.int(trial, nrow(trace))
trace[c("trial", setdiff(names(trace), "trial"))]38: evaluate::evaluate(...) }))
out <- list(sims = sims, traces = traces, failures = failures, 39: call = Call)evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, } keep_message = if (is.numeric(options$message)) TRUE else options$message, else { stop_on_error = if (is.numeric(options$error)) options$error else { sims <- bind_rows(successful_results) if (options$error && options$include) out <- list(sims = sims, failures = failures, call = Call) 0L } else 2L attr(out$sims, "arguments") <- NULL }, output_handler = knit_handlers(options$render, options)) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda,
N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, 40: end_of_study = end_of_study)in_dir(input_dir(), expr) attr(out, "decision_design") <- attr(successful_results[[1]],
"decision_design", exact = TRUE)41: attr(out, "prior_design") <- attr(successful_results[[1]], in_input_dir(evaluate(code, envir = env, new_device = FALSE, "prior_design", exact = TRUE) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, attr(out, "rng_metadata") <- rng_metadata keep_message = if (is.numeric(options$message)) TRUE else options$message, attr(out, "arguments") <- Arguments stop_on_error = if (is.numeric(options$error)) options$error else { attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, if (options$error && options$include) backend = backend, selection_reason = execution$reason, 0L requested_ncores = as.integer(ncores), workers = as.integer(workers), else 2L tasks = as.integer(N_trials)) }, output_handler = knit_handlers(options$render, options))) if (nrow(failures) > 0L) {
warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", 42: "for details.", call. = FALSE)eng_r(options) }
return(out)43: })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, block_exec(params) 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, 44: 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, call_block(x) prior_surv = c(1, 144.927536231884, 1, 144.927536231884,
1, 285.714285714286), block = 3, rand_ratio = c(control = 1, 45: treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, process_group(group) 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, 46: mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 error = function(e) { ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, if (progress && is.function(pb$interrupt)) seed = 3425430) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 23: error <<- format(e)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)))47:
with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 24: error = function(e) {eval(expr, envir) if (progress && is.function(pb$interrupt))
pb$interrupt()25: if (is_R_CMD_build() || is_R_CMD_check()) eval(expr, envir) error <<- format(e)
}), list(rlang_trace_top_env = knit_global()))26:
withVisible(eval(expr, envir))48:
xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 27: error = function(e) {withCallingHandlers(code, error = function (e) if (progress && is.function(pb$interrupt)) rlang::entrace(e), message = function (cnd) pb$interrupt(){ if (is_R_CMD_build() || is_R_CMD_check()) watcher$capture_plot_and_output() error <<- format(e) if (on_message$capture) { }), list(rlang_trace_top_env = knit_global())), function(loc) { watcher$push(cnd) setwd(wd) } write_utf8(res, output %n% stdout()) if (on_message$silence) { paste0("\nQuitting from ", loc, if (!is.null(error)) invokeRestart("muffleMessage") paste0("\n", rule(), error, "\n", rule())) }}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)}, warning = function (cnd)
{49: if (getOption("warn") >= 2 || getOption("warn") < 0) {process_file(text, output) return()
}50: watcher$capture_plot_and_output()knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) if (on_warning$capture) {
cnd <- sanitize_call(cnd)51: watcher$push(cnd)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), } output_dir = getwd(), ...) if (on_warning$silence) {
invokeRestart("muffleWarning")52: }vweave_rmarkdown(...)}, error = function (cnd)
{53: watcher$capture_plot_and_output()engine$weave(file, quiet = quiet, encoding = enc) cnd <- sanitize_call(cnd)
watcher$push(cnd)54: switch(on_error, continue = invokeRestart("eval_continue"), doTryCatch(return(expr), name, parentenv, handler) stop = invokeRestart("eval_stop"), error = NULL)
})55:
tryCatchOne(expr, names, parentenv, handlers[[1L]])28:
eval(call)56:
tryCatchList(expr, classes, parentenv, handlers)29:
eval(call)57:
tryCatch({30: engine$weave(file, quiet = quiet, encoding = enc)with_handlers({ setwd(startdir) for (expr in tle$exprs) { output <- find_vignette_product(name, by = "weave", engine = engine) ev <- withVisible(eval(expr, envir)) if (!have.makefile && vignette_is_tex(output)) { watcher$capture_plot_and_output() texi2pdf(file = output, clean = FALSE, quiet = quiet) watcher$print_value(ev$value, ev$visible, envir) output <- find_vignette_product(name, by = "texi2pdf", } engine = engine) TRUE }}, error = function(e) {}, handlers) OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", 31: file, conditionMessage(e)))doWithOneRestart(return(expr), restart)})
32: 58: withOneRestart(expr, restarts[[1L]])tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks",
TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpFwIV3A/file7df075dbef97.rds")33:
withRestartList(expr, restarts[-nr])An irrecoverable exception occurred. R is aborting now ...
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$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/RtmpFwIV3A/file7df075dbef97.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
2026-09-24 08:36:31.203 R[37928:358147] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-09-24 08:36:32.815 R[38536:361486] 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
2026-09-24 08:36:43.888 R[38830:367305] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-09-24 08:36:45.850 R[38967:367711] 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