Last updated on 2026-11-14 00:50:05 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 28.81 | 542.36 | 571.17 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.70 | 374.42 | 393.12 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | 355.55 | 375.55 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 366.04 | 386.04 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 43.00 | 596.00 | 639.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 26.91 | 519.09 | 546.00 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.95 | 529.38 | 555.33 | 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 | 42.00 | 427.00 | 469.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 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 | 651.00 | 705.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpGQ4TOp/file9bfc433c316f.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpGQ4TOp/file9bfc433c316f.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
2026-11-14 07:14:56.942 R[41656:372696] 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))
Traceback:
1:
impute_predictive_arm(time = data_in$time[current_treatment], 3: hazards = treatment_hazards, random_input = random_inputs$current_treatment, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm,
binary_imputation = predictive_binary_imputation, check_futility = check_futility) 2:
fill_predictive_imputations(imputations = current, rows = current_treatment, 4: values = impute_predictive_arm(time = data_in$time[current_treatment], evaluate_interim_decision(data_interim = data_interim, look = i, hazards = treatment_hazards, random_input = random_inputs$current_treatment, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) active_followup = active_followup_at(data_total, look_time),
end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, 3: single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, binary_imputation = predictive_binary_imputation, check_futility = check_futility) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval,
method = method, binary_imputation = binary_imputation, check_futility = check_futility, 4: Qn = Qn[i])evaluate_interim_decision(data_interim = data_interim, look = i,
planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, 5: active_followup = active_followup_at(data_total, look_time), survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, cutpoints = cutpoints, N_total = N_total, lambda = lambda, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, prop_loss = prop_loss, alternative = alternative, h0 = h0, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, Qn = Qn[i]) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method,
imputed_final = imputed_final, empty_interval = empty_interval, 5: return_trace = return_trace, prior_surv_final = prior_surv_final, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) cutpoints = cutpoints, N_total = N_total, lambda = lambda,
lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, 6: prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, doTryCatch(return(expr), name, parentenv, handler) binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio,
prop_loss = prop_loss, alternative = alternative, h0 = h0, 7: Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, tryCatchOne(expr, names, parentenv, handlers[[1L]]) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method,
imputed_final = imputed_final, empty_interval = empty_interval, 8: return_trace = return_trace, prior_surv_final = prior_surv_final, tryCatchList(expr, classes, parentenv, handlers) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
9: 6: tryCatch({doTryCatch(return(expr), name, parentenv, handler) if (!is.null(trial_streams)) {
assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) 7: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) result <- survival_adapt_fn(hazard_treatment = hazard_treatment,
hazard_control = hazard_control, cutpoints = cutpoints, 8: N_total = N_total, lambda = lambda, lambda_time = lambda_time, tryCatchList(expr, classes, parentenv, handlers) interim_look = interim_look, end_of_study = end_of_study,
prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, 9: binary_imputation = binary_imputation, block = block, tryCatch({ rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, if (!is.null(trial_streams)) { h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, } method = method, imputed_final = imputed_final, empty_interval = empty_interval, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, return_trace = return_trace, prior_surv_final = prior_surv_final, hazard_control = hazard_control, cutpoints = cutpoints, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) N_total = N_total, lambda = lambda, lambda_time = lambda_time, attr(result, "arguments") <- NULL interim_look = interim_look, end_of_study = end_of_study, if (inherits(result, "goldilocks_trial")) { prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, attr(result$summary, "arguments") <- NULL binary_imputation = binary_imputation, block = block, } rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, list(trial = as.integer(x), result = result) h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, }, error = function(error) { N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], method = method, imputed_final = imputed_final, empty_interval = empty_interval, message = conditionMessage(error)) return_trace = return_trace, prior_surv_final = prior_surv_final, }) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
attr(result, "arguments") <- NULL10: if (inherits(result, "goldilocks_trial")) {FUN(X[[i]], ...) attr(result$summary, "arguments") <- NULL
}11: list(trial = as.integer(x), result = result)lapply(X = S, FUN = FUN, ...)}, error = function(error) {
list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], 12: message = conditionMessage(error))doTryCatch(return(expr), name, parentenv, handler)})
13: 10: tryCatchOne(expr, names, parentenv, handlers[[1L]])FUN(X[[i]], ...)
14: 11: tryCatchList(expr, classes, parentenv, handlers)lapply(X = S, FUN = FUN, ...)
15: 12: tryCatch(expr, error = function(e) {doTryCatch(return(expr), name, parentenv, handler) call <- conditionCall(e)
if (!is.null(call)) {13: if (identical(call[[1L]], quote(doTryCatch))) tryCatchOne(expr, names, parentenv, handlers[[1L]]) call <- sys.call(-4L)
dcall <- deparse(call, nlines = 1L)14: prefix <- paste("Error in", dcall, ": ")tryCatchList(expr, classes, parentenv, handlers) LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]15: w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")tryCatch(expr, error = function(e) { if (is.na(w)) call <- conditionCall(e) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], if (!is.null(call)) { type = "b") if (identical(call[[1L]], quote(doTryCatch))) if (w > LONG) call <- sys.call(-4L) prefix <- paste0(prefix, "\n ") dcall <- deparse(call, nlines = 1L) } prefix <- paste("Error in", dcall, ": ") else prefix <- "Error : " LONG <- 75L msg <- paste0(prefix, conditionMessage(e), "\n") sm <- strsplit(conditionMessage(e), "\n")[[1L]] .Internal(seterrmessage(msg[1L])) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (!silent && isTRUE(getOption("show.error.messages"))) { if (is.na(w)) cat(msg, file = outFile) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], .Internal(printDeferredWarnings()) type = "b") } if (w > LONG) invisible(structure(msg, class = "try-error", condition = e)) prefix <- paste0(prefix, "\n ")}) }
else prefix <- "Error : "16: msg <- paste0(prefix, conditionMessage(e), "\n")try(lapply(X = S, FUN = FUN, ...), silent = TRUE) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {17: cat(msg, file = outFile)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) .Internal(printDeferredWarnings())
}18: invisible(structure(msg, class = "try-error", condition = e))FUN(X[[i]], ...)})
19: 16: lapply(seq_len(cores), inner.do)try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
20: 17: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
18: 21: FUN(X[[i]], ...)pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
19: 22: lapply(seq_len(cores), inner.do)(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL,
N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, 20: end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) treatment = 1), prop_loss = 0, alternative = "greater",
h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, 21: N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) imputed_final = FALSE, empty_interval = c("prior", "propagate",
"error"), return_trace = FALSE, ncores = 1L, backend = c("auto", 22: "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, Qn = 1, rmst_tau = end_of_study) 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, Call <- match.call() treatment = 1), prop_loss = 0, alternative = "greater", Arguments <- capture_arguments(sim_trials, environment()) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, method <- normalize_analysis_method(method) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", Arguments$method <- method imputed_final = FALSE, empty_interval = c("prior", "propagate", empty_interval <- match.arg(empty_interval) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", binary_imputation <- match.arg(binary_imputation) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", backend <- match.arg(backend) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Arguments$empty_interval <- empty_interval Qn = 1, rmst_tau = end_of_study) Arguments$binary_imputation <- binary_imputation{ Arguments$backend <- backend Call <- match.call() requested_backend <- backend Arguments <- capture_arguments(sim_trials, environment()) caller_rng_kind <- RNGkind() method <- normalize_analysis_method(method) validate_positive_integer_scalar(N_trials, "N_trials") Arguments$method <- method single_arm <- is.null(hazard_control) empty_interval <- match.arg(empty_interval) if (!single_arm) { binary_imputation <- match.arg(binary_imputation) rand_ratio <- validate_randomization_args(N_total, block, backend <- match.arg(backend) rand_ratio, allocation_name = "rand_ratio") Arguments$empty_interval <- empty_interval Arguments$rand_ratio <- rand_ratio Arguments$binary_imputation <- binary_imputation } Arguments$backend <- backend prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) requested_backend <- backend Arguments$prop_loss <- prop_loss caller_rng_kind <- RNGkind() validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_trials, "N_trials") validate_positive_integer_scalar(N_impute, "N_impute") single_arm <- is.null(hazard_control) validate_positive_integer_scalar(N_mcmc, "N_mcmc") if (!single_arm) { validate_single_probability(mc_conf_level, "mc_conf_level", rand_ratio <- validate_randomization_args(N_total, block, upper_open = TRUE) rand_ratio, allocation_name = "rand_ratio") if (mc_conf_level <= 0.5) { Arguments$rand_ratio <- rand_ratio stop("'mc_conf_level' must be greater than 0.5 and less than 1") } } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > Arguments$prop_loss <- prop_loss 0), N_impute = N_impute) validate_logical_scalar(return_trace, "return_trace") if (identical(method, "rmst")) { validate_positive_integer_scalar(N_impute, "N_impute") validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_rmst_args(rmst_tau, end_of_study, h0) validate_single_probability(mc_conf_level, "mc_conf_level", validate_analysis_configuration(method, alternative, upper_open = TRUE) is.null(hazard_control), imputed_final) if (mc_conf_level <= 0.5) { } stop("'mc_conf_level' must be greater than 0.5 and less than 1") validate_positive_integer_scalar(ncores, "ncores") } execution <- resolve_sim_execution(backend = backend, ncores = ncores, validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > N_trials = N_trials) 0), N_impute = N_impute) backend <- execution$backend if (identical(method, "rmst")) { workers <- execution$workers validate_endpoint_time(end_of_study, cutpoints, "end_of_study") stream_seed <- NULL validate_rmst_args(rmst_tau, end_of_study, h0) if (!is.null(seed)) { validate_analysis_configuration(method, alternative, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || is.null(hazard_control), imputed_final) !is.finite(seed) || seed != floor(seed)) { } stop("'seed' must be NULL or a single integer value") validate_positive_integer_scalar(ncores, "ncores") } execution <- resolve_sim_execution(backend = backend, ncores = ncores, old_kind <- RNGkind() N_trials = N_trials) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, backend <- execution$backend inherits = FALSE) workers <- execution$workers if (old_seed_exists) { stream_seed <- NULL old_seed <- get(".Random.seed", envir = .GlobalEnv, if (!is.null(seed)) { inherits = FALSE) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || } !is.finite(seed) || seed != floor(seed)) { on.exit({ stop("'seed' must be NULL or a single integer value") do.call(RNGkind, as.list(old_kind)) } if (old_seed_exists) { old_kind <- RNGkind() assign(".Random.seed", old_seed, envir = .GlobalEnv) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) inherits = FALSE)) { if (old_seed_exists) { rm(".Random.seed", envir = .GlobalEnv) old_seed <- get(".Random.seed", envir = .GlobalEnv, } inherits = FALSE) }, add = TRUE) } stream_seed <- seed on.exit({ trial_streams <- make_rng_streams(stream_seed, N_trials) do.call(RNGkind, as.list(old_kind)) } if (old_seed_exists) { else if (backend == "psock") { assign(".Random.seed", old_seed, envir = .GlobalEnv) stream_seed <- sample.int(.Machine$integer.max - 1L, } else if (exists(".Random.seed", envir = .GlobalEnv, size = 1L) inherits = FALSE)) { trial_streams <- make_rng_streams(stream_seed, N_trials) rm(".Random.seed", envir = .GlobalEnv) } } else { }, add = TRUE) trial_streams <- NULL stream_seed <- seed } trial_streams <- make_rng_streams(stream_seed, N_trials) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { } caller_rng_kind[1] else if (backend == "psock") { } else { stream_seed <- sample.int(.Machine$integer.max - 1L, "L'Ecuyer-CMRG" size = 1L) }, seed_policy = if (!is.null(seed)) { trial_streams <- make_rng_streams(stream_seed, N_trials) "explicit_preserve_caller" } } else if (backend == "psock") { else { "caller_derived_psock" trial_streams <- NULL } else { } "caller_state" rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), caller_rng_kind[1] stream_seed = stream_seed) } else { survival_adapt_fn <- if (backend == "psock") { "L'Ecuyer-CMRG" make_psock_callable("survival_adapt") }, seed_policy = if (!is.null(seed)) { } "explicit_preserve_caller" else { } else if (backend == "psock") { survival_adapt "caller_derived_psock" } } else { survival_adapt_wrapper <- function(x) { "caller_state" tryCatch({ }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), if (!is.null(trial_streams)) { stream_seed = stream_seed) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) survival_adapt_fn <- if (backend == "psock") { } make_psock_callable("survival_adapt") result <- survival_adapt_fn(hazard_treatment = hazard_treatment, } hazard_control = hazard_control, cutpoints = cutpoints, else { N_total = N_total, lambda = lambda, lambda_time = lambda_time, survival_adapt interim_look = interim_look, end_of_study = end_of_study, } prior_surv = prior_surv, prior_bin = prior_bin, survival_adapt_wrapper <- function(x) { bin_method = bin_method, binary_imputation = binary_imputation, tryCatch({ block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, if (!is.null(trial_streams)) { alternative = alternative, h0 = h0, Fn = Fn, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, } N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, method = method, imputed_final = imputed_final, hazard_control = hazard_control, cutpoints = cutpoints, empty_interval = empty_interval, return_trace = return_trace, N_total = N_total, lambda = lambda, lambda_time = lambda_time, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, interim_look = interim_look, end_of_study = end_of_study, rmst_tau = rmst_tau) prior_surv = prior_surv, prior_bin = prior_bin, attr(result, "arguments") <- NULL bin_method = bin_method, binary_imputation = binary_imputation, if (inherits(result, "goldilocks_trial")) { block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, attr(result$summary, "arguments") <- NULL alternative = alternative, h0 = h0, Fn = Fn, } Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, list(trial = as.integer(x), result = result) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }, error = function(error) { method = method, imputed_final = imputed_final, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], empty_interval = empty_interval, return_trace = return_trace, message = conditionMessage(error)) prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, }) rmst_tau = rmst_tau) } attr(result, "arguments") <- NULL trial_index <- seq_len(N_trials) if (inherits(result, "goldilocks_trial")) { trial_results <- switch(backend, sequential = lapply(trial_index, attr(result$summary, "arguments") <- NULL survival_adapt_wrapper), fork = pbmclapply(trial_index, } survival_adapt_wrapper, mc.cores = workers), psock = { list(trial = as.integer(x), result = result) active_cluster <- make_sim_cluster(workers) }, error = function(error) { on.exit(stop_sim_cluster(active_cluster), add = TRUE) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], initialize_sim_cluster(active_cluster) message = conditionMessage(error)) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) }) } failed <- vapply(trial_results, function(x) is.null(x$result), trial_index <- seq_len(N_trials) logical(1)) trial_results <- switch(backend, sequential = lapply(trial_index, failed_results <- trial_results[failed] survival_adapt_wrapper), fork = pbmclapply(trial_index, failures <- data.frame(trial = vapply(failed_results, `[[`, survival_adapt_wrapper, mc.cores = workers), psock = { integer(1), "trial"), error_class = vapply(failed_results, active_cluster <- make_sim_cluster(workers) `[[`, character(1), "error_class"), message = vapply(failed_results, on.exit(stop_sim_cluster(active_cluster), add = TRUE) `[[`, character(1), "message"), stringsAsFactors = FALSE) initialize_sim_cluster(active_cluster) if (all(failed)) { run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", }) failures$message[1L])) failed <- vapply(trial_results, function(x) is.null(x$result), error$failures <- failures logical(1)) class(error) <- c("goldilocks_all_trials_failed", class(error)) failed_results <- trial_results[failed] stop(error) failures <- data.frame(trial = vapply(failed_results, `[[`, } integer(1), "trial"), error_class = vapply(failed_results, successful_trials <- trial_results[!failed] `[[`, character(1), "error_class"), message = vapply(failed_results, successful_results <- lapply(successful_trials, `[[`, "result") `[[`, character(1), "message"), stringsAsFactors = FALSE) if (return_trace) { if (all(failed)) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", traces <- bind_rows(lapply(seq_along(successful_results), failures$message[1L])) function(i) { error$failures <- failures trace <- successful_results[[i]]$trace class(error) <- c("goldilocks_all_trials_failed", class(error)) trial <- successful_trials[[i]]$trial stop(error) trace$trial <- rep.int(trial, nrow(trace)) } trace[c("trial", setdiff(names(trace), "trial"))] successful_trials <- trial_results[!failed] })) successful_results <- lapply(successful_trials, `[[`, "result") out <- list(sims = sims, traces = traces, failures = failures, if (return_trace) { call = Call) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) } traces <- bind_rows(lapply(seq_along(successful_results), else { function(i) { sims <- bind_rows(successful_results) trace <- successful_results[[i]]$trace out <- list(sims = sims, failures = failures, call = Call) trial <- successful_trials[[i]]$trial } trace$trial <- rep.int(trial, nrow(trace)) attr(out$sims, "arguments") <- NULL trace[c("trial", setdiff(names(trace), "trial"))] attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, })) N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, out <- list(sims = sims, traces = traces, failures = failures, end_of_study = end_of_study) call = Call) attr(out, "decision_design") <- attr(successful_results[[1]], } "decision_design", exact = TRUE) else { attr(out, "prior_design") <- attr(successful_results[[1]], sims <- bind_rows(successful_results) "prior_design", exact = TRUE) out <- list(sims = sims, failures = failures, call = Call) attr(out, "rng_metadata") <- rng_metadata } attr(out, "arguments") <- Arguments attr(out$sims, "arguments") <- NULL attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, backend = backend, selection_reason = execution$reason, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, requested_ncores = as.integer(ncores), workers = as.integer(workers), end_of_study = end_of_study) tasks = as.integer(N_trials)) attr(out, "decision_design") <- attr(successful_results[[1]], if (nrow(failures) > 0L) { "decision_design", exact = TRUE) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", attr(out, "prior_design") <- attr(successful_results[[1]], "for details.", call. = FALSE) "prior_design", exact = TRUE) } attr(out, "rng_metadata") <- rng_metadata return(out) attr(out, "arguments") <- Arguments})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, backend = backend, selection_reason = execution$reason, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, requested_ncores = as.integer(ncores), workers = as.integer(workers), 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, tasks = as.integer(N_trials)) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, if (nrow(failures) > 0L) { 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, "for details.", call. = FALSE) 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, return(out) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, seed = 3425430) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333,
prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 23: 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, seed = 3425430))) 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", 24: imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168eval(expr, envir) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2,
seed = 3425430)25:
eval(expr, envir)23:
do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, 26: hazard_control = hazard_control_week, N_trials = 20, ncores = 2, withVisible(eval(expr, envir)) seed = 3425430)))
27: 24: withCallingHandlers(code, error = function (e) eval(expr, envir)rlang::entrace(e), message = function (cnd)
{25: watcher$capture_plot_and_output()eval(expr, envir) if (on_message$capture) {
watcher$push(cnd)26: }withVisible(eval(expr, envir)) if (on_message$silence) {
invokeRestart("muffleMessage")27: }withCallingHandlers(code, error = function (e) }, warning = function (cnd) rlang::entrace(e), message = function (cnd) {{ if (getOption("warn") >= 2 || getOption("warn") < 0) { watcher$capture_plot_and_output() return() if (on_message$capture) { } watcher$push(cnd) watcher$capture_plot_and_output() } if (on_warning$capture) { if (on_message$silence) { cnd <- sanitize_call(cnd) invokeRestart("muffleMessage") watcher$push(cnd) } }}, warning = function (cnd) if (on_warning$silence) {{ invokeRestart("muffleWarning") if (getOption("warn") >= 2 || getOption("warn") < 0) { } return()}, error = function (cnd) }{ watcher$capture_plot_and_output() watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) cnd <- sanitize_call(cnd) watcher$push(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), } stop = invokeRestart("eval_stop"), error = NULL) if (on_warning$silence) {}) invokeRestart("muffleWarning")
}28: }, error = function (cnd) eval(call){
watcher$capture_plot_and_output()29: cnd <- sanitize_call(cnd)eval(call) watcher$push(cnd)
switch(on_error, continue = invokeRestart("eval_continue"), 30: stop = invokeRestart("eval_stop"), error = NULL)with_handlers({}) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))28: watcher$capture_plot_and_output()eval(call) watcher$print_value(ev$value, ev$visible, envir)
}29: TRUEeval(call)}, handlers)
30: 31: with_handlers({doWithOneRestart(return(expr), restart) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))32: watcher$capture_plot_and_output()withOneRestart(expr, restarts[[1L]]) watcher$print_value(ev$value, ev$visible, envir)
}33: TRUEwithRestartList(expr, restarts[-nr])}, handlers)
34: 31: doWithOneRestart(return(expr), restart)doWithOneRestart(return(expr), restart)
35: 32: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])withOneRestart(expr, restarts[[1L]])
36: 33: withRestartList(expr, restarts)withRestartList(expr, restarts[-nr])
37: 34: withRestarts(with_handlers({doWithOneRestart(return(expr), restart) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))35: watcher$capture_plot_and_output()withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) watcher$print_value(ev$value, ev$visible, envir)
}36: TRUEwithRestartList(expr, restarts)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
37: 38: withRestarts(with_handlers({evaluate::evaluate(...) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))39: watcher$capture_plot_and_output()evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, watcher$print_value(ev$value, ev$visible, envir) keep_message = if (is.numeric(options$message)) TRUE else options$message, } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 38: 0Levaluate::evaluate(...) else 2L
}, output_handler = knit_handlers(options$render, options))39:
evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 40: keep_message = if (is.numeric(options$message)) TRUE else options$message, in_dir(input_dir(), expr) stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 41: 0Lin_input_dir(evaluate(code, envir = env, new_device = FALSE, else 2L keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, }, output_handler = knit_handlers(options$render, options)) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {40: if (options$error && options$include) in_dir(input_dir(), expr) 0L
else 2L41: }, output_handler = knit_handlers(options$render, options)))in_input_dir(evaluate(code, envir = env, new_device = FALSE,
keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 42: keep_message = if (is.numeric(options$message)) TRUE else options$message, eng_r(options) stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 43: 0Lblock_exec(params) else 2L
}, output_handler = knit_handlers(options$render, options)))44:
call_block(x)42:
eng_r(options)45:
process_group(group)43:
block_exec(params)46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 44: error = function(e) {call_block(x) if (progress && is.function(pb$interrupt))
pb$interrupt()45: if (is_R_CMD_build() || is_R_CMD_check()) process_group(group) error <<- format(e)
})46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 47: error = function(e) {with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (progress && is.function(pb$interrupt)) error = function(e) { pb$interrupt() if (progress && is.function(pb$interrupt)) if (is_R_CMD_build() || is_R_CMD_check()) pb$interrupt() error <<- format(e) if (is_R_CMD_build() || is_R_CMD_check()) }) error <<- format(e)
}), list(rlang_trace_top_env = knit_global()))47:
with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 48: error = function(e) {xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (progress && is.function(pb$interrupt)) error = function(e) { pb$interrupt() if (progress && is.function(pb$interrupt)) if (is_R_CMD_build() || is_R_CMD_check()) pb$interrupt() error <<- format(e) if (is_R_CMD_build() || is_R_CMD_check()) }), list(rlang_trace_top_env = knit_global())) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) {48: setwd(wd)xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), write_utf8(res, output %n% stdout()) error = function(e) { paste0("\nQuitting from ", loc, if (!is.null(error)) if (progress && is.function(pb$interrupt)) paste0("\n", rule(), error, "\n", rule())) pb$interrupt()}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)49: }), list(rlang_trace_top_env = knit_global())), function(loc) {process_file(text, output) setwd(wd)
write_utf8(res, output %n% stdout())50: paste0("\nQuitting from ", loc, if (!is.null(error)) knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) paste0("\n", rule(), error, "\n", rule()))
}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)51:
rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 49: output_dir = getwd(), ...)process_file(text, output)
52: 50: vweave_rmarkdown(...)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
53: 51: engine$weave(file, quiet = quiet, encoding = enc)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)54:
doTryCatch(return(expr), name, parentenv, handler)52:
vweave_rmarkdown(...)55:
tryCatchOne(expr, names, parentenv, handlers[[1L]])53:
engine$weave(file, quiet = quiet, encoding = enc)56:
tryCatchList(expr, classes, parentenv, handlers)54:
doTryCatch(return(expr), name, parentenv, handler)57:
tryCatch({55: engine$weave(file, quiet = quiet, encoding = enc)tryCatchOne(expr, names, parentenv, handlers[[1L]]) setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)56: if (!have.makefile && vignette_is_tex(output)) {tryCatchList(expr, classes, parentenv, handlers) texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf", 57: engine = engine)tryCatch({ } engine$weave(file, quiet = quiet, encoding = enc)}, error = function(e) { setwd(startdir) OK <<- FALSE output <- find_vignette_product(name, by = "weave", engine = engine) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", if (!have.makefile && vignette_is_tex(output)) { file, conditionMessage(e))) texi2pdf(file = output, clean = FALSE, quiet = quiet)}) output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)58: }tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", }, error = function(e) { TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpGQ4TOp/file9bfc18631b04.rds") OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", An irrecoverable exception occurred. R is aborting now ...
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/RtmpGQ4TOp/file9bfc18631b04.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
2026-11-14 07:15:02.905 R[42025:373670] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-11-14 07:15:04.701 R[42072:373829] 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
2026-11-14 07:15:17.099 R[42412:374754] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-11-14 07:15:18.961 R[42504:375067] 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