Last updated on 2026-10-27 05:58:15 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 28.40 | 542.16 | 570.56 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 19.30 | 353.07 | 372.37 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 368.08 | 389.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 360.17 | 380.17 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 44.00 | 514.00 | 558.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 23.55 | 532.84 | 556.39 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.07 | 526.02 | 551.09 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 41.00 | 430.00 | 471.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 10.00 | 96.00 | 106.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 54.00 | 578.00 | 632.00 | OK |
Version: 1.0.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘advent.Rmd’ using rmarkdown
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = future, rows = future_treatment, values = impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend requested_backend <- backend caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) } validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) backend <- execution$backend workers <- execution$workers stream_seed <- NULL if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } on.exit({ do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) } }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) } else { trial_streams <- NULL } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] } else { "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" } else if (backend == "psock") { "caller_derived_psock" } else { "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") } else { survival_adapt } survival_adapt_wrapper <- function(x) { tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result) }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) }) } trial_index <- seq_len(N_trials) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) error$failures <- failures class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) } successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] })) out <- list(sims = sims, traces = traces, failures = failures, call = Call) } else { sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) } attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) } return(out)})(N_total = 750, lambda = c(0.0666666666666667, 0.166666666666667, 0.333333333333333, 0.5, 0.666666666666667, 0.833333333333333, 1, 1.1), lambda_time = c(30L, 60L, 90L, 120L, 150L, 180L, 210L), interim_look = c(350, 450, 550, 650), end_of_study = 360L, prior_surv = c(0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 5, 10000), prior_surv_final = c(shape = 0.5, rate = 0.001 ), prior_bin = c(0.5, 0.5), bin_method = "quadrature", block = 2, rand_ratio = c(control = 1, treatment = 1), alternative = "less", Fn = c(0.05, 0.1, 0.1, 0.1), Sn = c(0.95, 0.9, 0.85, 0.8), N_impute = 30, empty_interval = "prior", method = "bayes-bin", imputed_final = TRUE, ncores = 2, cutpoints = c(90, 104, 150, 210), prop_loss = 0.075, N_trials = 500, hazard_treatment = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), hazard_control = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)
23: do.call(sim_trials, c(advent_effectiveness_args, list(N_trials = 500, hazard_treatment = eff_hazard_per_day, hazard_control = eff_hazard_per_day, h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)))
24: eval(expr, envir)
25: eval(expr, envir)
26: withVisible(eval(expr, envir))
27: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
28: eval(call)
29: eval(call)
30: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
31: doWithOneRestart(return(expr), restart)
32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpTEYD5H/filed81d35f33882.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpTEYD5H/filed81d35f33882.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-10-27 14:34:07.932 R[57044:391656] 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,
Traceback:
interim_look = interim_look, end_of_study = end_of_study, 1: prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, impute_predictive_arm(time = data_in$time[current_treatment], rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, hazards = treatment_hazards, random_input = random_inputs$current_treatment, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level,
method = method, imputed_final = imputed_final, empty_interval = empty_interval, 2: return_trace = return_trace, prior_surv_final = prior_surv_final, fill_predictive_imputations(imputations = current, rows = current_treatment, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) values = impute_predictive_arm(time = data_in$time[current_treatment], attr(result, "arguments") <- NULL hazards = treatment_hazards, random_input = random_inputs$current_treatment, if (inherits(result, "goldilocks_trial")) { end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) attr(result$summary, "arguments") <- NULL
} 3: list(trial = as.integer(x), result = result)impute_predictive_draws(data_in = data_interim, hazards = post_lambda, }, error = function(error) { end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], binary_imputation = predictive_binary_imputation, check_futility = check_futility) message = conditionMessage(error))
}) 4:
evaluate_interim_decision(data_interim = data_interim, look = i, 10: planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, FUN(X[[i]], ...) active_followup = active_followup_at(data_total, look_time),
end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, 11: single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, lapply(X = S, FUN = FUN, ...) 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, 12: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, doTryCatch(return(expr), name, parentenv, handler) method = method, binary_imputation = binary_imputation, check_futility = check_futility,
Qn = Qn[i])13:
tryCatchOne(expr, names, parentenv, handlers[[1L]]) 5:
survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, 14: cutpoints = cutpoints, N_total = N_total, lambda = lambda, tryCatchList(expr, classes, parentenv, handlers) 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, 15: binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, tryCatch(expr, error = function(e) { prop_loss = prop_loss, alternative = alternative, h0 = h0, call <- conditionCall(e) Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, if (!is.null(call)) { N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, if (identical(call[[1L]], quote(doTryCatch))) imputed_final = imputed_final, empty_interval = empty_interval, call <- sys.call(-4L) return_trace = return_trace, prior_surv_final = prior_surv_final, dcall <- deparse(call, nlines = 1L) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L 6: sm <- strsplit(conditionMessage(e), "\n")[[1L]]doTryCatch(return(expr), name, parentenv, handler) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 7: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], tryCatchOne(expr, names, parentenv, handlers[[1L]]) type = "b")
if (w > LONG) 8: prefix <- paste0(prefix, "\n ")tryCatchList(expr, classes, parentenv, handlers) }
else prefix <- "Error : " 9: msg <- paste0(prefix, conditionMessage(e), "\n")tryCatch({ .Internal(seterrmessage(msg[1L])) if (!is.null(trial_streams)) { if (!silent && isTRUE(getOption("show.error.messages"))) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) cat(msg, file = outFile) } .Internal(printDeferredWarnings()) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, } hazard_control = hazard_control, cutpoints = cutpoints, invisible(structure(msg, class = "try-error", condition = e)) 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, 16: binary_imputation = binary_imputation, block = block, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative,
h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, 17: N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) method = method, imputed_final = imputed_final, empty_interval = empty_interval,
return_trace = return_trace, prior_surv_final = prior_surv_final, 18: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)FUN(X[[i]], ...) attr(result, "arguments") <- NULL
if (inherits(result, "goldilocks_trial")) {19: attr(result$summary, "arguments") <- NULLlapply(seq_len(cores), inner.do) }
list(trial = as.integer(x), result = result)20: }, error = function(error) {mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) message = conditionMessage(error))
})21:
pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)10:
FUN(X[[i]], ...)22:
(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, 11: N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, lapply(X = S, FUN = FUN, ...) 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, 12: treatment = 1), prop_loss = 0, alternative = "greater", doTryCatch(return(expr), name, parentenv, handler) 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", 13: imputed_final = FALSE, empty_interval = c("prior", "propagate", tryCatchOne(expr, names, parentenv, handlers[[1L]]) "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, 14: tryCatchList(expr, classes, parentenv, handlers) Qn = 1, rmst_tau = end_of_study)
{15: Call <- match.call()tryCatch(expr, error = function(e) { call <- conditionCall(e) Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method if (!is.null(call)) { empty_interval <- match.arg(empty_interval) if (identical(call[[1L]], quote(doTryCatch))) binary_imputation <- match.arg(binary_imputation) call <- sys.call(-4L) backend <- match.arg(backend) dcall <- deparse(call, nlines = 1L) Arguments$empty_interval <- empty_interval prefix <- paste("Error in", dcall, ": ") LONG <- 75L Arguments$binary_imputation <- binary_imputation sm <- strsplit(conditionMessage(e), "\n")[[1L]] Arguments$backend <- backend w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") requested_backend <- backend if (is.na(w)) caller_rng_kind <- RNGkind() w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], validate_positive_integer_scalar(N_trials, "N_trials") type = "b") if (w > LONG) single_arm <- is.null(hazard_control) prefix <- paste0(prefix, "\n ") if (!single_arm) { } rand_ratio <- validate_randomization_args(N_total, block, else prefix <- "Error : " rand_ratio, allocation_name = "rand_ratio") msg <- paste0(prefix, conditionMessage(e), "\n") Arguments$rand_ratio <- rand_ratio .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) { prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) cat(msg, file = outFile) Arguments$prop_loss <- prop_loss .Internal(printDeferredWarnings()) validate_logical_scalar(return_trace, "return_trace") } validate_positive_integer_scalar(N_impute, "N_impute") invisible(structure(msg, class = "try-error", condition = e))}) validate_positive_integer_scalar(N_mcmc, "N_mcmc")
validate_single_probability(mc_conf_level, "mc_conf_level", 16: upper_open = TRUE)try(lapply(X = S, FUN = FUN, ...), silent = TRUE) if (mc_conf_level <= 0.5) {
stop("'mc_conf_level' must be greater than 0.5 and less than 1")17: }sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss >
0), N_impute = N_impute)18: if (identical(method, "rmst")) {FUN(X[[i]], ...) validate_endpoint_time(end_of_study, cutpoints, "end_of_study")
validate_rmst_args(rmst_tau, end_of_study, h0)19: validate_analysis_configuration(method, alternative, lapply(seq_len(cores), inner.do) is.null(hazard_control), imputed_final)
}20: validate_positive_integer_scalar(ncores, "ncores")mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, execution <- resolve_sim_execution(backend = backend, ncores = ncores, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) N_trials = N_trials)
backend <- execution$backend21: workers <- execution$workers stream_seed <- NULLpbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) if (!is.null(seed)) {
if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || 22: !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value")(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, inherits = FALSE) 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, if (old_seed_exists) { 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", old_seed <- get(".Random.seed", envir = .GlobalEnv, imputed_final = FALSE, empty_interval = c("prior", "propagate", inherits = FALSE) } "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", on.exit({ "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, assign(".Random.seed", old_seed, envir = .GlobalEnv) Qn = 1, rmst_tau = end_of_study) { } else if (exists(".Random.seed", envir = .GlobalEnv, Call <- match.call() inherits = FALSE)) { Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method rm(".Random.seed", envir = .GlobalEnv) empty_interval <- match.arg(empty_interval) } }, add = TRUE) binary_imputation <- match.arg(binary_imputation) stream_seed <- seed backend <- match.arg(backend) trial_streams <- make_rng_streams(stream_seed, N_trials) Arguments$empty_interval <- empty_interval } Arguments$binary_imputation <- binary_imputation else if (backend == "psock") { Arguments$backend <- backend stream_seed <- sample.int(.Machine$integer.max - 1L, requested_backend <- backend size = 1L) caller_rng_kind <- RNGkind() trial_streams <- make_rng_streams(stream_seed, N_trials) } validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) if (!single_arm) { else { rand_ratio <- validate_randomization_args(N_total, block, trial_streams <- NULL rand_ratio, allocation_name = "rand_ratio") } Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] } else { Arguments$prop_loss <- prop_loss "L'Ecuyer-CMRG" validate_logical_scalar(return_trace, "return_trace") }, seed_policy = if (!is.null(seed)) { validate_positive_integer_scalar(N_impute, "N_impute") "explicit_preserve_caller" validate_positive_integer_scalar(N_mcmc, "N_mcmc") } else if (backend == "psock") { validate_single_probability(mc_conf_level, "mc_conf_level", "caller_derived_psock" upper_open = TRUE) } else { if (mc_conf_level <= 0.5) { "caller_state" stop("'mc_conf_level' must be greater than 0.5 and less than 1") } }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > stream_seed = stream_seed) 0), N_impute = N_impute) survival_adapt_fn <- if (backend == "psock") { if (identical(method, "rmst")) { make_psock_callable("survival_adapt") validate_endpoint_time(end_of_study, cutpoints, "end_of_study") } validate_rmst_args(rmst_tau, end_of_study, h0) else { validate_analysis_configuration(method, alternative, survival_adapt is.null(hazard_control), imputed_final) } } survival_adapt_wrapper <- function(x) { validate_positive_integer_scalar(ncores, "ncores") tryCatch({ execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) if (!is.null(trial_streams)) { backend <- execution$backend assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) workers <- execution$workers } stream_seed <- NULL result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, if (!is.null(seed)) { N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || interim_look = interim_look, end_of_study = end_of_study, !is.finite(seed) || seed != floor(seed)) { prior_surv = prior_surv, prior_bin = prior_bin, stop("'seed' must be NULL or a single integer value") bin_method = bin_method, binary_imputation = binary_imputation, } block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, old_kind <- RNGkind() alternative = alternative, h0 = h0, Fn = Fn, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, inherits = FALSE) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, method = method, imputed_final = imputed_final, inherits = FALSE) empty_interval = empty_interval, return_trace = return_trace, } prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, on.exit({ rmst_tau = rmst_tau) do.call(RNGkind, as.list(old_kind)) attr(result, "arguments") <- NULL if (old_seed_exists) { if (inherits(result, "goldilocks_trial")) { assign(".Random.seed", old_seed, envir = .GlobalEnv) attr(result$summary, "arguments") <- NULL } else if (exists(".Random.seed", envir = .GlobalEnv, } inherits = FALSE)) { list(trial = as.integer(x), result = result) rm(".Random.seed", envir = .GlobalEnv) }, error = function(error) { } list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], }, add = TRUE) message = conditionMessage(error)) stream_seed <- seed }) trial_streams <- make_rng_streams(stream_seed, N_trials) } } trial_index <- seq_len(N_trials) else if (backend == "psock") { trial_results <- switch(backend, sequential = lapply(trial_index, stream_seed <- sample.int(.Machine$integer.max - 1L, survival_adapt_wrapper), fork = pbmclapply(trial_index, size = 1L) survival_adapt_wrapper, mc.cores = workers), psock = { trial_streams <- make_rng_streams(stream_seed, N_trials) active_cluster <- make_sim_cluster(workers) } on.exit(stop_sim_cluster(active_cluster), add = TRUE) else { initialize_sim_cluster(active_cluster) trial_streams <- NULL run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) } }) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { failed <- vapply(trial_results, function(x) is.null(x$result), caller_rng_kind[1] logical(1)) } else { failed_results <- trial_results[failed] "L'Ecuyer-CMRG" failures <- data.frame(trial = vapply(failed_results, `[[`, }, seed_policy = if (!is.null(seed)) { integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, "explicit_preserve_caller" `[[`, character(1), "message"), stringsAsFactors = FALSE) } else if (backend == "psock") { if (all(failed)) { "caller_derived_psock" error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", } else { failures$message[1L])) error$failures <- failures "caller_state" class(error) <- c("goldilocks_all_trials_failed", class(error)) }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stop(error) stream_seed = stream_seed) } survival_adapt_fn <- if (backend == "psock") { successful_trials <- trial_results[!failed] make_psock_callable("survival_adapt") successful_results <- lapply(successful_trials, `[[`, "result") } if (return_trace) { else { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) survival_adapt traces <- bind_rows(lapply(seq_along(successful_results), } function(i) { survival_adapt_wrapper <- function(x) { trace <- successful_results[[i]]$trace tryCatch({ trial <- successful_trials[[i]]$trial if (!is.null(trial_streams)) { trace$trial <- rep.int(trial, nrow(trace)) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) trace[c("trial", setdiff(names(trace), "trial"))] } })) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, out <- list(sims = sims, traces = traces, failures = failures, hazard_control = hazard_control, cutpoints = cutpoints, call = Call) N_total = N_total, lambda = lambda, lambda_time = lambda_time, } interim_look = interim_look, end_of_study = end_of_study, else { prior_surv = prior_surv, prior_bin = prior_bin, sims <- bind_rows(successful_results) bin_method = bin_method, binary_imputation = binary_imputation, out <- list(sims = sims, failures = failures, call = Call) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, } alternative = alternative, h0 = h0, Fn = Fn, attr(out$sims, "arguments") <- NULL Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, method = method, imputed_final = imputed_final, end_of_study = end_of_study) empty_interval = empty_interval, return_trace = return_trace, attr(out, "decision_design") <- attr(successful_results[[1]], prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, "decision_design", exact = TRUE) rmst_tau = rmst_tau) attr(out, "prior_design") <- attr(successful_results[[1]], attr(result, "arguments") <- NULL "prior_design", exact = TRUE) if (inherits(result, "goldilocks_trial")) { attr(out, "rng_metadata") <- rng_metadata attr(result$summary, "arguments") <- NULL attr(out, "arguments") <- Arguments } attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, list(trial = as.integer(x), result = result) backend = backend, selection_reason = execution$reason, }, error = function(error) { requested_ncores = as.integer(ncores), workers = as.integer(workers), list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], tasks = as.integer(N_trials)) message = conditionMessage(error)) if (nrow(failures) > 0L) { }) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", } "for details.", call. = FALSE) trial_index <- seq_len(N_trials) } trial_results <- switch(backend, sequential = lapply(trial_index, return(out) survival_adapt_wrapper), fork = pbmclapply(trial_index, })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, survival_adapt_wrapper, mc.cores = workers), psock = { 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, active_cluster <- make_sim_cluster(workers) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, on.exit(stop_sim_cluster(active_cluster), add = TRUE) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, initialize_sim_cluster(active_cluster) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, }) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) failed_results <- trial_results[failed] 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", failures <- data.frame(trial = vapply(failed_results, `[[`, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 integer(1), "trial"), error_class = vapply(failed_results, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, `[[`, character(1), "error_class"), message = vapply(failed_results, seed = 3425430) `[[`, character(1), "message"), stringsAsFactors = FALSE)
if (all(failed)) {23: error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, failures$message[1L])) hazard_control = hazard_control_week, N_trials = 20, ncores = 2, error$failures <- failures seed = 3425430))) class(error) <- c("goldilocks_all_trials_failed", class(error))
stop(error)24: }eval(expr, envir) successful_trials <- trial_results[!failed]
successful_results <- lapply(successful_trials, `[[`, "result")25: if (return_trace) {eval(expr, envir) sims <- bind_rows(lapply(successful_results, function(x) x$summary))
traces <- bind_rows(lapply(seq_along(successful_results), 26: function(i) {withVisible(eval(expr, envir)) trace <- successful_results[[i]]$trace
trial <- successful_trials[[i]]$trial27: trace$trial <- rep.int(trial, nrow(trace))withCallingHandlers(code, error = function (e) trace[c("trial", setdiff(names(trace), "trial"))]rlang::entrace(e), message = function (cnd) })){ out <- list(sims = sims, traces = traces, failures = failures, watcher$capture_plot_and_output() call = Call) if (on_message$capture) { } watcher$push(cnd) else { } sims <- bind_rows(successful_results) if (on_message$silence) { out <- list(sims = sims, failures = failures, call = Call) invokeRestart("muffleMessage") } } attr(out$sims, "arguments") <- NULL}, warning = function (cnd) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, { N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, if (getOption("warn") >= 2 || getOption("warn") < 0) { end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], return() "decision_design", exact = TRUE) } attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) watcher$capture_plot_and_output() attr(out, "rng_metadata") <- rng_metadata if (on_warning$capture) { attr(out, "arguments") <- Arguments cnd <- sanitize_call(cnd) attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, watcher$push(cnd) backend = backend, selection_reason = execution$reason, } if (on_warning$silence) { requested_ncores = as.integer(ncores), workers = as.integer(workers), invokeRestart("muffleWarning") tasks = as.integer(N_trials)) }}, error = function (cnd) if (nrow(failures) > 0L) {{ watcher$capture_plot_and_output() warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", cnd <- sanitize_call(cnd) "for details.", call. = FALSE) watcher$push(cnd) } switch(on_error, continue = invokeRestart("eval_continue"), return(out) stop = invokeRestart("eval_stop"), error = NULL)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, }) lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333,
8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 28: 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, eval(call) 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, 29: mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168eval(call) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2,
seed = 3425430)30:
with_handlers({23: for (expr in tle$exprs) {do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, ev <- withVisible(eval(expr, envir)) hazard_control = hazard_control_week, N_trials = 20, ncores = 2, watcher$capture_plot_and_output() seed = 3425430))) watcher$print_value(ev$value, ev$visible, envir)
}24: TRUEeval(expr, envir)
}, handlers)25:
31: eval(expr, envir)doWithOneRestart(return(expr), restart)
26: withVisible(eval(expr, envir))
32: 27: withOneRestart(expr, restarts[[1L]])withCallingHandlers(code, error = function (e)
33: rlang::entrace(e), message = function (cnd) withRestartList(expr, restarts[-nr]){
watcher$capture_plot_and_output()34: if (on_message$capture) {doWithOneRestart(return(expr), restart)
watcher$push(cnd)35: }withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) if (on_message$silence) {
36: invokeRestart("muffleMessage")withRestartList(expr, restarts) }
}, warning = function (cnd) {37: withRestarts(with_handlers({ if (getOption("warn") >= 2 || getOption("warn") < 0) { for (expr in tle$exprs) { return() ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() } watcher$print_value(ev$value, ev$visible, envir) watcher$capture_plot_and_output() } if (on_warning$capture) { TRUE cnd <- sanitize_call(cnd)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) watcher$push(cnd)
}38: if (on_warning$silence) {evaluate::evaluate(...) invokeRestart("muffleWarning")
}39: }, error = function (cnd) 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, watcher$capture_plot_and_output() stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L cnd <- sanitize_call(cnd) else 2L watcher$push(cnd) }, output_handler = knit_handlers(options$render, options)) switch(on_error, continue = invokeRestart("eval_continue"),
stop = invokeRestart("eval_stop"), error = NULL)})40:
in_dir(input_dir(), expr)28:
eval(call)41:
29: in_input_dir(evaluate(code, envir = env, new_device = FALSE, eval(call) keep_warning = if (is.numeric(options$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$error else {30: if (options$error && options$include) with_handlers({ for (expr in tle$exprs) { 0L ev <- withVisible(eval(expr, envir)) else 2L watcher$capture_plot_and_output() }, output_handler = knit_handlers(options$render, options))) watcher$print_value(ev$value, ev$visible, envir)
42: }eng_r(options) TRUE}, handlers)
43: 31: doWithOneRestart(return(expr), restart)block_exec(params)
32: withOneRestart(expr, restarts[[1L]])44:
call_block(x)33:
withRestartList(expr, restarts[-nr])45:
process_group(group)34:
doWithOneRestart(return(expr), restart)46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 35: error = function(e) {withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) if (progress && is.function(pb$interrupt))
pb$interrupt()36: if (is_R_CMD_build() || is_R_CMD_check()) withRestartList(expr, restarts) error <<- format(e)
})37:
withRestarts(with_handlers({47: for (expr in tle$exprs) {with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), ev <- withVisible(eval(expr, envir)) error = function(e) { watcher$capture_plot_and_output() if (progress && is.function(pb$interrupt)) watcher$print_value(ev$value, ev$visible, envir) pb$interrupt() } if (is_R_CMD_build() || is_R_CMD_check()) TRUE error <<- format(e)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) }), list(rlang_trace_top_env = knit_global()))
38: evaluate::evaluate(...)
48: 39: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, error = function(e) { keep_message = if (is.numeric(options$message)) TRUE else options$message, if (progress && is.function(pb$interrupt)) stop_on_error = if (is.numeric(options$error)) options$error else { pb$interrupt() if (options$error && options$include) if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) 0L else 2L }), list(rlang_trace_top_env = knit_global())), function(loc) { }, output_handler = knit_handlers(options$render, options)) setwd(wd)
write_utf8(res, output %n% stdout())40: paste0("\nQuitting from ", loc, if (!is.null(error)) in_dir(input_dir(), expr) paste0("\n", rule(), error, "\n", rule()))
}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)41:
in_input_dir(evaluate(code, envir = env, new_device = FALSE, 49: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, process_file(text, output) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {50: if (options$error && options$include) knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) 0L
else 2L51: }, output_handler = knit_handlers(options$render, options)))rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)42:
eng_r(options)52:
vweave_rmarkdown(...)43:
53: block_exec(params)engine$weave(file, quiet = quiet, encoding = enc)
44: 54: call_block(x)
doTryCatch(return(expr), name, parentenv, handler)45:
55: process_group(group)tryCatchOne(expr, names, parentenv, handlers[[1L]])
46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 56: error = function(e) {tryCatchList(expr, classes, parentenv, handlers) if (progress && is.function(pb$interrupt))
pb$interrupt()57: if (is_R_CMD_build() || is_R_CMD_check()) tryCatch({ error <<- format(e) }) engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine)47: if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet)with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { output <- find_vignette_product(name, by = "texi2pdf", if (progress && is.function(pb$interrupt)) engine = engine) pb$interrupt() } if (is_R_CMD_build() || is_R_CMD_check()) }, error = function(e) { error <<- format(e) OK <<- FALSE }), list(rlang_trace_top_env = knit_global())) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))
})48:
xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 58: error = function(e) {tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", if (progress && is.function(pb$interrupt)) TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpTEYD5H/filed81d72d41328.rds") pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) An irrecoverable exception occurred. R is aborting now ...
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/RtmpTEYD5H/filed81d72d41328.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-10-27 14:34:15.257 R[60289:396282] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-27 14:34:16.980 R[60751:396862] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-10-27 14:34:29.541 R[63776:401284] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-27 14:34:31.591 R[63898:401560] 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