Last updated on 2026-10-09 02:54:18 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 25.02 | 544.81 | 569.83 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 20.25 | 376.74 | 396.99 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | 357.57 | 377.57 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 364.81 | 384.81 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 42.00 | 513.00 | 555.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 26.98 | 520.14 | 547.12 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 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 | 43.00 | 436.00 | 479.00 | 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 | 579.00 | 633.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpqX4kGh/file2fa759e2ee5.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpqX4kGh/file2fa759e2ee5.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
2026-10-09 12:05:16.595 R[15748:331120] 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]])
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment],
hazards = treatment_hazards, random_input = random_inputs$current_treatment, 8: end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)tryCatchList(expr, classes, parentenv, handlers)
2: 9: tryCatch({fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) hazards = treatment_hazards, random_input = random_inputs$current_treatment, } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) hazard_control = hazard_control, cutpoints = cutpoints,
N_total = N_total, lambda = lambda, lambda_time = lambda_time, 3: interim_look = interim_look, end_of_study = end_of_study, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, binary_imputation = predictive_binary_imputation, check_futility = check_futility) 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, 4: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)evaluate_interim_decision(data_interim = data_interim, look = i, attr(result, "arguments") <- NULL planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, if (inherits(result, "goldilocks_trial")) { active_followup = active_followup_at(data_total, look_time), attr(result$summary, "arguments") <- NULL 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, list(trial = as.integer(x), result = result) prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, }, error = function(error) { h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, message = conditionMessage(error)) method = method, binary_imputation = binary_imputation, check_futility = check_futility, }) Qn = Qn[i])
10: 5: FUN(X[[i]], ...)survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control,
cutpoints = cutpoints, N_total = N_total, lambda = lambda, 11: lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, lapply(X = S, FUN = FUN, ...) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method,
binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, 12: prop_loss = prop_loss, alternative = alternative, h0 = h0, doTryCatch(return(expr), name, parentenv, handler) Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute,
13: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, tryCatchOne(expr, names, parentenv, handlers[[1L]]) imputed_final = imputed_final, empty_interval = empty_interval,
return_trace = return_trace, prior_surv_final = prior_surv_final, 14: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)tryCatchList(expr, classes, parentenv, handlers)
6: 15: doTryCatch(return(expr), name, parentenv, handler)
tryCatch(expr, error = function(e) { 7: tryCatchOne(expr, names, parentenv, handlers[[1L]]) call <- conditionCall(e) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 8: call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L)tryCatchList(expr, classes, parentenv, handlers) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L 9: sm <- strsplit(conditionMessage(e), "\n")[[1L]]tryCatch({ w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (!is.null(trial_streams)) { if (is.na(w)) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, if (w > LONG) N_total = N_total, lambda = lambda, lambda_time = lambda_time, prefix <- paste0(prefix, "\n ") interim_look = interim_look, end_of_study = end_of_study, } prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, else prefix <- "Error : " binary_imputation = binary_imputation, block = block, msg <- paste0(prefix, conditionMessage(e), "\n") rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, .Internal(seterrmessage(msg[1L])) h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, if (!silent && isTRUE(getOption("show.error.messages"))) { N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, cat(msg, file = outFile) method = method, imputed_final = imputed_final, empty_interval = empty_interval, .Internal(printDeferredWarnings()) return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) } attr(result, "arguments") <- NULL invisible(structure(msg, class = "try-error", condition = e)) if (inherits(result, "goldilocks_trial")) {}) attr(result$summary, "arguments") <- NULL
16: }try(lapply(X = S, FUN = FUN, ...), silent = TRUE) list(trial = as.integer(x), result = result)
}, error = function(error) {17: list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) message = conditionMessage(error))
})18:
FUN(X[[i]], ...)10:
19: FUN(X[[i]], ...)
lapply(seq_len(cores), inner.do)
11: 20: lapply(X = S, FUN = FUN, ...)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)
12:
doTryCatch(return(expr), name, parentenv, handler)21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
13: 22: tryCatchOne(expr, names, parentenv, handlers[[1L]])
(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, 14: 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, tryCatchList(expr, classes, parentenv, handlers) 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1,
treatment = 1), prop_loss = 0, alternative = "greater", 15: h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, tryCatch(expr, error = function(e) { N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", call <- conditionCall(e) if (!is.null(call)) { imputed_final = FALSE, empty_interval = c("prior", "propagate", if (identical(call[[1L]], quote(doTryCatch))) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", call <- sys.call(-4L) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, dcall <- deparse(call, nlines = 1L) Qn = 1, rmst_tau = end_of_study) { prefix <- paste("Error in", dcall, ": ") Call <- match.call() LONG <- 75L Arguments <- capture_arguments(sim_trials, environment()) sm <- strsplit(conditionMessage(e), "\n")[[1L]] method <- normalize_analysis_method(method) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") Arguments$method <- method if (is.na(w)) empty_interval <- match.arg(empty_interval) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], binary_imputation <- match.arg(binary_imputation) type = "b") backend <- match.arg(backend) if (w > LONG) Arguments$empty_interval <- empty_interval prefix <- paste0(prefix, "\n ") Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend } requested_backend <- backend else prefix <- "Error : " caller_rng_kind <- RNGkind() msg <- paste0(prefix, conditionMessage(e), "\n") validate_positive_integer_scalar(N_trials, "N_trials") .Internal(seterrmessage(msg[1L])) single_arm <- is.null(hazard_control) if (!silent && isTRUE(getOption("show.error.messages"))) { if (!single_arm) { cat(msg, file = outFile) rand_ratio <- validate_randomization_args(N_total, block, .Internal(printDeferredWarnings()) rand_ratio, allocation_name = "rand_ratio") } Arguments$rand_ratio <- rand_ratio invisible(structure(msg, class = "try-error", condition = e))}) }
prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm)16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace")
validate_positive_integer_scalar(N_impute, "N_impute")17: validate_positive_integer_scalar(N_mcmc, "N_mcmc")sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) validate_single_probability(mc_conf_level, "mc_conf_level",
upper_open = TRUE)18: if (mc_conf_level <= 0.5) {FUN(X[[i]], ...) stop("'mc_conf_level' must be greater than 0.5 and less than 1")
}19: validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > lapply(seq_len(cores), inner.do) 0), N_impute = N_impute)
if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study")20: validate_rmst_args(rmst_tau, end_of_study, h0)mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, validate_analysis_configuration(method, alternative, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) is.null(hazard_control), imputed_final)
}21: validate_positive_integer_scalar(ncores, "ncores")pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) execution <- resolve_sim_execution(backend = backend, ncores = ncores,
N_trials = N_trials)22: backend <- execution$backend(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, workers <- execution$workers stream_seed <- NULL N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, if (!is.null(seed)) { end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, !is.finite(seed) || seed != floor(seed)) { treatment = 1), prop_loss = 0, alternative = "greater", stop("'seed' must be NULL or a single integer value") 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_kind <- RNGkind() imputed_final = FALSE, empty_interval = c("prior", "propagate", old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", inherits = FALSE) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", if (old_seed_exists) { "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, old_seed <- get(".Random.seed", envir = .GlobalEnv, Qn = 1, rmst_tau = end_of_study) inherits = FALSE){ } Call <- match.call() on.exit({ Arguments <- capture_arguments(sim_trials, environment()) do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { method <- normalize_analysis_method(method) Arguments$method <- method assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, empty_interval <- match.arg(empty_interval) inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) binary_imputation <- match.arg(binary_imputation) } }, add = TRUE) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) Arguments$binary_imputation <- binary_imputation } Arguments$backend <- backend requested_backend <- backend else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") size = 1L) single_arm <- is.null(hazard_control) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, trial_streams <- make_rng_streams(stream_seed, N_trials) } rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio else { trial_streams <- NULL } } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) caller_rng_kind[1] Arguments$prop_loss <- prop_loss } else { validate_logical_scalar(return_trace, "return_trace") "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") "explicit_preserve_caller" validate_single_probability(mc_conf_level, "mc_conf_level", } else if (backend == "psock") { upper_open = TRUE) if (mc_conf_level <= 0.5) { "caller_derived_psock" } else { stop("'mc_conf_level' must be greater than 0.5 and less than 1") "caller_state" } }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > survival_adapt_fn <- if (backend == "psock") { 0), N_impute = N_impute) make_psock_callable("survival_adapt") if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") } else { validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, survival_adapt is.null(hazard_control), imputed_final) } } survival_adapt_wrapper <- function(x) { validate_positive_integer_scalar(ncores, "ncores") tryCatch({ execution <- resolve_sim_execution(backend = backend, ncores = ncores, if (!is.null(trial_streams)) { N_trials = N_trials) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) backend <- execution$backend } workers <- execution$workers result <- survival_adapt_fn(hazard_treatment = hazard_treatment, stream_seed <- NULL hazard_control = hazard_control, cutpoints = cutpoints, if (!is.null(seed)) { N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || interim_look = interim_look, end_of_study = end_of_study, !is.finite(seed) || seed != floor(seed)) { prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, stop("'seed' must be NULL or a single integer value") } block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, alternative = alternative, h0 = h0, Fn = Fn, inherits = FALSE) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, if (old_seed_exists) { N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, old_seed <- get(".Random.seed", envir = .GlobalEnv, method = method, imputed_final = imputed_final, inherits = FALSE) empty_interval = empty_interval, return_trace = return_trace, } prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, on.exit({ rmst_tau = rmst_tau) attr(result, "arguments") <- NULL do.call(RNGkind, as.list(old_kind)) if (inherits(result, "goldilocks_trial")) { if (old_seed_exists) { attr(result$summary, "arguments") <- NULL assign(".Random.seed", old_seed, envir = .GlobalEnv) } } else if (exists(".Random.seed", envir = .GlobalEnv, list(trial = as.integer(x), result = result) inherits = FALSE)) { }, error = function(error) { rm(".Random.seed", envir = .GlobalEnv) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], } message = conditionMessage(error)) }, add = TRUE) }) stream_seed <- seed } trial_streams <- make_rng_streams(stream_seed, N_trials) trial_index <- seq_len(N_trials) } trial_results <- switch(backend, sequential = lapply(trial_index, else if (backend == "psock") { survival_adapt_wrapper), fork = pbmclapply(trial_index, stream_seed <- sample.int(.Machine$integer.max - 1L, survival_adapt_wrapper, mc.cores = workers), psock = { size = 1L) active_cluster <- make_sim_cluster(workers) trial_streams <- make_rng_streams(stream_seed, N_trials) on.exit(stop_sim_cluster(active_cluster), add = TRUE) } initialize_sim_cluster(active_cluster) else { run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) trial_streams <- NULL }) failed <- vapply(trial_results, function(x) is.null(x$result), } logical(1)) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] failed_results <- trial_results[failed] } else { "L'Ecuyer-CMRG" failures <- data.frame(trial = vapply(failed_results, `[[`, }, seed_policy = if (!is.null(seed)) { integer(1), "trial"), error_class = vapply(failed_results, "explicit_preserve_caller" `[[`, character(1), "error_class"), message = vapply(failed_results, } else if (backend == "psock") { `[[`, character(1), "message"), stringsAsFactors = FALSE) "caller_derived_psock" if (all(failed)) { } else { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), failures$message[1L])) error$failures <- failures stream_seed = stream_seed) class(error) <- c("goldilocks_all_trials_failed", class(error)) survival_adapt_fn <- if (backend == "psock") { stop(error) make_psock_callable("survival_adapt") } } else { successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") survival_adapt } if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) survival_adapt_wrapper <- function(x) { traces <- bind_rows(lapply(seq_along(successful_results), tryCatch({ function(i) { if (!is.null(trial_streams)) { trace <- successful_results[[i]]$trace assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) trial <- successful_trials[[i]]$trial } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, trace$trial <- rep.int(trial, nrow(trace)) hazard_control = hazard_control, cutpoints = cutpoints, trace[c("trial", setdiff(names(trace), "trial"))] N_total = N_total, lambda = lambda, lambda_time = lambda_time, })) out <- list(sims = sims, traces = traces, failures = failures, interim_look = interim_look, end_of_study = end_of_study, call = Call) 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, else { alternative = alternative, h0 = h0, Fn = Fn, sims <- bind_rows(successful_results) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, out <- list(sims = sims, failures = failures, call = Call) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, } empty_interval = empty_interval, return_trace = return_trace, attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, attr(result, "arguments") <- NULL end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], } list(trial = as.integer(x), result = result) "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata }, error = function(error) { attr(out, "arguments") <- Arguments list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, message = conditionMessage(error)) backend = backend, selection_reason = execution$reason, }) requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) } if (nrow(failures) > 0L) { trial_index <- seq_len(N_trials) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", trial_results <- switch(backend, sequential = lapply(trial_index, "for details.", call. = FALSE) survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { } return(out) active_cluster <- make_sim_cluster(workers)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, on.exit(stop_sim_cluster(active_cluster), add = TRUE) lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, initialize_sim_cluster(active_cluster) 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, failed <- vapply(trial_results, function(x) is.null(x$result), treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, logical(1)) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, failed_results <- trial_results[failed] 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", `[[`, character(1), "error_class"), message = vapply(failed_results, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 `[[`, character(1), "message"), stringsAsFactors = FALSE) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, if (all(failed)) { seed = 3425430)
error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L]))23: do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, error$failures <- failures hazard_control = hazard_control_week, N_trials = 20, ncores = 2, seed = 3425430))) class(error) <- c("goldilocks_all_trials_failed", class(error))
stop(error)24: }eval(expr, envir)
successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result")25: eval(expr, envir) if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results),
26: function(i) { trace <- successful_results[[i]]$tracewithVisible(eval(expr, envir)) trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace))
27: trace[c("trial", setdiff(names(trace), "trial"))] }))withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) out <- list(sims = sims, traces = traces, failures = failures, call = Call){ watcher$capture_plot_and_output() } if (on_message$capture) { else { watcher$push(cnd) 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, { if (getOption("warn") >= 2 || getOption("warn") < 0) { N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, return() end_of_study = end_of_study) } attr(out, "decision_design") <- attr(successful_results[[1]], watcher$capture_plot_and_output() "decision_design", exact = TRUE) if (on_warning$capture) { attr(out, "prior_design") <- attr(successful_results[[1]], cnd <- sanitize_call(cnd) "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata watcher$push(cnd) attr(out, "arguments") <- Arguments } attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, if (on_warning$silence) { invokeRestart("muffleWarning") backend = backend, selection_reason = execution$reason, } requested_ncores = as.integer(ncores), workers = as.integer(workers), }, error = function (cnd) tasks = as.integer(N_trials)){ if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) "for details.", call. = FALSE) } watcher$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, eval(call) prior_surv = c(1, 144.927536231884, 1, 144.927536231884,
1, 285.714285714286), block = 3, rand_ratio = c(control = 1, 29: 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, eval(call)
0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, 30: mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", with_handlers({ imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, for (expr in tle$exprs) { seed = 3425430) ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output()
23: watcher$print_value(ev$value, ev$visible, envir) }do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, TRUE hazard_control = hazard_control_week, N_trials = 20, ncores = 2, }, handlers) seed = 3425430)))
31: doWithOneRestart(return(expr), restart)24: eval(expr, envir)
32:
withOneRestart(expr, restarts[[1L]])25:
eval(expr, envir)
33: 26: withRestartList(expr, restarts[-nr])withVisible(eval(expr, envir))
34: 27: doWithOneRestart(return(expr), restart)withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) {
watcher$capture_plot_and_output() if (on_message$capture) {35: watcher$push(cnd) }withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) if (on_message$silence) {
invokeRestart("muffleMessage")36: }}, warning = function (cnd) withRestartList(expr, restarts)
{ if (getOption("warn") >= 2 || getOption("warn") < 0) {37: return()withRestarts(with_handlers({ } for (expr in tle$exprs) { watcher$capture_plot_and_output() ev <- withVisible(eval(expr, envir)) if (on_warning$capture) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$print_value(ev$value, ev$visible, envir) } watcher$push(cnd) TRUE }}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) if (on_warning$silence) {
invokeRestart("muffleWarning")38: }evaluate::evaluate(...)}, error = function (cnd) {
39: watcher$capture_plot_and_output() cnd <- sanitize_call(cnd)evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { stop = invokeRestart("eval_stop"), error = NULL) if (options$error && options$include) }) 0L
28: else 2L }, output_handler = knit_handlers(options$render, options))eval(call)
29: 40: eval(call)in_dir(input_dir(), expr)
30:
with_handlers({41: for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir))in_input_dir(evaluate(code, envir = env, new_device = FALSE, watcher$capture_plot_and_output() keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, watcher$print_value(ev$value, ev$visible, envir) keep_message = if (is.numeric(options$message)) TRUE else options$message, } stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) TRUE}, handlers) 0L
else 2L31: }, output_handler = knit_handlers(options$render, options)))doWithOneRestart(return(expr), restart)
42: 32: eng_r(options)withOneRestart(expr, restarts[[1L]])
43: 33: withRestartList(expr, restarts[-nr])block_exec(params)
34: 44: doWithOneRestart(return(expr), restart)call_block(x)
35:
withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])45:
process_group(group)36:
withRestartList(expr, restarts)46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 37: withRestarts(with_handlers({ error = function(e) { for (expr in tle$exprs) { if (progress && is.function(pb$interrupt)) ev <- withVisible(eval(expr, envir)) pb$interrupt() watcher$capture_plot_and_output() if (is_R_CMD_build() || is_R_CMD_check()) watcher$print_value(ev$value, ev$visible, envir) error <<- format(e) } }) TRUE
}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) { if (progress && is.function(pb$interrupt)) 38: pb$interrupt()evaluate::evaluate(...)
if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e)39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, }), list(rlang_trace_top_env = knit_global())) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {48: if (options$error && options$include) xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 0L error = function(e) { else 2L if (progress && is.function(pb$interrupt)) }, output_handler = knit_handlers(options$render, options))
pb$interrupt()40: if (is_R_CMD_build() || is_R_CMD_check()) in_dir(input_dir(), expr) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) {41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, setwd(wd) write_utf8(res, output %n% stdout()) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, paste0("\nQuitting from ", loc, if (!is.null(error)) stop_on_error = if (is.numeric(options$error)) options$error else { paste0("\n", rule(), error, "\n", rule())) if (options$error && options$include) }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) 0L
else 2L49: process_file(text, output) }, output_handler = knit_handlers(options$render, options)))
50: 42: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)eng_r(options)
51: 43: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), block_exec(params) output_dir = getwd(), ...)
44: 52: call_block(x)vweave_rmarkdown(...)
45: 53: engine$weave(file, quiet = quiet, encoding = enc)process_group(group)
54: 46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), doTryCatch(return(expr), name, parentenv, handler) error = function(e) {
if (progress && is.function(pb$interrupt)) 55: pb$interrupt()tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)56: })tryCatchList(expr, classes, parentenv, handlers)
47: 57: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), tryCatch({ error = function(e) { if (progress && is.function(pb$interrupt)) engine$weave(file, quiet = quiet, encoding = enc) pb$interrupt() setwd(startdir) if (is_R_CMD_build() || is_R_CMD_check()) output <- find_vignette_product(name, by = "weave", engine = engine) error <<- format(e) if (!have.makefile && vignette_is_tex(output)) { }), list(rlang_trace_top_env = knit_global())) texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf", 48: engine = engine)xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), } error = function(e) {}, error = function(e) { if (progress && is.function(pb$interrupt)) OK <<- FALSE pb$interrupt() message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", if (is_R_CMD_build() || is_R_CMD_check()) file, conditionMessage(e))) error <<- format(e)}) }), list(rlang_trace_top_env = knit_global())), function(loc) {
setwd(wd)58: write_utf8(res, output %n% stdout())tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", paste0("\nQuitting from ", loc, if (!is.null(error)) TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpqX4kGh/file2fa774894f05.rds") paste0("\n", rule(), error, "\n", rule()))
}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)An irrecoverable exception occurred. R is aborting now ...
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/RtmpqX4kGh/file2fa774894f05.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
2026-10-09 12:05:22.889 R[16154:332120] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-09 12:05:24.587 R[16227:332338] 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
2026-10-09 12:05:35.578 R[16534:333297] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-09 12:05:37.539 R[16615:333549] 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