Last updated on 2026-11-05 04:02:28 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 27.55 | 543.44 | 570.99 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.12 | 357.30 | 375.42 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 369.17 | 390.17 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 364.34 | 384.34 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 44.00 | 514.00 | 558.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | OK | ||||
| r-release-linux-x86_64 | 1.0.0 | 25.40 | 524.19 | 549.59 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 44.00 | 428.00 | 472.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 9.00 | 88.00 | 97.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 49.00 | 573.00 | 622.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpoXNwFB/filee16e871123e.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpoXNwFB/filee16e871123e.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-11-05 14:16:49.912 R[58647:399491] XType: Using static font registry.
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1,
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment], 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, hazards = treatment_hazards, random_input = random_inputs$current_treatment, treatment = 1), prop_loss = 0, alternative = "greater", end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500,
N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", 2: imputed_final = FALSE, empty_interval = c("prior", "propagate", fill_predictive_imputations(imputations = current, rows = current_treatment, "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study)
{ 3: Call <- match.call()impute_predictive_draws(data_in = data_interim, hazards = post_lambda, Arguments <- capture_arguments(sim_trials, environment()) end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, method <- normalize_analysis_method(method) Arguments$method <- method binary_imputation = predictive_binary_imputation, check_futility = check_futility) empty_interval <- match.arg(empty_interval)
binary_imputation <- match.arg(binary_imputation) 4: backend <- match.arg(backend)evaluate_interim_decision(data_interim = data_interim, look = i, Arguments$empty_interval <- empty_interval planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend active_followup = active_followup_at(data_total, look_time), requested_backend <- backend end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, caller_rng_kind <- RNGkind() h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, validate_positive_integer_scalar(N_trials, "N_trials") N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, single_arm <- is.null(hazard_control) method = method, binary_imputation = binary_imputation, check_futility = check_futility, if (!single_arm) { Qn = Qn[i]) rand_ratio <- validate_randomization_args(N_total, block,
rand_ratio, allocation_name = "rand_ratio") 5: Arguments$rand_ratio <- rand_ratiosurvival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, } cutpoints = cutpoints, N_total = N_total, lambda = lambda, prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, Arguments$prop_loss <- prop_loss prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, validate_logical_scalar(return_trace, "return_trace") binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, validate_positive_integer_scalar(N_impute, "N_impute") prop_loss = prop_loss, alternative = alternative, h0 = h0, validate_positive_integer_scalar(N_mcmc, "N_mcmc") Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, validate_single_probability(mc_conf_level, "mc_conf_level", imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, upper_open = TRUE) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) if (mc_conf_level <= 0.5) {
stop("'mc_conf_level' must be greater than 0.5 and less than 1") 6: }doTryCatch(return(expr), name, parentenv, handler) validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss >
0), N_impute = N_impute) 7: if (identical(method, "rmst")) {tryCatchOne(expr, names, parentenv, handlers[[1L]]) validate_endpoint_time(end_of_study, cutpoints, "end_of_study")
validate_rmst_args(rmst_tau, end_of_study, h0) 8: validate_analysis_configuration(method, alternative, tryCatchList(expr, classes, parentenv, handlers) is.null(hazard_control), imputed_final)
} 9: validate_positive_integer_scalar(ncores, "ncores")tryCatch({ execution <- resolve_sim_execution(backend = backend, ncores = ncores, if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) N_trials = N_trials) } backend <- execution$backend result <- survival_adapt_fn(hazard_treatment = hazard_treatment, workers <- execution$workers hazard_control = hazard_control, cutpoints = cutpoints, stream_seed <- NULL N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (!is.null(seed)) { interim_look = interim_look, end_of_study = end_of_study, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, !is.finite(seed) || seed != floor(seed)) { binary_imputation = binary_imputation, block = block, stop("'seed' must be NULL or a single integer value") rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, } h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, old_kind <- RNGkind() N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, method = method, imputed_final = imputed_final, empty_interval = empty_interval, inherits = FALSE) return_trace = return_trace, prior_surv_final = prior_surv_final, if (old_seed_exists) { generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) old_seed <- get(".Random.seed", envir = .GlobalEnv, attr(result, "arguments") <- NULL inherits = FALSE) if (inherits(result, "goldilocks_trial")) { } attr(result$summary, "arguments") <- NULL on.exit({ } do.call(RNGkind, as.list(old_kind)) list(trial = as.integer(x), result = result) if (old_seed_exists) {}, error = function(error) { assign(".Random.seed", old_seed, envir = .GlobalEnv) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], } else if (exists(".Random.seed", envir = .GlobalEnv, message = conditionMessage(error))}) inherits = FALSE)) {
rm(".Random.seed", envir = .GlobalEnv)10: }FUN(X[[i]], ...) }, add = TRUE)
stream_seed <- seed11: trial_streams <- make_rng_streams(stream_seed, N_trials)lapply(X = S, FUN = FUN, ...) }
else if (backend == "psock") {12: stream_seed <- sample.int(.Machine$integer.max - 1L, doTryCatch(return(expr), name, parentenv, handler) size = 1L)
trial_streams <- make_rng_streams(stream_seed, N_trials)13: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) else {
trial_streams <- NULL14: }tryCatchList(expr, classes, parentenv, handlers) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {
caller_rng_kind[1]15: } else {tryCatch(expr, error = function(e) { "L'Ecuyer-CMRG" call <- conditionCall(e) }, seed_policy = if (!is.null(seed)) { if (!is.null(call)) { "explicit_preserve_caller" if (identical(call[[1L]], quote(doTryCatch))) } else if (backend == "psock") { call <- sys.call(-4L) "caller_derived_psock" dcall <- deparse(call, nlines = 1L) } else { prefix <- paste("Error in", dcall, ": ") "caller_state" LONG <- 75L }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), sm <- strsplit(conditionMessage(e), "\n")[[1L]] stream_seed = stream_seed) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") survival_adapt_fn <- if (backend == "psock") { if (is.na(w)) make_psock_callable("survival_adapt") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") else { if (w > LONG) survival_adapt prefix <- paste0(prefix, "\n ") } } survival_adapt_wrapper <- function(x) { else prefix <- "Error : " tryCatch({ msg <- paste0(prefix, conditionMessage(e), "\n") .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, 16: bin_method = bin_method, binary_imputation = binary_imputation, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss,
alternative = alternative, h0 = h0, Fn = Fn, 17: Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level,
method = method, imputed_final = imputed_final, 18: empty_interval = empty_interval, return_trace = return_trace, FUN(X[[i]], ...) prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints,
rmst_tau = rmst_tau)19: attr(result, "arguments") <- NULLlapply(seq_len(cores), inner.do) if (inherits(result, "goldilocks_trial")) {
attr(result$summary, "arguments") <- NULL20: }mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, list(trial = as.integer(x), result = result) mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) }, error = function(error) {
21: list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) message = conditionMessage(error))
})22: }(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, trial_index <- seq_len(N_trials) N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, trial_results <- switch(backend, sequential = lapply(trial_index, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, survival_adapt_wrapper), fork = pbmclapply(trial_index, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, survival_adapt_wrapper, mc.cores = workers), psock = { treatment = 1), prop_loss = 0, alternative = "greater", active_cluster <- make_sim_cluster(workers) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, on.exit(stop_sim_cluster(active_cluster), add = TRUE) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", initialize_sim_cluster(active_cluster) imputed_final = FALSE, empty_interval = c("prior", "propagate", run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", }) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", failed <- vapply(trial_results, function(x) is.null(x$result), "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, logical(1)) Qn = 1, rmst_tau = end_of_study) failed_results <- trial_results[failed]{ failures <- data.frame(trial = vapply(failed_results, `[[`, Call <- match.call() integer(1), "trial"), error_class = vapply(failed_results, Arguments <- capture_arguments(sim_trials, environment()) `[[`, character(1), "error_class"), message = vapply(failed_results, method <- normalize_analysis_method(method) `[[`, character(1), "message"), stringsAsFactors = FALSE) Arguments$method <- method if (all(failed)) { empty_interval <- match.arg(empty_interval) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", binary_imputation <- match.arg(binary_imputation) failures$message[1L])) backend <- match.arg(backend) error$failures <- failures Arguments$empty_interval <- empty_interval class(error) <- c("goldilocks_all_trials_failed", class(error)) Arguments$binary_imputation <- binary_imputation stop(error) Arguments$backend <- backend } requested_backend <- backend successful_trials <- trial_results[!failed] caller_rng_kind <- RNGkind() successful_results <- lapply(successful_trials, `[[`, "result") validate_positive_integer_scalar(N_trials, "N_trials") if (return_trace) { single_arm <- is.null(hazard_control) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) if (!single_arm) { traces <- bind_rows(lapply(seq_along(successful_results), rand_ratio <- validate_randomization_args(N_total, block, function(i) { rand_ratio, allocation_name = "rand_ratio") trace <- successful_results[[i]]$trace Arguments$rand_ratio <- rand_ratio trial <- successful_trials[[i]]$trial } trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) })) Arguments$prop_loss <- prop_loss out <- list(sims = sims, traces = traces, failures = failures, validate_logical_scalar(return_trace, "return_trace") call = Call) validate_positive_integer_scalar(N_impute, "N_impute") } else { validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", sims <- bind_rows(successful_results) upper_open = TRUE) out <- list(sims = sims, failures = failures, call = Call) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") } } attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > end_of_study = end_of_study) 0), N_impute = N_impute) attr(out, "decision_design") <- attr(successful_results[[1]], if (identical(method, "rmst")) { "decision_design", exact = TRUE) validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) validate_analysis_configuration(method, alternative, attr(out, "rng_metadata") <- rng_metadata is.null(hazard_control), imputed_final) attr(out, "arguments") <- Arguments } attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, validate_positive_integer_scalar(ncores, "ncores") backend = backend, selection_reason = execution$reason, execution <- resolve_sim_execution(backend = backend, ncores = ncores, requested_ncores = as.integer(ncores), workers = as.integer(workers), N_trials = N_trials) tasks = as.integer(N_trials)) backend <- execution$backend if (nrow(failures) > 0L) { workers <- execution$workers warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", stream_seed <- NULL "for details.", call. = FALSE) if (!is.null(seed)) { } if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || return(out) !is.finite(seed) || seed != floor(seed)) {})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, stop("'seed' must be NULL or a single integer value") 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, old_kind <- RNGkind() 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, inherits = FALSE) treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, if (old_seed_exists) { Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, old_seed <- get(".Random.seed", envir = .GlobalEnv, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, inherits = FALSE) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", } imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 on.exit({ ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, do.call(RNGkind, as.list(old_kind)) seed = 3425430) 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)
}23: }, add = TRUE)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, stream_seed <- seed hazard_control = hazard_control_week, N_trials = 20, ncores = 2, trial_streams <- make_rng_streams(stream_seed, N_trials) seed = 3425430))) }
else if (backend == "psock") {24: stream_seed <- sample.int(.Machine$integer.max - 1L, eval(expr, envir) size = 1L)
trial_streams <- make_rng_streams(stream_seed, N_trials)25: }eval(expr, envir) else {
trial_streams <- NULL26: }withVisible(eval(expr, envir)) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {
caller_rng_kind[1]27: } else {withCallingHandlers(code, error = function (e) "L'Ecuyer-CMRG"rlang::entrace(e), message = function (cnd) }, seed_policy = if (!is.null(seed)) {{ "explicit_preserve_caller" watcher$capture_plot_and_output() } else if (backend == "psock") { if (on_message$capture) { "caller_derived_psock" watcher$push(cnd) } else { } "caller_state" if (on_message$silence) { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), invokeRestart("muffleMessage") stream_seed = stream_seed) } survival_adapt_fn <- if (backend == "psock") {}, warning = function (cnd) make_psock_callable("survival_adapt"){ } if (getOption("warn") >= 2 || getOption("warn") < 0) { else { return() survival_adapt } } survival_adapt_wrapper <- function(x) { watcher$capture_plot_and_output() tryCatch({ if (!is.null(trial_streams)) { if (on_warning$capture) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } cnd <- sanitize_call(cnd) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, watcher$push(cnd) hazard_control = hazard_control, cutpoints = cutpoints, } N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (on_warning$silence) { interim_look = interim_look, end_of_study = end_of_study, invokeRestart("muffleWarning") } prior_surv = prior_surv, prior_bin = prior_bin, }, error = function (cnd) bin_method = bin_method, binary_imputation = binary_imputation, { block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, watcher$capture_plot_and_output() alternative = alternative, h0 = h0, Fn = Fn, cnd <- sanitize_call(cnd) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, watcher$push(cnd) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, switch(on_error, continue = invokeRestart("eval_continue"), method = method, imputed_final = imputed_final, stop = invokeRestart("eval_stop"), error = NULL) empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, }) rmst_tau = rmst_tau)
attr(result, "arguments") <- NULL28: if (inherits(result, "goldilocks_trial")) {eval(call) attr(result$summary, "arguments") <- NULL
}29: list(trial = as.integer(x), result = result)eval(call) }, error = function(error) {
list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], 30: message = conditionMessage(error))with_handlers({ }) for (expr in tle$exprs) { } ev <- withVisible(eval(expr, envir)) trial_index <- seq_len(N_trials) watcher$capture_plot_and_output() trial_results <- switch(backend, sequential = lapply(trial_index, watcher$print_value(ev$value, ev$visible, envir) survival_adapt_wrapper), fork = pbmclapply(trial_index, } survival_adapt_wrapper, mc.cores = workers), psock = { TRUE active_cluster <- make_sim_cluster(workers)}, handlers) on.exit(stop_sim_cluster(active_cluster), add = TRUE)
initialize_sim_cluster(active_cluster)31: run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper)doWithOneRestart(return(expr), restart) })
failed <- vapply(trial_results, function(x) is.null(x$result), 32: logical(1))withOneRestart(expr, restarts[[1L]]) failed_results <- trial_results[failed]
failures <- data.frame(trial = vapply(failed_results, `[[`, 33: integer(1), "trial"), error_class = vapply(failed_results, withRestartList(expr, restarts[-nr]) `[[`, character(1), "error_class"), message = vapply(failed_results,
`[[`, character(1), "message"), stringsAsFactors = FALSE)34: if (all(failed)) {doWithOneRestart(return(expr), restart) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ",
failures$message[1L]))35: error$failures <- failureswithOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) class(error) <- c("goldilocks_all_trials_failed", class(error))
stop(error)36: }withRestartList(expr, restarts) successful_trials <- trial_results[!failed]
successful_results <- lapply(successful_trials, `[[`, "result")37: if (return_trace) {withRestarts(with_handlers({ sims <- bind_rows(lapply(successful_results, function(x) x$summary)) for (expr in tle$exprs) { traces <- bind_rows(lapply(seq_along(successful_results), ev <- withVisible(eval(expr, envir)) function(i) { watcher$capture_plot_and_output() trace <- successful_results[[i]]$trace watcher$print_value(ev$value, ev$visible, envir) trial <- successful_trials[[i]]$trial } trace$trial <- rep.int(trial, nrow(trace)) TRUE trace[c("trial", setdiff(names(trace), "trial"))]}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) }))
out <- list(sims = sims, traces = traces, failures = failures, 38: call = Call)evaluate::evaluate(...) }
else {39: sims <- bind_rows(successful_results)evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, out <- list(sims = sims, failures = failures, call = Call) keep_message = if (is.numeric(options$message)) TRUE else options$message, } stop_on_error = if (is.numeric(options$error)) options$error else { attr(out$sims, "arguments") <- NULL if (options$error && options$include) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, 0L N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, else 2L end_of_study = end_of_study) }, output_handler = knit_handlers(options$render, options)) attr(out, "decision_design") <- attr(successful_results[[1]],
"decision_design", exact = TRUE)40: attr(out, "prior_design") <- attr(successful_results[[1]], in_dir(input_dir(), expr) "prior_design", exact = TRUE)
attr(out, "rng_metadata") <- rng_metadata41: attr(out, "arguments") <- Argumentsin_input_dir(evaluate(code, envir = env, new_device = FALSE, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, backend = backend, selection_reason = execution$reason, keep_message = if (is.numeric(options$message)) TRUE else options$message, requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) stop_on_error = if (is.numeric(options$error)) options$error else { if (nrow(failures) > 0L) { if (options$error && options$include) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", 0L "for details.", call. = FALSE) else 2L } }, output_handler = knit_handlers(options$render, options))) return(out)
})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, 42: lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, eng_r(options) 8.66666666666667, 13, 17.3333333333333, 21.6666666666667,
26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, 43: prior_surv = c(1, 144.927536231884, 1, 144.927536231884, block_exec(params) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1,
treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, 44: Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, call_block(x) 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", 45: imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168process_group(group) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2,
seed = 3425430)46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 23: error = function(e) {do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, if (progress && is.function(pb$interrupt)) hazard_control = hazard_control_week, N_trials = 20, ncores = 2, pb$interrupt() seed = 3425430))) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)24: eval(expr, envir) })
25: 47: eval(expr, envir)with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {26: if (progress && is.function(pb$interrupt)) withVisible(eval(expr, envir)) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 27: error <<- format(e)withCallingHandlers(code, error = function (e) }), list(rlang_trace_top_env = knit_global()))rlang::entrace(e), message = function (cnd)
{48: watcher$capture_plot_and_output()xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (on_message$capture) { error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() watcher$push(cnd) if (is_R_CMD_build() || is_R_CMD_check()) } if (on_message$silence) { error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { invokeRestart("muffleMessage") setwd(wd) } write_utf8(res, output %n% stdout())}, warning = function (cnd) paste0("\nQuitting from ", loc, if (!is.null(error)) { paste0("\n", rule(), error, "\n", rule())) if (getOption("warn") >= 2 || getOption("warn") < 0) {}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) return()
}49: watcher$capture_plot_and_output()process_file(text, output) if (on_warning$capture) {
cnd <- sanitize_call(cnd)50: watcher$push(cnd)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) }
if (on_warning$silence) {51: invokeRestart("muffleWarning")rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), } output_dir = getwd(), ...)}, error = function (cnd)
{52: watcher$capture_plot_and_output()vweave_rmarkdown(...) cnd <- sanitize_call(cnd)
watcher$push(cnd)53: switch(on_error, continue = invokeRestart("eval_continue"), engine$weave(file, quiet = quiet, encoding = enc) stop = invokeRestart("eval_stop"), error = NULL)
})54:
doTryCatch(return(expr), name, parentenv, handler)28:
eval(call)55:
tryCatchOne(expr, names, parentenv, handlers[[1L]])29:
eval(call)56:
tryCatchList(expr, classes, parentenv, handlers)30:
with_handlers({57: for (expr in tle$exprs) {tryCatch({ ev <- withVisible(eval(expr, envir)) engine$weave(file, quiet = quiet, encoding = enc) watcher$capture_plot_and_output() setwd(startdir) watcher$print_value(ev$value, ev$visible, envir) output <- find_vignette_product(name, by = "weave", engine = engine) } if (!have.makefile && vignette_is_tex(output)) { TRUE texi2pdf(file = output, clean = FALSE, quiet = quiet)}, handlers) output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)31: }doWithOneRestart(return(expr), restart)}, error = function(e) {
OK <<- FALSE32: message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", withOneRestart(expr, restarts[[1L]]) file, conditionMessage(e)))
})33:
withRestartList(expr, restarts[-nr])58:
tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 34: TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpoXNwFB/filee16e70cd094a.rds")doWithOneRestart(return(expr), restart)
An irrecoverable exception occurred. R is aborting now ...
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpoXNwFB/filee16e70cd094a.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-11-05 14:16:56.262 R[59265:400931] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-11-05 14:16:57.978 R[59386:401172] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-11-05 14:17:09.313 R[60237:403066] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-11-05 14:17:11.235 R[60393:403505] 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