Last updated on 2026-09-26 18:51:35 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 28.47 | 541.84 | 570.31 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.09 | 369.81 | 387.90 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 352.13 | 373.13 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 358.85 | 378.85 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 42.00 | 513.00 | 555.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 27.41 | 519.49 | 546.90 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.16 | 518.28 | 543.44 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 47.00 | 437.00 | 484.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 7.00 | 89.00 | 96.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 56.00 | 579.00 | 635.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpiYvqlt/file8bdc655cfacd.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpiYvqlt/file8bdc655cfacd.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-09-27 03:53:03.507 R[37437:355533] XType: Using static font registry.
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
Traceback:
1: 3: impute_predictive_arm(time = data_in$time[current_treatment], impute_predictive_draws(data_in = data_interim, hazards = post_lambda, hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
binary_imputation = predictive_binary_imputation, check_futility = check_futility) 2:
fill_predictive_imputations(imputations = current, rows = current_treatment, 4: values = impute_predictive_arm(time = data_in$time[current_treatment], evaluate_interim_decision(data_interim = data_interim, look = i, hazards = treatment_hazards, random_input = random_inputs$current_treatment, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) active_followup = active_followup_at(data_total, look_time),
end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, 3: single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, binary_imputation = predictive_binary_imputation, check_futility = check_futility) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval,
method = method, binary_imputation = binary_imputation, check_futility = check_futility, 4: Qn = Qn[i])evaluate_interim_decision(data_interim = data_interim, look = i,
planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, 5: active_followup = active_followup_at(data_total, look_time), survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, cutpoints = cutpoints, N_total = N_total, lambda = lambda, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, prop_loss = prop_loss, alternative = alternative, h0 = h0, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, Qn = Qn[i]) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method,
imputed_final = imputed_final, empty_interval = empty_interval, 5: return_trace = return_trace, prior_surv_final = prior_surv_final, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) cutpoints = cutpoints, N_total = N_total, lambda = lambda,
lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, 6: prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, doTryCatch(return(expr), name, parentenv, handler) binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio,
prop_loss = prop_loss, alternative = alternative, h0 = h0, 7: Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, tryCatchOne(expr, names, parentenv, handlers[[1L]]) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method,
imputed_final = imputed_final, empty_interval = empty_interval, 8: return_trace = return_trace, prior_surv_final = prior_surv_final, tryCatchList(expr, classes, parentenv, handlers) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
9: 6: tryCatch({doTryCatch(return(expr), name, parentenv, handler) if (!is.null(trial_streams)) {
assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) 7: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) result <- survival_adapt_fn(hazard_treatment = hazard_treatment,
hazard_control = hazard_control, cutpoints = cutpoints, 8: N_total = N_total, lambda = lambda, lambda_time = lambda_time, tryCatchList(expr, classes, parentenv, handlers) interim_look = interim_look, end_of_study = end_of_study,
prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, 9: binary_imputation = binary_imputation, block = block, tryCatch({ rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, if (!is.null(trial_streams)) { h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, } method = method, imputed_final = imputed_final, empty_interval = empty_interval, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, return_trace = return_trace, prior_surv_final = prior_surv_final, hazard_control = hazard_control, cutpoints = cutpoints, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) N_total = N_total, lambda = lambda, lambda_time = lambda_time, attr(result, "arguments") <- NULL interim_look = interim_look, end_of_study = end_of_study, if (inherits(result, "goldilocks_trial")) { prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, attr(result$summary, "arguments") <- NULL binary_imputation = binary_imputation, block = block, } rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, list(trial = as.integer(x), result = result) h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, }, error = function(error) { N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], method = method, imputed_final = imputed_final, empty_interval = empty_interval, message = conditionMessage(error)) return_trace = return_trace, prior_surv_final = prior_surv_final, }) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
attr(result, "arguments") <- NULL10: if (inherits(result, "goldilocks_trial")) {FUN(X[[i]], ...) attr(result$summary, "arguments") <- NULL
11: }lapply(X = S, FUN = FUN, ...) list(trial = as.integer(x), result = result)
}, error = function(error) {12: list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], doTryCatch(return(expr), name, parentenv, handler) message = conditionMessage(error))
})13:
tryCatchOne(expr, names, parentenv, handlers[[1L]])
10: 14: FUN(X[[i]], ...)tryCatchList(expr, classes, parentenv, handlers)
11: 15: lapply(X = S, FUN = FUN, ...)tryCatch(expr, error = function(e) {
call <- conditionCall(e)12: if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) doTryCatch(return(expr), name, parentenv, handler)
call <- sys.call(-4L)13: dcall <- deparse(call, nlines = 1L)tryCatchOne(expr, names, parentenv, handlers[[1L]]) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L14: sm <- strsplit(conditionMessage(e), "\n")[[1L]]tryCatchList(expr, classes, parentenv, handlers) 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 ")15: }tryCatch(expr, error = function(e) { else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") call <- conditionCall(e) .Internal(seterrmessage(msg[1L])) if (!is.null(call)) { if (!silent && isTRUE(getOption("show.error.messages"))) { if (identical(call[[1L]], quote(doTryCatch))) cat(msg, file = outFile) call <- sys.call(-4L) .Internal(printDeferredWarnings()) dcall <- deparse(call, nlines = 1L) } prefix <- paste("Error in", dcall, ": ") invisible(structure(msg, class = "try-error", condition = e)) LONG <- 75L}) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")16: if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], try(lapply(X = S, FUN = FUN, ...), silent = TRUE) type = "b")
if (w > LONG) 17: prefix <- paste0(prefix, "\n ")sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) }
else prefix <- "Error : "18: FUN(X[[i]], ...) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))19: if (!silent && isTRUE(getOption("show.error.messages"))) {lapply(seq_len(cores), inner.do) cat(msg, file = outFile)
.Internal(printDeferredWarnings())20: }mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, invisible(structure(msg, class = "try-error", condition = e)) mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)21:
pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)17:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))22:
(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, 18: N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, FUN(X[[i]], ...) end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1,
19: 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, lapply(seq_len(cores), inner.do) treatment = 1), prop_loss = 0, alternative = "greater",
h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, 20: N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, imputed_final = FALSE, empty_interval = c("prior", "propagate", mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto",
"fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", 21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints,
Qn = 1, rmst_tau = end_of_study) 22: {(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, Call <- match.call() N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, Arguments <- capture_arguments(sim_trials, environment()) end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, method <- normalize_analysis_method(method) 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, Arguments$method <- method treatment = 1), prop_loss = 0, alternative = "greater", empty_interval <- match.arg(empty_interval) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, binary_imputation <- match.arg(binary_imputation) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", backend <- match.arg(backend) imputed_final = FALSE, empty_interval = c("prior", "propagate", Arguments$empty_interval <- empty_interval "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", Arguments$binary_imputation <- binary_imputation "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", Arguments$backend <- backend requested_backend <- backend "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, caller_rng_kind <- RNGkind() Qn = 1, rmst_tau = end_of_study) validate_positive_integer_scalar(N_trials, "N_trials"){ single_arm <- is.null(hazard_control) Call <- match.call() if (!single_arm) { Arguments <- capture_arguments(sim_trials, environment()) rand_ratio <- validate_randomization_args(N_total, block, method <- normalize_analysis_method(method) rand_ratio, allocation_name = "rand_ratio") Arguments$method <- method Arguments$rand_ratio <- rand_ratio empty_interval <- match.arg(empty_interval) } binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss Arguments$empty_interval <- empty_interval validate_logical_scalar(return_trace, "return_trace") Arguments$binary_imputation <- binary_imputation validate_positive_integer_scalar(N_impute, "N_impute") Arguments$backend <- backend validate_positive_integer_scalar(N_mcmc, "N_mcmc") requested_backend <- backend validate_single_probability(mc_conf_level, "mc_conf_level", caller_rng_kind <- RNGkind() upper_open = TRUE) validate_positive_integer_scalar(N_trials, "N_trials") if (mc_conf_level <= 0.5) { single_arm <- is.null(hazard_control) stop("'mc_conf_level' must be greater than 0.5 and less than 1") if (!single_arm) { } rand_ratio <- validate_randomization_args(N_total, block, validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) rand_ratio, allocation_name = "rand_ratio") if (identical(method, "rmst")) { Arguments$rand_ratio <- rand_ratio validate_endpoint_time(end_of_study, cutpoints, "end_of_study") } validate_rmst_args(rmst_tau, end_of_study, h0) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) validate_analysis_configuration(method, alternative, Arguments$prop_loss <- prop_loss is.null(hazard_control), imputed_final) validate_logical_scalar(return_trace, "return_trace") } validate_positive_integer_scalar(ncores, "ncores") validate_positive_integer_scalar(N_impute, "N_impute") execution <- resolve_sim_execution(backend = backend, ncores = ncores, validate_positive_integer_scalar(N_mcmc, "N_mcmc") N_trials = N_trials) validate_single_probability(mc_conf_level, "mc_conf_level", backend <- execution$backend upper_open = TRUE) workers <- execution$workers if (mc_conf_level <= 0.5) { stream_seed <- NULL stop("'mc_conf_level' must be greater than 0.5 and less than 1") if (!is.null(seed)) { } if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > !is.finite(seed) || seed != floor(seed)) { 0), N_impute = N_impute) stop("'seed' must be NULL or a single integer value") if (identical(method, "rmst")) { } validate_endpoint_time(end_of_study, cutpoints, "end_of_study") old_kind <- RNGkind() validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, is.null(hazard_control), imputed_final) inherits = FALSE) } if (old_seed_exists) { validate_positive_integer_scalar(ncores, "ncores") old_seed <- get(".Random.seed", envir = .GlobalEnv, execution <- resolve_sim_execution(backend = backend, ncores = ncores, inherits = FALSE) N_trials = N_trials) } backend <- execution$backend on.exit({ workers <- execution$workers do.call(RNGkind, as.list(old_kind)) stream_seed <- NULL if (old_seed_exists) { if (!is.null(seed)) { assign(".Random.seed", old_seed, envir = .GlobalEnv) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || } else if (exists(".Random.seed", envir = .GlobalEnv, !is.finite(seed) || seed != floor(seed)) { inherits = FALSE)) { stop("'seed' must be NULL or a single integer value") rm(".Random.seed", envir = .GlobalEnv) } } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, }, add = TRUE) inherits = FALSE) stream_seed <- seed if (old_seed_exists) { trial_streams <- make_rng_streams(stream_seed, N_trials) old_seed <- get(".Random.seed", envir = .GlobalEnv, } inherits = FALSE) else if (backend == "psock") { } stream_seed <- sample.int(.Machine$integer.max - 1L, on.exit({ size = 1L) do.call(RNGkind, as.list(old_kind)) trial_streams <- make_rng_streams(stream_seed, N_trials) if (old_seed_exists) { } assign(".Random.seed", old_seed, envir = .GlobalEnv) else { } else if (exists(".Random.seed", envir = .GlobalEnv, trial_streams <- NULL inherits = FALSE)) { } rm(".Random.seed", envir = .GlobalEnv) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { } caller_rng_kind[1] }, add = TRUE) } else { stream_seed <- seed "L'Ecuyer-CMRG" trial_streams <- make_rng_streams(stream_seed, N_trials) }, seed_policy = if (!is.null(seed)) { } "explicit_preserve_caller" else if (backend == "psock") { } else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, "caller_derived_psock" size = 1L) } else { "caller_state" trial_streams <- make_rng_streams(stream_seed, N_trials) }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), } else { stream_seed = stream_seed) trial_streams <- NULL survival_adapt_fn <- if (backend == "psock") { } make_psock_callable("survival_adapt") rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { } caller_rng_kind[1] else { } else { survival_adapt "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { } "explicit_preserve_caller" survival_adapt_wrapper <- function(x) { } else if (backend == "psock") { tryCatch({ "caller_derived_psock" if (!is.null(trial_streams)) { } else { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) "caller_state" } }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), result <- survival_adapt_fn(hazard_treatment = hazard_treatment, stream_seed = stream_seed) hazard_control = hazard_control, cutpoints = cutpoints, survival_adapt_fn <- if (backend == "psock") { N_total = N_total, lambda = lambda, lambda_time = lambda_time, make_psock_callable("survival_adapt") interim_look = interim_look, end_of_study = end_of_study, } prior_surv = prior_surv, prior_bin = prior_bin, else { bin_method = bin_method, binary_imputation = binary_imputation, survival_adapt block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, } alternative = alternative, h0 = h0, Fn = Fn, survival_adapt_wrapper <- function(x) { Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, tryCatch({ N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, if (!is.null(trial_streams)) { method = method, imputed_final = imputed_final, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) empty_interval = empty_interval, return_trace = return_trace, } prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, rmst_tau = rmst_tau) hazard_control = hazard_control, cutpoints = cutpoints, attr(result, "arguments") <- NULL N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, if (inherits(result, "goldilocks_trial")) { prior_surv = prior_surv, prior_bin = prior_bin, attr(result$summary, "arguments") <- NULL bin_method = bin_method, binary_imputation = binary_imputation, } list(trial = as.integer(x), result = result) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, }, error = function(error) { alternative = alternative, h0 = h0, Fn = Fn, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, message = conditionMessage(error)) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }) method = method, imputed_final = imputed_final, } empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, trial_index <- seq_len(N_trials) rmst_tau = rmst_tau) trial_results <- switch(backend, sequential = lapply(trial_index, attr(result, "arguments") <- NULL survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { if (inherits(result, "goldilocks_trial")) { active_cluster <- make_sim_cluster(workers) attr(result$summary, "arguments") <- NULL on.exit(stop_sim_cluster(active_cluster), add = TRUE) } initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) list(trial = as.integer(x), result = result) }) }, error = function(error) { failed <- vapply(trial_results, function(x) is.null(x$result), list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) logical(1)) }) failed_results <- trial_results[failed] } trial_index <- seq_len(N_trials) failures <- data.frame(trial = vapply(failed_results, `[[`, trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, integer(1), "trial"), error_class = vapply(failed_results, survival_adapt_wrapper, mc.cores = workers), psock = { `[[`, character(1), "error_class"), message = vapply(failed_results, active_cluster <- make_sim_cluster(workers) `[[`, character(1), "message"), stringsAsFactors = FALSE) on.exit(stop_sim_cluster(active_cluster), add = TRUE) if (all(failed)) { initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", }) failures$message[1L])) failed <- vapply(trial_results, function(x) is.null(x$result), error$failures <- failures logical(1)) class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) failed_results <- trial_results[failed] } failures <- data.frame(trial = vapply(failed_results, `[[`, successful_trials <- trial_results[!failed] integer(1), "trial"), error_class = vapply(failed_results, successful_results <- lapply(successful_trials, `[[`, "result") `[[`, character(1), "error_class"), message = vapply(failed_results, if (return_trace) { `[[`, character(1), "message"), stringsAsFactors = FALSE) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) if (all(failed)) { traces <- bind_rows(lapply(seq_along(successful_results), error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", function(i) { failures$message[1L])) trace <- successful_results[[i]]$trace error$failures <- failures trial <- successful_trials[[i]]$trial class(error) <- c("goldilocks_all_trials_failed", class(error)) trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] stop(error) })) } out <- list(sims = sims, traces = traces, failures = failures, successful_trials <- trial_results[!failed] call = Call) successful_results <- lapply(successful_trials, `[[`, "result") } if (return_trace) { else { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) sims <- bind_rows(successful_results) traces <- bind_rows(lapply(seq_along(successful_results), out <- list(sims = sims, failures = failures, call = Call) function(i) { } trace <- successful_results[[i]]$trace attr(out$sims, "arguments") <- NULL trial <- successful_trials[[i]]$trial attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, })) end_of_study = end_of_study) out <- list(sims = sims, traces = traces, failures = failures, attr(out, "decision_design") <- attr(successful_results[[1]], call = Call) "decision_design", exact = TRUE) } attr(out, "prior_design") <- attr(successful_results[[1]], else { "prior_design", exact = TRUE) sims <- bind_rows(successful_results) attr(out, "rng_metadata") <- rng_metadata out <- list(sims = sims, failures = failures, call = Call) attr(out, "arguments") <- Arguments } attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, attr(out$sims, "arguments") <- NULL backend = backend, selection_reason = execution$reason, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, requested_ncores = as.integer(ncores), workers = as.integer(workers), N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) tasks = as.integer(N_trials)) attr(out, "decision_design") <- attr(successful_results[[1]], if (nrow(failures) > 0L) { "decision_design", exact = TRUE) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", attr(out, "prior_design") <- attr(successful_results[[1]], "for details.", call. = FALSE) "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata } attr(out, "arguments") <- Arguments return(out) attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, backend = backend, selection_reason = execution$reason, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, requested_ncores = as.integer(ncores), workers = as.integer(workers), 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, tasks = as.integer(N_trials)) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, if (nrow(failures) > 0L) { prior_surv = c(1, 144.927536231884, 1, 144.927536231884, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, "for details.", call. = FALSE) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, } 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, return(out) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, seed = 3425430) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333,
prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, 23: treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, seed = 3425430))) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank",
imputed_final = FALSE, hazard_treatment = c(0.005796, 0.0016824: ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, eval(expr, envir) seed = 3425430)
25: 23: eval(expr, envir)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week,
hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 26: seed = 3425430)))withVisible(eval(expr, envir))
24: 27: eval(expr, envir)withCallingHandlers(code, error = function (e)
rlang::entrace(e), message = function (cnd) 25: {eval(expr, envir) watcher$capture_plot_and_output()
if (on_message$capture) {26: watcher$push(cnd)withVisible(eval(expr, envir))
}27: if (on_message$silence) {withCallingHandlers(code, error = function (e) invokeRestart("muffleMessage")rlang::entrace(e), message = function (cnd) }{}, warning = function (cnd) watcher$capture_plot_and_output(){ if (on_message$capture) { if (getOption("warn") >= 2 || getOption("warn") < 0) { watcher$push(cnd) return() } } if (on_message$silence) { watcher$capture_plot_and_output() invokeRestart("muffleMessage") if (on_warning$capture) { } cnd <- sanitize_call(cnd)}, warning = function (cnd) { watcher$push(cnd) if (getOption("warn") >= 2 || getOption("warn") < 0) { } return() if (on_warning$silence) { } invokeRestart("muffleWarning") watcher$capture_plot_and_output() } if (on_warning$capture) {}, error = function (cnd) cnd <- sanitize_call(cnd){ watcher$push(cnd) } watcher$capture_plot_and_output() if (on_warning$silence) { cnd <- sanitize_call(cnd) invokeRestart("muffleWarning") watcher$push(cnd) } switch(on_error, continue = invokeRestart("eval_continue"), }, error = function (cnd) stop = invokeRestart("eval_stop"), error = NULL){}) watcher$capture_plot_and_output()
cnd <- sanitize_call(cnd)28: watcher$push(cnd)eval(call) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)
})29:
eval(call)28:
eval(call)30:
with_handlers({29: for (expr in tle$exprs) {eval(call) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()30: watcher$print_value(ev$value, ev$visible, envir)with_handlers({ } for (expr in tle$exprs) { TRUE ev <- withVisible(eval(expr, envir))}, handlers) watcher$capture_plot_and_output()
watcher$print_value(ev$value, ev$visible, envir)31: }doWithOneRestart(return(expr), restart) TRUE
}, handlers)32: withOneRestart(expr, restarts[[1L]])
31: 33: doWithOneRestart(return(expr), restart)withRestartList(expr, restarts[-nr])
32: 34: withOneRestart(expr, restarts[[1L]])doWithOneRestart(return(expr), restart)
33: withRestartList(expr, restarts[-nr])35:
34: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])doWithOneRestart(return(expr), restart)
36: 35: withRestartList(expr, restarts)withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
37: 36: withRestarts(with_handlers({withRestartList(expr, restarts) for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()37: watcher$print_value(ev$value, ev$visible, envir)withRestarts(with_handlers({ } for (expr in tle$exprs) { TRUE ev <- withVisible(eval(expr, envir))}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir)
}38: TRUEevaluate::evaluate(...)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
39: 38: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, evaluate::evaluate(...) 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) 39: 0Levaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, else 2L keep_message = if (is.numeric(options$message)) TRUE else options$message, }, output_handler = knit_handlers(options$render, options)) stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 40: 0Lin_dir(input_dir(), expr) else 2L
}, output_handler = knit_handlers(options$render, options))41: in_input_dir(evaluate(code, envir = env, new_device = FALSE,
40: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, in_dir(input_dir(), expr) keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else {
41: if (options$error && options$include) in_input_dir(evaluate(code, envir = env, new_device = FALSE, 0L else 2L keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, }, output_handler = knit_handlers(options$render, options))) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {42: if (options$error && options$include) eng_r(options)
0L43: else 2Lblock_exec(params) }, output_handler = knit_handlers(options$render, options)))
42: 44: eng_r(options)call_block(x)
43: 45: block_exec(params)process_group(group)
44: 46: call_block(x)withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {45: if (progress && is.function(pb$interrupt)) process_group(group) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 46: error <<- format(e)withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), })
error = function(e) {47: if (progress && is.function(pb$interrupt)) with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), pb$interrupt() error = function(e) { if (is_R_CMD_build() || is_R_CMD_check()) if (progress && is.function(pb$interrupt)) error <<- format(e) pb$interrupt() }) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)47: }), list(rlang_trace_top_env = knit_global()))with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {48: if (progress && is.function(pb$interrupt)) xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), pb$interrupt() error = function(e) { if (is_R_CMD_build() || is_R_CMD_check()) if (progress && is.function(pb$interrupt)) error <<- format(e) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) }), list(rlang_trace_top_env = knit_global())) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd)48: write_utf8(res, output %n% stdout())xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), paste0("\nQuitting from ", loc, if (!is.null(error)) error = function(e) { paste0("\n", rule(), error, "\n", rule())) if (progress && is.function(pb$interrupt)) }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e)49: }), list(rlang_trace_top_env = knit_global())), function(loc) {process_file(text, output) setwd(wd)
write_utf8(res, output %n% stdout())50: paste0("\nQuitting from ", loc, if (!is.null(error)) knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) paste0("\n", rule(), error, "\n", rule()))
51: }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)49:
52: process_file(text, output)vweave_rmarkdown(...)
50: 53: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)engine$weave(file, quiet = quiet, encoding = enc)
51: 54: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), doTryCatch(return(expr), name, parentenv, handler) output_dir = getwd(), ...)
55: 52: tryCatchOne(expr, names, parentenv, handlers[[1L]])vweave_rmarkdown(...)
56: 53: tryCatchList(expr, classes, parentenv, handlers)engine$weave(file, quiet = quiet, encoding = enc)
57: 54: tryCatch({doTryCatch(return(expr), name, parentenv, handler) engine$weave(file, quiet = quiet, encoding = enc)
55: setwd(startdir)tryCatchOne(expr, names, parentenv, handlers[[1L]]) output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {56: texi2pdf(file = output, clean = FALSE, quiet = quiet)tryCatchList(expr, classes, parentenv, handlers) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }
}, error = function(e) {57: OK <<- FALSEtryCatch({ message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", engine$weave(file, quiet = quiet, encoding = enc) file, conditionMessage(e))) setwd(startdir)}) output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {58: texi2pdf(file = output, clean = FALSE, quiet = quiet)tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", output <- find_vignette_product(name, by = "texi2pdf", TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpiYvqlt/file8bdc597483ae.rds") engine = engine)
}An irrecoverable exception occurred. R is aborting now ...
}, 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/RtmpiYvqlt/file8bdc597483ae.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-09-27 03:53:10.181 R[37654:356208] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-09-27 03:53:11.919 R[37748:356501] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-09-27 03:53:23.114 R[38093:357564] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-09-27 03:53:24.988 R[38222:357957] 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