Last updated on 2026-09-28 09:51:15 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 | 20.16 | 365.76 | 385.92 | 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 | 26.62 | 522.63 | 549.25 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmp96bzhA/file8f08142ecc9.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/Rtmp96bzhA/file8f08142ecc9.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
2026-09-28 18:58:55.284 R[40280:359697] 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,
Traceback:
values = impute_predictive_arm(time = data_in$time[current_treatment], 1: hazards = treatment_hazards, random_input = random_inputs$current_treatment, impute_predictive_arm(time = data_in$time[current_treatment], end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) hazards = treatment_hazards, random_input = random_inputs$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, 2: end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, fill_predictive_imputations(imputations = current, rows = current_treatment, binary_imputation = predictive_binary_imputation, check_futility = check_futility) values = impute_predictive_arm(time = data_in$time[current_treatment],
hazards = treatment_hazards, random_input = random_inputs$current_treatment, 4: end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))evaluate_interim_decision(data_interim = data_interim, look = i,
planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, 3: active_followup = active_followup_at(data_total, look_time), impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, binary_imputation = predictive_binary_imputation, check_futility = check_futility) 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, 4: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, evaluate_interim_decision(data_interim = data_interim, look = i, method = method, binary_imputation = binary_imputation, check_futility = check_futility, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, Qn = Qn[i]) active_followup = active_followup_at(data_total, look_time),
end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, 5: single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, cutpoints = cutpoints, N_total = N_total, lambda = lambda, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, method = method, binary_imputation = binary_imputation, check_futility = check_futility, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, Qn = Qn[i]) prop_loss = prop_loss, alternative = alternative, h0 = h0,
Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, 5: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, imputed_final = imputed_final, empty_interval = empty_interval, cutpoints = cutpoints, N_total = N_total, lambda = lambda, return_trace = return_trace, prior_surv_final = prior_surv_final, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method,
binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, 6: prop_loss = prop_loss, alternative = alternative, h0 = h0, doTryCatch(return(expr), name, parentenv, handler) Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute,
N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, 7: imputed_final = imputed_final, empty_interval = empty_interval, tryCatchOne(expr, names, parentenv, handlers[[1L]]) return_trace = return_trace, prior_surv_final = prior_surv_final,
generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) 8:
tryCatchList(expr, classes, parentenv, handlers) 6:
doTryCatch(return(expr), name, parentenv, handler) 9:
tryCatch({ 7: if (!is.null(trial_streams)) {tryCatchOne(expr, names, parentenv, handlers[[1L]])
assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) 8: }tryCatchList(expr, classes, parentenv, handlers) result <- survival_adapt_fn(hazard_treatment = hazard_treatment,
hazard_control = hazard_control, cutpoints = cutpoints, 9: N_total = N_total, lambda = lambda, lambda_time = lambda_time, tryCatch({ interim_look = interim_look, end_of_study = end_of_study, if (!is.null(trial_streams)) { prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) binary_imputation = binary_imputation, block = block, } rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, hazard_control = hazard_control, cutpoints = cutpoints, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, N_total = N_total, lambda = lambda, lambda_time = lambda_time, method = method, imputed_final = imputed_final, empty_interval = empty_interval, interim_look = interim_look, end_of_study = end_of_study, return_trace = return_trace, prior_surv_final = prior_surv_final, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) binary_imputation = binary_imputation, block = block, attr(result, "arguments") <- NULL rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, if (inherits(result, "goldilocks_trial")) { h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, attr(result$summary, "arguments") <- NULL N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, } method = method, imputed_final = imputed_final, empty_interval = empty_interval, list(trial = as.integer(x), result = result) return_trace = return_trace, prior_surv_final = prior_surv_final, }, error = function(error) { generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], attr(result, "arguments") <- NULL message = conditionMessage(error)) if (inherits(result, "goldilocks_trial")) {}) attr(result$summary, "arguments") <- NULL
}10: list(trial = as.integer(x), result = result)FUN(X[[i]], ...)}, error = function(error) {
list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], 11: message = conditionMessage(error))lapply(X = S, FUN = FUN, ...)})
12:
doTryCatch(return(expr), name, parentenv, handler)10:
FUN(X[[i]], ...)13:
tryCatchOne(expr, names, parentenv, handlers[[1L]])11:
lapply(X = S, FUN = FUN, ...)14:
tryCatchList(expr, classes, parentenv, handlers)12:
doTryCatch(return(expr), name, parentenv, handler)15:
tryCatch(expr, error = function(e) {13: call <- conditionCall(e)tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 14: call <- sys.call(-4L)tryCatchList(expr, classes, parentenv, handlers) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ")15: LONG <- 75LtryCatch(expr, error = function(e) { sm <- strsplit(conditionMessage(e), "\n")[[1L]] call <- conditionCall(e) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (!is.null(call)) { if (is.na(w)) if (identical(call[[1L]], quote(doTryCatch))) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], call <- sys.call(-4L) type = "b") dcall <- deparse(call, nlines = 1L) if (w > LONG) prefix <- paste("Error in", dcall, ": ") prefix <- paste0(prefix, "\n ") LONG <- 75L } sm <- strsplit(conditionMessage(e), "\n")[[1L]] else prefix <- "Error : " w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (is.na(w)) if (!silent && isTRUE(getOption("show.error.messages"))) { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], cat(msg, file = outFile) type = "b") .Internal(printDeferredWarnings()) if (w > LONG) } prefix <- paste0(prefix, "\n ") invisible(structure(msg, class = "try-error", condition = e)) }}) else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")16: .Internal(seterrmessage(msg[1L]))try(lapply(X = S, FUN = FUN, ...), silent = TRUE) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)17: .Internal(printDeferredWarnings())sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) }
invisible(structure(msg, class = "try-error", condition = e))18: })FUN(X[[i]], ...)
16: 19: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)lapply(seq_len(cores), inner.do)
17: 20: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))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)18:
FUN(X[[i]], ...)21:
pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)19:
lapply(seq_len(cores), inner.do)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, 20: 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, treatment = 1), prop_loss = 0, alternative = "greater", mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) 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", 21: imputed_final = FALSE, empty_interval = c("prior", "propagate", pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
"error"), return_trace = FALSE, ncores = 1L, backend = c("auto", 22: "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) 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, Call <- match.call() 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, Arguments <- capture_arguments(sim_trials, environment()) treatment = 1), prop_loss = 0, alternative = "greater", method <- normalize_analysis_method(method) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, Arguments$method <- method N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", empty_interval <- match.arg(empty_interval) imputed_final = FALSE, empty_interval = c("prior", "propagate", binary_imputation <- match.arg(binary_imputation) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", backend <- match.arg(backend) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", Arguments$empty_interval <- empty_interval "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend Qn = 1, rmst_tau = end_of_study) requested_backend <- backend{ Call <- match.call() caller_rng_kind <- RNGkind() Arguments <- capture_arguments(sim_trials, environment()) validate_positive_integer_scalar(N_trials, "N_trials") method <- normalize_analysis_method(method) single_arm <- is.null(hazard_control) Arguments$method <- method if (!single_arm) { empty_interval <- match.arg(empty_interval) rand_ratio <- validate_randomization_args(N_total, block, binary_imputation <- match.arg(binary_imputation) rand_ratio, allocation_name = "rand_ratio") backend <- match.arg(backend) Arguments$rand_ratio <- rand_ratio Arguments$empty_interval <- empty_interval } Arguments$binary_imputation <- binary_imputation prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$backend <- backend Arguments$prop_loss <- prop_loss requested_backend <- backend validate_logical_scalar(return_trace, "return_trace") caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_trials, "N_trials") validate_positive_integer_scalar(N_mcmc, "N_mcmc") single_arm <- is.null(hazard_control) validate_single_probability(mc_conf_level, "mc_conf_level", if (!single_arm) { upper_open = TRUE) rand_ratio <- validate_randomization_args(N_total, block, if (mc_conf_level <= 0.5) { rand_ratio, allocation_name = "rand_ratio") stop("'mc_conf_level' must be greater than 0.5 and less than 1") Arguments$rand_ratio <- rand_ratio } } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) 0), N_impute = N_impute) Arguments$prop_loss <- prop_loss if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_logical_scalar(return_trace, "return_trace") validate_rmst_args(rmst_tau, end_of_study, h0) validate_positive_integer_scalar(N_impute, "N_impute") validate_analysis_configuration(method, alternative, validate_positive_integer_scalar(N_mcmc, "N_mcmc") is.null(hazard_control), imputed_final) validate_single_probability(mc_conf_level, "mc_conf_level", } upper_open = TRUE) validate_positive_integer_scalar(ncores, "ncores") if (mc_conf_level <= 0.5) { execution <- resolve_sim_execution(backend = backend, ncores = ncores, stop("'mc_conf_level' must be greater than 0.5 and less than 1") N_trials = N_trials) } backend <- execution$backend validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > workers <- execution$workers 0), N_impute = N_impute) stream_seed <- NULL if (identical(method, "rmst")) { if (!is.null(seed)) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || validate_rmst_args(rmst_tau, end_of_study, h0) !is.finite(seed) || seed != floor(seed)) { validate_analysis_configuration(method, alternative, stop("'seed' must be NULL or a single integer value") is.null(hazard_control), imputed_final) } } old_kind <- RNGkind() validate_positive_integer_scalar(ncores, "ncores") old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, execution <- resolve_sim_execution(backend = backend, ncores = ncores, inherits = FALSE) N_trials = N_trials) if (old_seed_exists) { backend <- execution$backend old_seed <- get(".Random.seed", envir = .GlobalEnv, workers <- execution$workers inherits = FALSE) stream_seed <- NULL } if (!is.null(seed)) { on.exit({ if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || do.call(RNGkind, as.list(old_kind)) !is.finite(seed) || seed != floor(seed)) { if (old_seed_exists) { stop("'seed' must be NULL or a single integer value") assign(".Random.seed", old_seed, envir = .GlobalEnv) } } else if (exists(".Random.seed", envir = .GlobalEnv, old_kind <- RNGkind() inherits = FALSE)) { old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, rm(".Random.seed", envir = .GlobalEnv) inherits = FALSE) } if (old_seed_exists) { }, add = TRUE) old_seed <- get(".Random.seed", envir = .GlobalEnv, stream_seed <- seed inherits = FALSE) trial_streams <- make_rng_streams(stream_seed, N_trials) } } on.exit({ else if (backend == "psock") { do.call(RNGkind, as.list(old_kind)) stream_seed <- sample.int(.Machine$integer.max - 1L, if (old_seed_exists) { size = 1L) assign(".Random.seed", old_seed, envir = .GlobalEnv) trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (exists(".Random.seed", envir = .GlobalEnv, } else { inherits = FALSE)) { trial_streams <- NULL rm(".Random.seed", envir = .GlobalEnv) } } }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (backend == "psock") { rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { stream_seed <- sample.int(.Machine$integer.max - 1L, caller_rng_kind[1] size = 1L) } else { trial_streams <- make_rng_streams(stream_seed, N_trials) "L'Ecuyer-CMRG" } }, seed_policy = if (!is.null(seed)) { else { "explicit_preserve_caller" trial_streams <- NULL } else if (backend == "psock") { } "caller_derived_psock" rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { } else { caller_rng_kind[1] "caller_state" } else { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), "L'Ecuyer-CMRG" stream_seed = stream_seed) }, seed_policy = if (!is.null(seed)) { survival_adapt_fn <- if (backend == "psock") { "explicit_preserve_caller" make_psock_callable("survival_adapt") } else if (backend == "psock") { } "caller_derived_psock" else { } else { survival_adapt "caller_state" } }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), survival_adapt_wrapper <- function(x) { stream_seed = stream_seed) tryCatch({ survival_adapt_fn <- if (backend == "psock") { if (!is.null(trial_streams)) { make_psock_callable("survival_adapt") assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } else { result <- survival_adapt_fn(hazard_treatment = hazard_treatment, survival_adapt hazard_control = hazard_control, cutpoints = cutpoints, } N_total = N_total, lambda = lambda, lambda_time = lambda_time, survival_adapt_wrapper <- function(x) { interim_look = interim_look, end_of_study = end_of_study, tryCatch({ prior_surv = prior_surv, prior_bin = prior_bin, if (!is.null(trial_streams)) { bin_method = bin_method, binary_imputation = binary_imputation, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, } alternative = alternative, h0 = h0, Fn = Fn, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, hazard_control = hazard_control, cutpoints = cutpoints, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, N_total = N_total, lambda = lambda, lambda_time = lambda_time, empty_interval = empty_interval, return_trace = return_trace, interim_look = interim_look, end_of_study = end_of_study, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, prior_surv = prior_surv, prior_bin = prior_bin, rmst_tau = rmst_tau) bin_method = bin_method, binary_imputation = binary_imputation, attr(result, "arguments") <- NULL block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, if (inherits(result, "goldilocks_trial")) { alternative = alternative, h0 = h0, Fn = Fn, attr(result$summary, "arguments") <- NULL Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, } N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, list(trial = as.integer(x), result = result) method = method, imputed_final = imputed_final, }, error = function(error) { empty_interval = empty_interval, return_trace = return_trace, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, message = conditionMessage(error)) rmst_tau = rmst_tau) }) attr(result, "arguments") <- NULL } if (inherits(result, "goldilocks_trial")) { trial_index <- seq_len(N_trials) attr(result$summary, "arguments") <- NULL trial_results <- switch(backend, sequential = lapply(trial_index, } survival_adapt_wrapper), fork = pbmclapply(trial_index, list(trial = as.integer(x), result = result) survival_adapt_wrapper, mc.cores = workers), psock = { }, error = function(error) { active_cluster <- make_sim_cluster(workers) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], on.exit(stop_sim_cluster(active_cluster), add = TRUE) message = conditionMessage(error)) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) }) } failed <- vapply(trial_results, function(x) is.null(x$result), trial_index <- seq_len(N_trials) logical(1)) trial_results <- switch(backend, sequential = lapply(trial_index, failed_results <- trial_results[failed] survival_adapt_wrapper), fork = pbmclapply(trial_index, failures <- data.frame(trial = vapply(failed_results, `[[`, survival_adapt_wrapper, mc.cores = workers), psock = { integer(1), "trial"), error_class = vapply(failed_results, active_cluster <- make_sim_cluster(workers) `[[`, character(1), "error_class"), message = vapply(failed_results, on.exit(stop_sim_cluster(active_cluster), add = TRUE) `[[`, character(1), "message"), stringsAsFactors = FALSE) initialize_sim_cluster(active_cluster) if (all(failed)) { run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", }) failures$message[1L])) failed <- vapply(trial_results, function(x) is.null(x$result), error$failures <- failures logical(1)) class(error) <- c("goldilocks_all_trials_failed", class(error)) failed_results <- trial_results[failed] stop(error) failures <- data.frame(trial = vapply(failed_results, `[[`, } integer(1), "trial"), error_class = vapply(failed_results, successful_trials <- trial_results[!failed] `[[`, character(1), "error_class"), message = vapply(failed_results, successful_results <- lapply(successful_trials, `[[`, "result") `[[`, character(1), "message"), stringsAsFactors = FALSE) if (return_trace) { if (all(failed)) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", traces <- bind_rows(lapply(seq_along(successful_results), failures$message[1L])) function(i) { error$failures <- failures trace <- successful_results[[i]]$trace class(error) <- c("goldilocks_all_trials_failed", class(error)) trial <- successful_trials[[i]]$trial stop(error) trace$trial <- rep.int(trial, nrow(trace)) } trace[c("trial", setdiff(names(trace), "trial"))] successful_trials <- trial_results[!failed] })) successful_results <- lapply(successful_trials, `[[`, "result") out <- list(sims = sims, traces = traces, failures = failures, if (return_trace) { call = Call) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) } traces <- bind_rows(lapply(seq_along(successful_results), else { function(i) { sims <- bind_rows(successful_results) trace <- successful_results[[i]]$trace out <- list(sims = sims, failures = failures, call = Call) trial <- successful_trials[[i]]$trial } trace$trial <- rep.int(trial, nrow(trace)) attr(out$sims, "arguments") <- NULL trace[c("trial", setdiff(names(trace), "trial"))] attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, })) N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, out <- list(sims = sims, traces = traces, failures = failures, end_of_study = end_of_study) call = Call) attr(out, "decision_design") <- attr(successful_results[[1]], } "decision_design", exact = TRUE) else { attr(out, "prior_design") <- attr(successful_results[[1]], sims <- bind_rows(successful_results) "prior_design", exact = TRUE) out <- list(sims = sims, failures = failures, call = Call) attr(out, "rng_metadata") <- rng_metadata } attr(out, "arguments") <- Arguments attr(out$sims, "arguments") <- NULL attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, backend = backend, selection_reason = execution$reason, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, requested_ncores = as.integer(ncores), workers = as.integer(workers), end_of_study = end_of_study) tasks = as.integer(N_trials)) attr(out, "decision_design") <- attr(successful_results[[1]], if (nrow(failures) > 0L) { "decision_design", exact = TRUE) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", attr(out, "prior_design") <- attr(successful_results[[1]], "for details.", call. = FALSE) "prior_design", exact = TRUE) } attr(out, "rng_metadata") <- rng_metadata return(out) attr(out, "arguments") <- Arguments})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, backend = backend, selection_reason = execution$reason, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, requested_ncores = as.integer(ncores), workers = as.integer(workers), 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, tasks = as.integer(N_trials)) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, if (nrow(failures) > 0L) { 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, "for details.", call. = FALSE) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, } 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, return(out) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, seed = 3425430) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, prior_surv = c(1, 144.927536231884, 1, 144.927536231884,
23: 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, seed = 3425430))) 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300,
mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", 24: imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168eval(expr, envir) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2,
seed = 3425430)
25: 23: do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, eval(expr, envir) hazard_control = hazard_control_week, N_trials = 20, ncores = 2,
seed = 3425430)))26:
withVisible(eval(expr, envir))24:
eval(expr, envir)27:
withCallingHandlers(code, error = function (e) 25: rlang::entrace(e), message = function (cnd) eval(expr, envir){
watcher$capture_plot_and_output()26: if (on_message$capture) {withVisible(eval(expr, envir)) watcher$push(cnd)
}27: if (on_message$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$push(cnd){ } if (getOption("warn") >= 2 || getOption("warn") < 0) { if (on_warning$silence) { return() invokeRestart("muffleWarning") } } watcher$capture_plot_and_output()}, error = function (cnd) if (on_warning$capture) {{ cnd <- sanitize_call(cnd) watcher$capture_plot_and_output() watcher$push(cnd) cnd <- sanitize_call(cnd) } watcher$push(cnd) if (on_warning$silence) { switch(on_error, continue = invokeRestart("eval_continue"), invokeRestart("muffleWarning") stop = invokeRestart("eval_stop"), error = NULL) }})}, error = function (cnd)
{28: watcher$capture_plot_and_output()eval(call) cnd <- sanitize_call(cnd)
watcher$push(cnd)29: switch(on_error, continue = invokeRestart("eval_continue"), eval(call) stop = invokeRestart("eval_stop"), error = NULL)
})30:
with_handlers({28: for (expr in tle$exprs) {eval(call) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()29: watcher$print_value(ev$value, ev$visible, envir)eval(call) }
TRUE30: }, handlers)
with_handlers({31: for (expr in tle$exprs) {doWithOneRestart(return(expr), restart) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()32: watcher$print_value(ev$value, ev$visible, envir)withOneRestart(expr, restarts[[1L]]) }
TRUE33: }, handlers)withRestartList(expr, restarts[-nr])
31: 34: doWithOneRestart(return(expr), restart)doWithOneRestart(return(expr), restart)
32: 35: withOneRestart(expr, restarts[[1L]])withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
33: 36: withRestartList(expr, restarts[-nr])withRestartList(expr, restarts)
34: 37: doWithOneRestart(return(expr), restart)withRestarts(with_handlers({
for (expr in tle$exprs) {35: ev <- withVisible(eval(expr, envir))withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) watcher$capture_plot_and_output()
watcher$print_value(ev$value, ev$visible, envir)36: }withRestartList(expr, restarts) TRUE
}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)37:
withRestarts(with_handlers({38: for (expr in tle$exprs) {evaluate::evaluate(...) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()39: watcher$print_value(ev$value, ev$visible, envir)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, TRUE stop_on_error = if (is.numeric(options$error)) options$error else {}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) if (options$error && options$include)
0L38: else 2Levaluate::evaluate(...) }, output_handler = knit_handlers(options$render, options))
39: 40: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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$include) in_input_dir(evaluate(code, envir = env, new_device = FALSE, 0L keep_warning = if (is.numeric(options$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$render, options)))41:
in_input_dir(evaluate(code, envir = env, new_device = FALSE, 42: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, eng_r(options) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {43: if (options$error && options$include) block_exec(params) 0L
else 2L44: }, output_handler = knit_handlers(options$render, options)))call_block(x)
42: 45: eng_r(options)process_group(group)
43: 46: block_exec(params)withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {44: if (progress && is.function(pb$interrupt)) call_block(x) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 45: error <<- format(e)process_group(group) })
46: 47: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { error = function(e) { if (progress && is.function(pb$interrupt)) if (progress && is.function(pb$interrupt)) pb$interrupt() pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) error <<- format(e) }) }), list(rlang_trace_top_env = knit_global()))
47: 48: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) {xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (progress && is.function(pb$interrupt)) error = function(e) { pb$interrupt() if (progress && is.function(pb$interrupt)) if (is_R_CMD_build() || is_R_CMD_check()) pb$interrupt() error <<- format(e) if (is_R_CMD_build() || is_R_CMD_check()) }), list(rlang_trace_top_env = knit_global())) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) {48: setwd(wd)xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), write_utf8(res, output %n% stdout()) error = function(e) { paste0("\nQuitting from ", loc, if (!is.null(error)) if (progress && is.function(pb$interrupt)) paste0("\n", rule(), error, "\n", rule())) pb$interrupt()}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)49: }), list(rlang_trace_top_env = knit_global())), function(loc) {process_file(text, output) setwd(wd)
write_utf8(res, output %n% stdout())50: paste0("\nQuitting from ", loc, if (!is.null(error)) knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) paste0("\n", rule(), error, "\n", rule()))
}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)51:
rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 49: output_dir = getwd(), ...)process_file(text, output)
52: 50: vweave_rmarkdown(...)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
53: 51: engine$weave(file, quiet = quiet, encoding = enc)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)54:
52: doTryCatch(return(expr), name, parentenv, handler)vweave_rmarkdown(...)
55: 53: tryCatchOne(expr, names, parentenv, handlers[[1L]])engine$weave(file, quiet = quiet, encoding = enc)
56: 54: tryCatchList(expr, classes, parentenv, handlers)doTryCatch(return(expr), name, parentenv, handler)
57: 55: tryCatch({tryCatchOne(expr, names, parentenv, handlers[[1L]]) engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)56: output <- find_vignette_product(name, by = "weave", engine = engine)tryCatchList(expr, classes, parentenv, handlers) if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)57: output <- find_vignette_product(name, by = "texi2pdf", tryCatch({ engine = engine) engine$weave(file, quiet = quiet, encoding = enc) } setwd(startdir)}, error = function(e) { output <- find_vignette_product(name, by = "weave", engine = engine) OK <<- FALSE if (!have.makefile && vignette_is_tex(output)) { message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", texi2pdf(file = output, clean = FALSE, quiet = quiet) file, conditionMessage(e))) output <- find_vignette_product(name, by = "texi2pdf", }) engine = engine)
}58: }, error = function(e) {tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", OK <<- FALSE TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmp96bzhA/file8f082d74c35a.rds") message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))An irrecoverable exception occurred. R is aborting now ...
})
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/Rtmp96bzhA/file8f082d74c35a.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
2026-09-28 18:59:01.095 R[41950:362339] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-09-28 18:59:02.611 R[41962:362389] 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
2026-09-28 18:59:13.821 R[42245:363274] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-09-28 18:59:15.631 R[42464:363907] 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