Last updated on 2026-10-05 06:52:59 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 27.89 | 551.01 | 578.90 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.83 | 372.50 | 391.33 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 357.04 | 378.04 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 370.44 | 390.44 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 42.00 | 513.00 | 555.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 26.80 | 521.71 | 548.51 | OK | |
| r-release-linux-x86_64 | 1.0.0 | OK | ||||
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 43.00 | 436.00 | 479.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 9.00 | 83.00 | 92.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 52.00 | 572.00 | 624.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmpt2ZzPd/filecd6538e3f5bf.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/Rtmpt2ZzPd/filecd6538e3f5bf.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-10-05 16:53:25.502 R[54692:810157] 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: 4: evaluate_interim_decision(data_interim = data_interim, look = i, fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, hazards = treatment_hazards, random_input = random_inputs$current_treatment, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
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, Qn = Qn[i]) 4:
evaluate_interim_decision(data_interim = data_interim, look = i, 5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, cutpoints = cutpoints, N_total = N_total, lambda = lambda, active_followup = active_followup_at(data_total, look_time), lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, prop_loss = prop_loss, alternative = alternative, h0 = h0, 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, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, method = method, binary_imputation = binary_imputation, check_futility = check_futility, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, Qn = Qn[i]) imputed_final = imputed_final, empty_interval = empty_interval,
return_trace = return_trace, prior_surv_final = prior_surv_final, 5: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control,
cutpoints = cutpoints, N_total = N_total, lambda = lambda, 6: doTryCatch(return(expr), name, parentenv, handler) 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, 7: prop_loss = prop_loss, alternative = alternative, h0 = h0, tryCatchOne(expr, names, parentenv, handlers[[1L]]) 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, 8: tryCatchList(expr, classes, parentenv, handlers) 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) 9: tryCatch({
6: if (!is.null(trial_streams)) {doTryCatch(return(expr), name, parentenv, handler) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)
} result <- survival_adapt_fn(hazard_treatment = hazard_treatment, 7: hazard_control = hazard_control, cutpoints = cutpoints, tryCatchOne(expr, names, parentenv, handlers[[1L]])
N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, 8: prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, tryCatchList(expr, classes, parentenv, handlers) rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative,
h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, 9: N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, tryCatch({ if (!is.null(trial_streams)) { method = method, imputed_final = imputed_final, empty_interval = empty_interval, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) return_trace = return_trace, prior_surv_final = prior_surv_final, } generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, 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: list(trial = as.integer(x), result = result)lapply(X = S, FUN = FUN, ...)}, error = function(error) {
list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], 12: message = conditionMessage(error))doTryCatch(return(expr), name, parentenv, handler)})
13: 10: tryCatchOne(expr, names, parentenv, handlers[[1L]])FUN(X[[i]], ...)
11: 14: tryCatchList(expr, classes, parentenv, handlers)lapply(X = S, FUN = FUN, ...)
15: tryCatch(expr, error = function(e) {
call <- conditionCall(e)12: if (!is.null(call)) {doTryCatch(return(expr), name, parentenv, handler) if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L)13: dcall <- deparse(call, nlines = 1L)tryCatchOne(expr, names, parentenv, handlers[[1L]]) prefix <- paste("Error in", dcall, ": ") LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]14: w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")tryCatchList(expr, classes, parentenv, handlers) if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], 15: type = "b")tryCatch(expr, error = function(e) { if (w > LONG) call <- conditionCall(e) prefix <- paste0(prefix, "\n ") if (!is.null(call)) { } if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") dcall <- deparse(call, nlines = 1L) .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { prefix <- paste("Error in", dcall, ": ") cat(msg, file = outFile) LONG <- 75L .Internal(printDeferredWarnings()) sm <- strsplit(conditionMessage(e), "\n")[[1L]] } w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) invisible(structure(msg, class = "try-error", condition = e))}) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")16: if (w > LONG) try(lapply(X = S, FUN = FUN, ...), silent = TRUE) prefix <- paste0(prefix, "\n ") }
else prefix <- "Error : "17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))18: if (!silent && isTRUE(getOption("show.error.messages"))) {FUN(X[[i]], ...) cat(msg, file = outFile)
19: .Internal(printDeferredWarnings())lapply(seq_len(cores), inner.do) }
invisible(structure(msg, class = "try-error", condition = e))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)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: FUN(X[[i]], ...) 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, 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: "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) Qn = 1, rmst_tau = end_of_study)
{22: Call <- match.call() Arguments <- capture_arguments(sim_trials, environment())(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, method <- normalize_analysis_method(method) N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, Arguments$method <- method end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, empty_interval <- match.arg(empty_interval) 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, binary_imputation <- match.arg(binary_imputation) treatment = 1), prop_loss = 0, alternative = "greater", backend <- match.arg(backend) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, Arguments$empty_interval <- empty_interval N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", Arguments$binary_imputation <- binary_imputation imputed_final = FALSE, empty_interval = c("prior", "propagate", Arguments$backend <- backend "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", requested_backend <- backend "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", caller_rng_kind <- RNGkind() "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, validate_positive_integer_scalar(N_trials, "N_trials") Qn = 1, rmst_tau = end_of_study) single_arm <- is.null(hazard_control){ if (!single_arm) { Call <- match.call() rand_ratio <- validate_randomization_args(N_total, block, Arguments <- capture_arguments(sim_trials, environment()) rand_ratio, allocation_name = "rand_ratio") method <- normalize_analysis_method(method) Arguments$rand_ratio <- rand_ratio Arguments$method <- method } empty_interval <- match.arg(empty_interval) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) binary_imputation <- match.arg(binary_imputation) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") backend <- match.arg(backend) validate_positive_integer_scalar(N_impute, "N_impute") Arguments$empty_interval <- empty_interval validate_positive_integer_scalar(N_mcmc, "N_mcmc") Arguments$binary_imputation <- binary_imputation validate_single_probability(mc_conf_level, "mc_conf_level", Arguments$backend <- backend upper_open = TRUE) requested_backend <- backend if (mc_conf_level <= 0.5) { caller_rng_kind <- RNGkind() stop("'mc_conf_level' must be greater than 0.5 and less than 1") validate_positive_integer_scalar(N_trials, "N_trials") } single_arm <- is.null(hazard_control) validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > if (!single_arm) { 0), N_impute = N_impute) rand_ratio <- validate_randomization_args(N_total, block, if (identical(method, "rmst")) { rand_ratio, allocation_name = "rand_ratio") validate_endpoint_time(end_of_study, cutpoints, "end_of_study") Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) validate_rmst_args(rmst_tau, end_of_study, h0) Arguments$prop_loss <- prop_loss validate_analysis_configuration(method, alternative, validate_logical_scalar(return_trace, "return_trace") is.null(hazard_control), imputed_final) 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", } validate_positive_integer_scalar(ncores, "ncores") upper_open = TRUE) execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") 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)) { stop("'seed' must be NULL or a single integer value") validate_analysis_configuration(method, alternative, } is.null(hazard_control), imputed_final) old_kind <- RNGkind() } old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, validate_positive_integer_scalar(ncores, "ncores") inherits = FALSE) execution <- resolve_sim_execution(backend = backend, ncores = ncores, if (old_seed_exists) { N_trials = N_trials) old_seed <- get(".Random.seed", envir = .GlobalEnv, backend <- execution$backend inherits = FALSE) workers <- execution$workers } stream_seed <- NULL on.exit({ if (!is.null(seed)) { do.call(RNGkind, as.list(old_kind)) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || if (old_seed_exists) { !is.finite(seed) || seed != floor(seed)) { assign(".Random.seed", old_seed, envir = .GlobalEnv) stop("'seed' must be NULL or a single integer value") } else if (exists(".Random.seed", envir = .GlobalEnv, } inherits = FALSE)) { old_kind <- RNGkind() rm(".Random.seed", envir = .GlobalEnv) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, } inherits = FALSE) }, add = TRUE) if (old_seed_exists) { stream_seed <- seed old_seed <- get(".Random.seed", envir = .GlobalEnv, trial_streams <- make_rng_streams(stream_seed, N_trials) inherits = FALSE) } } else if (backend == "psock") { on.exit({ do.call(RNGkind, as.list(old_kind)) stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) if (old_seed_exists) { trial_streams <- make_rng_streams(stream_seed, N_trials) assign(".Random.seed", old_seed, envir = .GlobalEnv) } } else if (exists(".Random.seed", envir = .GlobalEnv, else { inherits = FALSE)) { trial_streams <- NULL rm(".Random.seed", envir = .GlobalEnv) } } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { }, add = TRUE) caller_rng_kind[1] stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else { } "L'Ecuyer-CMRG" else if (backend == "psock") { }, seed_policy = if (!is.null(seed)) { stream_seed <- sample.int(.Machine$integer.max - 1L, "explicit_preserve_caller" size = 1L) } else if (backend == "psock") { trial_streams <- make_rng_streams(stream_seed, N_trials) "caller_derived_psock" } } else { else { "caller_state" trial_streams <- NULL }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), } stream_seed = stream_seed) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { survival_adapt_fn <- if (backend == "psock") { caller_rng_kind[1] make_psock_callable("survival_adapt") } else { } "L'Ecuyer-CMRG" else { }, seed_policy = if (!is.null(seed)) { survival_adapt "explicit_preserve_caller" } } else if (backend == "psock") { survival_adapt_wrapper <- function(x) { "caller_derived_psock" tryCatch({ } else { if (!is.null(trial_streams)) { "caller_state" assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), } stream_seed = stream_seed) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, survival_adapt_fn <- if (backend == "psock") { hazard_control = hazard_control, cutpoints = cutpoints, make_psock_callable("survival_adapt") N_total = N_total, lambda = lambda, lambda_time = lambda_time, } interim_look = interim_look, end_of_study = end_of_study, else { prior_surv = prior_surv, prior_bin = prior_bin, survival_adapt bin_method = bin_method, binary_imputation = binary_imputation, } block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, survival_adapt_wrapper <- function(x) { alternative = alternative, h0 = h0, Fn = Fn, tryCatch({ Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, if (!is.null(trial_streams)) { N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) method = method, imputed_final = imputed_final, } empty_interval = empty_interval, return_trace = return_trace, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, hazard_control = hazard_control, cutpoints = 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, attr(result$summary, "arguments") <- NULL bin_method = bin_method, binary_imputation = binary_imputation, } block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, list(trial = as.integer(x), result = result) alternative = alternative, h0 = h0, Fn = Fn, }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, message = conditionMessage(error)) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }) method = method, imputed_final = imputed_final, } empty_interval = empty_interval, return_trace = return_trace, trial_index <- seq_len(N_trials) prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, trial_results <- switch(backend, sequential = lapply(trial_index, rmst_tau = rmst_tau) survival_adapt_wrapper), fork = pbmclapply(trial_index, attr(result, "arguments") <- NULL survival_adapt_wrapper, mc.cores = workers), psock = { if (inherits(result, "goldilocks_trial")) { active_cluster <- make_sim_cluster(workers) attr(result$summary, "arguments") <- NULL on.exit(stop_sim_cluster(active_cluster), add = TRUE) } initialize_sim_cluster(active_cluster) list(trial = as.integer(x), result = result) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }, error = function(error) { }) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], failed <- vapply(trial_results, function(x) is.null(x$result), message = conditionMessage(error)) logical(1)) }) failed_results <- trial_results[failed] } failures <- data.frame(trial = vapply(failed_results, `[[`, trial_index <- seq_len(N_trials) integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) trial_results <- switch(backend, sequential = lapply(trial_index, if (all(failed)) { survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) active_cluster <- make_sim_cluster(workers) error$failures <- failures on.exit(stop_sim_cluster(active_cluster), add = TRUE) class(error) <- c("goldilocks_all_trials_failed", class(error)) initialize_sim_cluster(active_cluster) stop(error) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) } }) successful_trials <- trial_results[!failed] failed <- vapply(trial_results, function(x) is.null(x$result), successful_results <- lapply(successful_trials, `[[`, "result") logical(1)) if (return_trace) { failed_results <- trial_results[failed] sims <- bind_rows(lapply(successful_results, function(x) x$summary)) failures <- data.frame(trial = vapply(failed_results, `[[`, traces <- bind_rows(lapply(seq_along(successful_results), integer(1), "trial"), error_class = vapply(failed_results, function(i) { `[[`, character(1), "error_class"), message = vapply(failed_results, trace <- successful_results[[i]]$trace `[[`, character(1), "message"), stringsAsFactors = FALSE) trial <- successful_trials[[i]]$trial if (all(failed)) { trace$trial <- rep.int(trial, nrow(trace)) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", trace[c("trial", setdiff(names(trace), "trial"))] failures$message[1L])) })) error$failures <- failures out <- list(sims = sims, traces = traces, failures = failures, class(error) <- c("goldilocks_all_trials_failed", class(error)) call = Call) stop(error) } } else { successful_trials <- trial_results[!failed] sims <- bind_rows(successful_results) successful_results <- lapply(successful_trials, `[[`, "result") out <- list(sims = sims, failures = failures, call = Call) if (return_trace) { } sims <- bind_rows(lapply(successful_results, function(x) x$summary)) attr(out$sims, "arguments") <- NULL traces <- bind_rows(lapply(seq_along(successful_results), attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, function(i) { end_of_study = end_of_study) trace <- successful_results[[i]]$trace attr(out, "decision_design") <- attr(successful_results[[1]], trial <- successful_trials[[i]]$trial "decision_design", exact = TRUE) trace$trial <- rep.int(trial, nrow(trace)) attr(out, "prior_design") <- attr(successful_results[[1]], trace[c("trial", setdiff(names(trace), "trial"))] "prior_design", exact = TRUE) })) attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments out <- list(sims = sims, traces = traces, failures = failures, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, call = Call) backend = backend, selection_reason = execution$reason, } requested_ncores = as.integer(ncores), workers = as.integer(workers), else { tasks = as.integer(N_trials)) sims <- bind_rows(successful_results) if (nrow(failures) > 0L) { out <- list(sims = sims, failures = failures, call = Call) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", } "for details.", call. = FALSE) attr(out$sims, "arguments") <- NULL } attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, return(out) N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, attr(out, "decision_design") <- attr(successful_results[[1]], lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, "prior_design", exact = TRUE) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, attr(out, "rng_metadata") <- rng_metadata prior_surv = c(1, 144.927536231884, 1, 144.927536231884, attr(out, "arguments") <- Arguments 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, backend = backend, selection_reason = execution$reason, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, requested_ncores = as.integer(ncores), workers = as.integer(workers), 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, tasks = as.integer(N_trials)) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", if (nrow(failures) > 0L) { imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, "for details.", call. = FALSE) seed = 3425430) }
return(out)23: })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, seed = 3425430)))
24: 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, eval(expr, envir) prior_surv = c(1, 144.927536231884, 1, 144.927536231884,
1, 285.714285714286), block = 3, rand_ratio = c(control = 1, 25: treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, eval(expr, envir) 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, 26: mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168withVisible(eval(expr, envir)) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2,
seed = 3425430)27:
withCallingHandlers(code, error = function (e) 23: rlang::entrace(e), message = function (cnd) do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, { hazard_control = hazard_control_week, N_trials = 20, ncores = 2, watcher$capture_plot_and_output() seed = 3425430))) if (on_message$capture) {
watcher$push(cnd)24: }eval(expr, envir) if (on_message$silence) {
invokeRestart("muffleMessage")25: }eval(expr, envir)}, warning = function (cnd) {
if (getOption("warn") >= 2 || getOption("warn") < 0) {26: return()withVisible(eval(expr, envir)) }
watcher$capture_plot_and_output()27: if (on_warning$capture) {withCallingHandlers(code, error = function (e) cnd <- sanitize_call(cnd)rlang::entrace(e), message = function (cnd) watcher$push(cnd){ } watcher$capture_plot_and_output() if (on_warning$silence) { if (on_message$capture) { invokeRestart("muffleWarning") watcher$push(cnd) } }}, error = function (cnd) if (on_message$silence) {{ invokeRestart("muffleMessage") watcher$capture_plot_and_output() } cnd <- sanitize_call(cnd)}, warning = function (cnd) watcher$push(cnd){ switch(on_error, continue = invokeRestart("eval_continue"), if (getOption("warn") >= 2 || getOption("warn") < 0) { stop = invokeRestart("eval_stop"), error = NULL) return()}) }
watcher$capture_plot_and_output()28: if (on_warning$capture) { cnd <- sanitize_call(cnd)eval(call) watcher$push(cnd)
}29: if (on_warning$silence) {eval(call) invokeRestart("muffleWarning")
}30: }, error = function (cnd) with_handlers({{ for (expr in tle$exprs) { watcher$capture_plot_and_output() ev <- withVisible(eval(expr, envir)) cnd <- sanitize_call(cnd) watcher$push(cnd) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) switch(on_error, continue = invokeRestart("eval_continue"), } stop = invokeRestart("eval_stop"), error = NULL) TRUE})}, handlers)
28: 31: eval(call)
doWithOneRestart(return(expr), restart)
29: 32: eval(call)withOneRestart(expr, restarts[[1L]])
30: 33: with_handlers({withRestartList(expr, restarts[-nr])
for (expr in tle$exprs) {34: ev <- withVisible(eval(expr, envir))doWithOneRestart(return(expr), restart) watcher$capture_plot_and_output()
watcher$print_value(ev$value, ev$visible, envir)35: }withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) TRUE
}, handlers)36:
withRestartList(expr, restarts)31:
doWithOneRestart(return(expr), restart)
37: withRestarts(with_handlers({32: for (expr in tle$exprs) {withOneRestart(expr, restarts[[1L]]) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()33: watcher$print_value(ev$value, ev$visible, envir)withRestartList(expr, restarts[-nr])
}34: TRUEdoWithOneRestart(return(expr), restart)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
35: 38: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])evaluate::evaluate(...)
36:
withRestartList(expr, restarts)39:
evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 37: keep_message = if (is.numeric(options$message)) TRUE else options$message, withRestarts(with_handlers({ stop_on_error = if (is.numeric(options$error)) options$error else { for (expr in tle$exprs) { if (options$error && options$include) ev <- withVisible(eval(expr, envir)) 0L watcher$capture_plot_and_output() else 2L watcher$print_value(ev$value, ev$visible, envir) }, output_handler = knit_handlers(options$render, options)) }
TRUE40: }, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)in_dir(input_dir(), expr)
38: 41: evaluate::evaluate(...)in_input_dir(evaluate(code, envir = env, new_device = FALSE,
keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 39: keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else {evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, if (options$error && options$include) keep_message = if (is.numeric(options$message)) TRUE else options$message, 0L stop_on_error = if (is.numeric(options$error)) options$error else { else 2L if (options$error && options$include) }, output_handler = knit_handlers(options$render, options))) 0L
else 2L42: }, output_handler = knit_handlers(options$render, options))eng_r(options)
40: 43: in_dir(input_dir(), expr)
block_exec(params)41:
in_input_dir(evaluate(code, envir = env, new_device = FALSE, 44: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, call_block(x) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {45: if (options$error && options$include) process_group(group) 0L
else 2L46: }, output_handler = knit_handlers(options$render, options)))
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 42: error = function(e) {eng_r(options) if (progress && is.function(pb$interrupt))
pb$interrupt()43: if (is_R_CMD_build() || is_R_CMD_check()) block_exec(params) error <<- format(e)
})44: call_block(x)
45: 47: process_group(group)with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {46: if (progress && is.function(pb$interrupt)) withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error = function(e) { error <<- format(e) if (progress && is.function(pb$interrupt)) pb$interrupt() }), list(rlang_trace_top_env = knit_global())) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)48: })xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) {
if (progress && is.function(pb$interrupt)) 47: pb$interrupt()with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (is_R_CMD_build() || is_R_CMD_check()) error = function(e) { error <<- format(e) if (progress && is.function(pb$interrupt)) }), list(rlang_trace_top_env = knit_global())), function(loc) { pb$interrupt() setwd(wd) if (is_R_CMD_build() || is_R_CMD_check()) write_utf8(res, output %n% stdout()) error <<- format(e) paste0("\nQuitting from ", loc, if (!is.null(error)) }), list(rlang_trace_top_env = knit_global())) paste0("\n", rule(), error, "\n", rule()))
}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)48:
xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 49: error = function(e) {process_file(text, output) if (progress && is.function(pb$interrupt))
pb$interrupt()50: if (is_R_CMD_build() || is_R_CMD_check()) knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) {51: setwd(wd)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), write_utf8(res, output %n% stdout()) output_dir = getwd(), ...) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))
}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)52: vweave_rmarkdown(...)
49: 53: process_file(text, output)engine$weave(file, quiet = quiet, encoding = enc)
50: 54: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)doTryCatch(return(expr), name, parentenv, handler)
51:
rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 55: output_dir = getwd(), ...)tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: 52: vweave_rmarkdown(...)tryCatchList(expr, classes, parentenv, handlers)
53: 57: engine$weave(file, quiet = quiet, encoding = enc)tryCatch({
54: engine$weave(file, quiet = quiet, encoding = enc)doTryCatch(return(expr), name, parentenv, handler) setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) {55: texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", tryCatchOne(expr, names, parentenv, handlers[[1L]])
engine = engine)56: }tryCatchList(expr, classes, parentenv, handlers)}, error = function(e) {
OK <<- FALSE57: message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", tryCatch({ file, conditionMessage(e))) engine$weave(file, quiet = quiet, encoding = enc)}) setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)58: if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", engine = engine) TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmpt2ZzPd/filecd6558dd3df2.rds") }
}, error = function(e) {An irrecoverable exception occurred. R is aborting now ...
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/Rtmpt2ZzPd/filecd6558dd3df2.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-10-05 16:53:31.962 R[55628:812003] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-05 16:53:33.600 R[55750:812348] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-10-05 16:53:45.431 R[57026:814881] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-05 16:53:47.364 R[57197:815417] 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