Last updated on 2026-10-19 04:52:14 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 26.64 | 539.10 | 565.74 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 20.20 | 358.42 | 378.62 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | 352.05 | 372.05 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 362.93 | 382.93 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 41.00 | 516.00 | 557.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 27.43 | 522.46 | 549.89 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.23 | 545.79 | 571.02 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 44.00 | 437.00 | 481.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 8.00 | 99.00 | 107.00 | 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 | 587.00 | 639.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmp9MRXPg/filea69a2bd81fb2.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/Rtmp9MRXPg/filea69a2bd81fb2.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
2026-10-19 11:40:32.685 R[47453:381400] 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:
3: 1: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, impute_predictive_arm(time = data_in$time[current_treatment], binary_imputation = predictive_binary_imputation, check_futility = check_futility)
hazards = treatment_hazards, random_input = random_inputs$current_treatment, 4: evaluate_interim_decision(data_interim = data_interim, look = i, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) planned_N = analysis_at_enrollnumber[i], calendar_time = look_time,
active_followup = active_followup_at(data_total, look_time), 2: fill_predictive_imputations(imputations = current, rows = current_treatment, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, values = impute_predictive_arm(time = data_in$time[current_treatment], single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, hazards = treatment_hazards, random_input = random_inputs$current_treatment, 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, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, 3: Qn = Qn[i])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) 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, 4: prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, active_followup = active_followup_at(data_total, look_time), prop_loss = prop_loss, alternative = alternative, h0 = h0, 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, 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, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, imputed_final = imputed_final, empty_interval = empty_interval, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, return_trace = return_trace, prior_surv_final = prior_surv_final, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
Qn = Qn[i]) 6:
doTryCatch(return(expr), name, parentenv, handler) 5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control,
7: cutpoints = cutpoints, N_total = N_total, lambda = lambda, tryCatchOne(expr, names, parentenv, handlers[[1L]]) 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, 8: prop_loss = prop_loss, alternative = alternative, h0 = h0, tryCatchList(expr, classes, parentenv, handlers) 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, 9: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)tryCatch({
if (!is.null(trial_streams)) { 6: doTryCatch(return(expr), name, parentenv, handler) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
8: tryCatchList(expr, classes, parentenv, handlers)
result <- survival_adapt_fn(hazard_treatment = hazard_treatment, 9: hazard_control = hazard_control, cutpoints = cutpoints, tryCatch({ N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (!is.null(trial_streams)) { interim_look = interim_look, end_of_study = end_of_study, 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, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, interim_look = interim_look, end_of_study = end_of_study, binary_imputation = binary_imputation, block = block, 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, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "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, if (inherits(result, "goldilocks_trial")) { return_trace = return_trace, prior_surv_final = prior_surv_final, attr(result$summary, "arguments") <- NULL generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) } attr(result, "arguments") <- NULL list(trial = as.integer(x), result = result) if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL}, error = function(error) { } list(trial = as.integer(x), result = result) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) message = conditionMessage(error))})})
10: 10: FUN(X[[i]], ...)
FUN(X[[i]], ...)11:
lapply(X = S, FUN = FUN, ...)11:
12: lapply(X = S, FUN = FUN, ...)doTryCatch(return(expr), name, parentenv, handler)
13: 12: tryCatchOne(expr, names, parentenv, handlers[[1L]])doTryCatch(return(expr), name, parentenv, handler)
14: 13: tryCatchList(expr, classes, parentenv, handlers)tryCatchOne(expr, names, parentenv, handlers[[1L]])
15: 14: tryCatch(expr, error = function(e) { call <- conditionCall(e)tryCatchList(expr, classes, parentenv, handlers) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 15: call <- sys.call(-4L)tryCatch(expr, error = function(e) { dcall <- deparse(call, nlines = 1L) call <- conditionCall(e) prefix <- paste("Error in", dcall, ": ") if (!is.null(call)) { LONG <- 75L if (identical(call[[1L]], quote(doTryCatch))) sm <- strsplit(conditionMessage(e), "\n")[[1L]] call <- sys.call(-4L) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) dcall <- deparse(call, nlines = 1L) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], prefix <- paste("Error in", dcall, ": ") type = "b") LONG <- 75L if (w > LONG) prefix <- paste0(prefix, "\n ") sm <- strsplit(conditionMessage(e), "\n")[[1L]] } w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") else prefix <- "Error : " if (is.na(w)) msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], if (!silent && isTRUE(getOption("show.error.messages"))) { type = "b") cat(msg, file = outFile) if (w > LONG) .Internal(printDeferredWarnings()) 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]], ...)
19: 16: lapply(seq_len(cores), inner.do)try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
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)
22: 19: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, lapply(seq_len(cores), inner.do) 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",
"fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", 22: "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, 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) Arguments$method <- method h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", empty_interval <- match.arg(empty_interval) imputed_final = FALSE, empty_interval = c("prior", "propagate", binary_imputation <- match.arg(binary_imputation) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", backend <- match.arg(backend) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", Arguments$empty_interval <- empty_interval "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Arguments$binary_imputation <- binary_imputation Qn = 1, rmst_tau = end_of_study) Arguments$backend <- backend{ 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) if (!single_arm) { Arguments$method <- method rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") empty_interval <- match.arg(empty_interval) Arguments$rand_ratio <- rand_ratio binary_imputation <- match.arg(binary_imputation) } backend <- match.arg(backend) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$empty_interval <- empty_interval Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") Arguments$binary_imputation <- binary_imputation validate_positive_integer_scalar(N_impute, "N_impute") Arguments$backend <- backend validate_positive_integer_scalar(N_mcmc, "N_mcmc") requested_backend <- backend validate_single_probability(mc_conf_level, "mc_conf_level", caller_rng_kind <- RNGkind() upper_open = TRUE) validate_positive_integer_scalar(N_trials, "N_trials") if (mc_conf_level <= 0.5) { single_arm <- is.null(hazard_control) stop("'mc_conf_level' must be greater than 0.5 and less than 1") if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, } rand_ratio, allocation_name = "rand_ratio") validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > Arguments$rand_ratio <- rand_ratio 0), N_impute = N_impute) } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) if (identical(method, "rmst")) { Arguments$prop_loss <- prop_loss 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") validate_single_probability(mc_conf_level, "mc_conf_level", is.null(hazard_control), imputed_final) upper_open = TRUE) } if (mc_conf_level <= 0.5) { validate_positive_integer_scalar(ncores, "ncores") stop("'mc_conf_level' must be greater than 0.5 and less than 1") } execution <- resolve_sim_execution(backend = backend, ncores = ncores, validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > N_trials = N_trials) 0), N_impute = N_impute) backend <- execution$backend if (identical(method, "rmst")) { workers <- execution$workers stream_seed <- NULL validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) if (!is.null(seed)) { validate_analysis_configuration(method, alternative, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || is.null(hazard_control), imputed_final) } validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, !is.finite(seed) || seed != floor(seed)) { N_trials = N_trials) stop("'seed' must be NULL or a single integer value") } backend <- execution$backend old_kind <- RNGkind() workers <- execution$workers old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, stream_seed <- NULL inherits = FALSE) if (!is.null(seed)) { if (old_seed_exists) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || old_seed <- get(".Random.seed", envir = .GlobalEnv, !is.finite(seed) || seed != floor(seed)) { inherits = FALSE) stop("'seed' must be NULL or a single integer value") } } on.exit({ old_kind <- RNGkind() do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, assign(".Random.seed", old_seed, envir = .GlobalEnv) inherits = FALSE) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, rm(".Random.seed", envir = .GlobalEnv) inherits = FALSE) } } }, add = TRUE) on.exit({ stream_seed <- seed do.call(RNGkind, as.list(old_kind)) trial_streams <- make_rng_streams(stream_seed, N_trials) if (old_seed_exists) { } else if (backend == "psock") { assign(".Random.seed", old_seed, envir = .GlobalEnv) stream_seed <- sample.int(.Machine$integer.max - 1L, } else if (exists(".Random.seed", envir = .GlobalEnv, size = 1L) inherits = FALSE)) { trial_streams <- make_rng_streams(stream_seed, N_trials) rm(".Random.seed", envir = .GlobalEnv) } } else { }, add = TRUE) trial_streams <- NULL } stream_seed <- seed rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] trial_streams <- make_rng_streams(stream_seed, N_trials) } else { } "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { else if (backend == "psock") { "explicit_preserve_caller" stream_seed <- sample.int(.Machine$integer.max - 1L, } else if (backend == "psock") { size = 1L) "caller_derived_psock" trial_streams <- make_rng_streams(stream_seed, N_trials) } else { } else { "caller_state" trial_streams <- NULL }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] stream_seed = stream_seed) } else { "L'Ecuyer-CMRG" survival_adapt_fn <- if (backend == "psock") { }, seed_policy = if (!is.null(seed)) { make_psock_callable("survival_adapt") "explicit_preserve_caller" } } else if (backend == "psock") { else { "caller_derived_psock" survival_adapt } else { } "caller_state" survival_adapt_wrapper <- function(x) { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), tryCatch({ stream_seed = stream_seed) if (!is.null(trial_streams)) { survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, else { hazard_control = hazard_control, cutpoints = cutpoints, survival_adapt N_total = N_total, lambda = lambda, lambda_time = lambda_time, } interim_look = interim_look, end_of_study = end_of_study, survival_adapt_wrapper <- function(x) { prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, tryCatch({ if (!is.null(trial_streams)) { block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) alternative = alternative, h0 = h0, Fn = Fn, } Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, hazard_control = hazard_control, cutpoints = cutpoints, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, N_total = N_total, lambda = lambda, lambda_time = lambda_time, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, interim_look = interim_look, end_of_study = end_of_study, rmst_tau = rmst_tau) prior_surv = prior_surv, prior_bin = prior_bin, 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$summary, "arguments") <- NULL Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, } method = method, imputed_final = imputed_final, list(trial = as.integer(x), result = result) empty_interval = empty_interval, return_trace = return_trace, }, error = function(error) { prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { message = conditionMessage(error)) attr(result$summary, "arguments") <- NULL }) } } list(trial = as.integer(x), result = result) trial_index <- seq_len(N_trials) }, error = function(error) { trial_results <- switch(backend, sequential = lapply(trial_index, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], survival_adapt_wrapper), fork = pbmclapply(trial_index, message = conditionMessage(error)) survival_adapt_wrapper, mc.cores = workers), psock = { }) active_cluster <- make_sim_cluster(workers) } on.exit(stop_sim_cluster(active_cluster), add = TRUE) trial_index <- seq_len(N_trials) initialize_sim_cluster(active_cluster) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) survival_adapt_wrapper, mc.cores = workers), psock = { }) active_cluster <- make_sim_cluster(workers) failed <- vapply(trial_results, function(x) is.null(x$result), on.exit(stop_sim_cluster(active_cluster), add = TRUE) logical(1)) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) failed_results <- trial_results[failed] }) failures <- data.frame(trial = vapply(failed_results, `[[`, failed <- vapply(trial_results, function(x) is.null(x$result), integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, logical(1)) `[[`, character(1), "message"), stringsAsFactors = FALSE) failed_results <- trial_results[failed] if (all(failed)) { failures <- data.frame(trial = vapply(failed_results, `[[`, error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", integer(1), "trial"), error_class = vapply(failed_results, failures$message[1L])) `[[`, character(1), "error_class"), message = vapply(failed_results, error$failures <- failures `[[`, character(1), "message"), stringsAsFactors = FALSE) class(error) <- c("goldilocks_all_trials_failed", class(error)) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", stop(error) failures$message[1L])) error$failures <- failures } class(error) <- c("goldilocks_all_trials_failed", class(error)) successful_trials <- trial_results[!failed] stop(error) successful_results <- lapply(successful_trials, `[[`, "result") } if (return_trace) { successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") sims <- bind_rows(lapply(successful_results, function(x) x$summary)) if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), traces <- bind_rows(lapply(seq_along(successful_results), function(i) { trace <- successful_results[[i]]$trace function(i) { trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial })) trace$trial <- rep.int(trial, nrow(trace)) out <- list(sims = sims, traces = traces, failures = failures, call = Call) trace[c("trial", setdiff(names(trace), "trial"))] } else { })) sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) out <- list(sims = sims, traces = traces, failures = failures, } attr(out$sims, "arguments") <- NULL call = Call) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, } N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, else { end_of_study = end_of_study) sims <- bind_rows(successful_results) attr(out, "decision_design") <- attr(successful_results[[1]], out <- list(sims = sims, failures = failures, call = Call) "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], } "prior_design", exact = TRUE) attr(out$sims, "arguments") <- NULL attr(out, "rng_metadata") <- rng_metadata attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, attr(out, "arguments") <- Arguments N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, end_of_study = end_of_study) requested_ncores = as.integer(ncores), workers = as.integer(workers), attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", attr(out, "prior_design") <- attr(successful_results[[1]], "for details.", call. = FALSE) "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata } attr(out, "arguments") <- Arguments return(out)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, requested_ncores = as.integer(ncores), workers = as.integer(workers), 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, tasks = as.integer(N_trials)) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, if (nrow(failures) > 0L) { treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 "for details.", call. = FALSE) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, 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))) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333,
prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 24: 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, eval(expr, envir) treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0,
Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, 25: 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, eval(expr, envir) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank",
imputed_final = FALSE, hazard_treatment = c(0.005796, 0.0016826: withVisible(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$capture_plot_and_output() if (on_message$capture) { seed = 3425430))) watcher$push(cnd)
}24: if (on_message$silence) { invokeRestart("muffleMessage")eval(expr, envir) }
}, warning = function (cnd) {25: if (getOption("warn") >= 2 || getOption("warn") < 0) {eval(expr, envir) return() }
watcher$capture_plot_and_output() if (on_warning$capture) {26: cnd <- sanitize_call(cnd)withVisible(eval(expr, envir)) watcher$push(cnd)
} if (on_warning$silence) { invokeRestart("muffleWarning")27: }withCallingHandlers(code, error = function (e) }, error = function (cnd) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output(){ watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) if (on_message$capture) { switch(on_error, continue = invokeRestart("eval_continue"), watcher$push(cnd) stop = invokeRestart("eval_stop"), error = NULL) }}) if (on_message$silence) {
28: invokeRestart("muffleMessage")eval(call)
}29: }, warning = function (cnd) eval(call){
30: if (getOption("warn") >= 2 || getOption("warn") < 0) {with_handlers({ return() } for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() if (on_warning$capture) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$print_value(ev$value, ev$visible, envir) } watcher$push(cnd) TRUE }}, handlers) if (on_warning$silence) {
invokeRestart("muffleWarning")31: doWithOneRestart(return(expr), restart)
}}, error = function (cnd) 32: withOneRestart(expr, restarts[[1L]])
{33: withRestartList(expr, restarts[-nr])
watcher$capture_plot_and_output()34: doWithOneRestart(return(expr), restart)
cnd <- sanitize_call(cnd) watcher$push(cnd)35: switch(on_error, continue = invokeRestart("eval_continue"), withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) stop = invokeRestart("eval_stop"), error = NULL)
36: })
withRestartList(expr, restarts)28:
eval(call)37:
withRestarts(with_handlers({29: for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir))eval(call) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir)
}30: TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)with_handlers({
for (expr in tle$exprs) {38: ev <- withVisible(eval(expr, envir))evaluate::evaluate(...)
39: watcher$capture_plot_and_output()evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, watcher$print_value(ev$value, ev$visible, envir) keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L } else 2L }, output_handler = knit_handlers(options$render, options)) TRUE
}, handlers)40:
in_dir(input_dir(), expr)31:
41: doWithOneRestart(return(expr), restart)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, 32: stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) withOneRestart(expr, restarts[[1L]]) 0L
else 2L }, output_handler = knit_handlers(options$render, options)))33:
withRestartList(expr, restarts[-nr])42:
eng_r(options)
34: doWithOneRestart(return(expr), restart)43: block_exec(params)
35: 44: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])call_block(x)
36: 45: process_group(group)withRestartList(expr, restarts)
46:
37: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) {withRestarts(with_handlers({ if (progress && is.function(pb$interrupt)) pb$interrupt() for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) if (is_R_CMD_build() || is_R_CMD_check()) watcher$capture_plot_and_output() error <<- format(e) }) watcher$print_value(ev$value, ev$visible, envir)
47: }with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), TRUE error = function(e) { if (progress && is.function(pb$interrupt)) }, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)38: }), list(rlang_trace_top_env = knit_global()))evaluate::evaluate(...)
48:
39: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, error = function(e) { keep_message = if (is.numeric(options$message)) TRUE else options$message, if (progress && is.function(pb$interrupt)) pb$interrupt() stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) if (is_R_CMD_build() || is_R_CMD_check()) 0L error <<- format(e) else 2L }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) }, output_handler = knit_handlers(options$render, options)) write_utf8(res, output %n% stdout())
paste0("\nQuitting from ", loc, if (!is.null(error)) 40: paste0("\n", rule(), error, "\n", rule()))in_dir(input_dir(), expr)}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: 41: process_file(text, output)
in_input_dir(evaluate(code, envir = env, new_device = FALSE, 50: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: keep_message = if (is.numeric(options$message)) TRUE else options$message, rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), stop_on_error = if (is.numeric(options$error)) options$error else { output_dir = getwd(), ...) if (options$error && options$include)
52: 0Lvweave_rmarkdown(...)
else 2L53: }, output_handler = knit_handlers(options$render, options)))engine$weave(file, quiet = quiet, encoding = enc)
42: 54: eng_r(options)doTryCatch(return(expr), name, parentenv, handler)
43: 55: block_exec(params)tryCatchOne(expr, names, parentenv, handlers[[1L]])
44: 56: call_block(x)tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({45: engine$weave(file, quiet = quiet, encoding = enc)process_group(group) setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)46: if (!have.makefile && vignette_is_tex(output)) {withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), texi2pdf(file = output, clean = FALSE, quiet = quiet) error = function(e) { output <- find_vignette_product(name, by = "texi2pdf", engine = engine) } if (progress && is.function(pb$interrupt)) }, error = function(e) { pb$interrupt() OK <<- FALSE if (is_R_CMD_build() || is_R_CMD_check()) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))}) error <<- format(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/Rtmp9MRXPg/filea69a68dd77d9.rds")
47: An irrecoverable exception occurred. R is aborting now ...
with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmp9MRXPg/filea69a68dd77d9.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
2026-10-19 11:40:40.267 R[50394:385831] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-19 11:40:42.100 R[51246:387127] 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
2026-10-19 11:40:54.831 R[52987:389983] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-19 11:40:56.833 R[54422:391937] 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