Last updated on 2026-11-07 07:50:52 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 28.31 | 548.80 | 577.11 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.57 | 367.04 | 385.61 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 364.34 | 384.34 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 44.00 | 514.00 | 558.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | OK | ||||
| r-release-linux-x86_64 | 1.0.0 | 25.40 | 524.19 | 549.59 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 44.00 | 428.00 | 472.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | ERROR | ||||
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 60.00 | 591.00 | 651.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmp7tamQf/filea11315590d21.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/Rtmp7tamQf/filea11315590d21.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-11-07 14:08:14.718 R[45239:373779] 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_draws(data_in = data_interim, hazards = post_lambda, impute_predictive_arm(time = data_in$time[current_treatment], end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, hazards = treatment_hazards, random_input = random_inputs$current_treatment, binary_imputation = predictive_binary_imputation, check_futility = check_futility) end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
4: 2: evaluate_interim_decision(data_interim = data_interim, look = i, fill_predictive_imputations(imputations = current, rows = current_treatment, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, values = impute_predictive_arm(time = data_in$time[current_treatment], active_followup = active_followup_at(data_total, look_time), hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final,
prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, 3: h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, method = method, binary_imputation = binary_imputation, check_futility = check_futility, binary_imputation = predictive_binary_imputation, check_futility = check_futility) Qn = Qn[i])
4: 5: evaluate_interim_decision(data_interim = data_interim, look = i, 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, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, method = method, binary_imputation = binary_imputation, check_futility = check_futility, imputed_final = imputed_final, empty_interval = empty_interval, Qn = Qn[i]) return_trace = return_trace, prior_surv_final = prior_surv_final,
generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) 5:
survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, 6: cutpoints = cutpoints, N_total = N_total, lambda = lambda, 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, 7: binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, tryCatchOne(expr, names, parentenv, handlers[[1L]]) prop_loss = prop_loss, alternative = alternative, h0 = h0,
Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, 8: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, tryCatchList(expr, classes, parentenv, handlers) 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({
6: if (!is.null(trial_streams)) {doTryCatch(return(expr), name, parentenv, handler) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)
} 7: result <- survival_adapt_fn(hazard_treatment = hazard_treatment, tryCatchOne(expr, names, parentenv, handlers[[1L]]) hazard_control = hazard_control, cutpoints = cutpoints,
N_total = N_total, lambda = lambda, lambda_time = lambda_time, 8: interim_look = interim_look, end_of_study = end_of_study, tryCatchList(expr, classes, parentenv, handlers) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method,
binary_imputation = binary_imputation, block = block, 9: rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, tryCatch({ h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, if (!is.null(trial_streams)) { N_impute = N_impute, 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, prior_surv_final = prior_surv_final, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) hazard_control = hazard_control, cutpoints = cutpoints, attr(result, "arguments") <- NULL N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (inherits(result, "goldilocks_trial")) { interim_look = interim_look, end_of_study = end_of_study, attr(result$summary, "arguments") <- NULL prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, } binary_imputation = binary_imputation, block = block, list(trial = as.integer(x), result = result) rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, }, error = function(error) { h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, message = conditionMessage(error)) method = method, imputed_final = imputed_final, empty_interval = empty_interval, }) return_trace = return_trace, prior_surv_final = prior_surv_final,
generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)10: attr(result, "arguments") <- NULLFUN(X[[i]], ...) if (inherits(result, "goldilocks_trial")) {
attr(result$summary, "arguments") <- NULL11: }lapply(X = S, FUN = FUN, ...) list(trial = as.integer(x), result = result)
}, error = function(error) {12: list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], doTryCatch(return(expr), name, parentenv, handler) message = conditionMessage(error))
})13:
tryCatchOne(expr, names, parentenv, handlers[[1L]])10:
FUN(X[[i]], ...)14:
tryCatchList(expr, classes, parentenv, handlers)11:
lapply(X = S, FUN = FUN, ...)15:
tryCatch(expr, error = function(e) {12: call <- conditionCall(e)doTryCatch(return(expr), name, parentenv, handler) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 13: call <- sys.call(-4L)tryCatchOne(expr, names, parentenv, handlers[[1L]]) dcall <- deparse(call, nlines = 1L)
prefix <- paste("Error in", dcall, ": ")14: LONG <- 75LtryCatchList(expr, classes, parentenv, handlers) sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")15: if (is.na(w)) tryCatch(expr, error = function(e) { w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], call <- conditionCall(e) type = "b") if (!is.null(call)) { if (w > LONG) if (identical(call[[1L]], quote(doTryCatch))) prefix <- paste0(prefix, "\n ") call <- sys.call(-4L) } dcall <- deparse(call, nlines = 1L) else prefix <- "Error : " prefix <- paste("Error in", dcall, ": ") msg <- paste0(prefix, conditionMessage(e), "\n") LONG <- 75L .Internal(seterrmessage(msg[1L])) sm <- strsplit(conditionMessage(e), "\n")[[1L]] if (!silent && isTRUE(getOption("show.error.messages"))) { w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") cat(msg, file = outFile) if (is.na(w)) .Internal(printDeferredWarnings()) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") } if (w > LONG) invisible(structure(msg, class = "try-error", condition = e)) prefix <- paste0(prefix, "\n ")}) }
else prefix <- "Error : "16: msg <- paste0(prefix, conditionMessage(e), "\n")try(lapply(X = S, FUN = FUN, ...), silent = TRUE) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {17: cat(msg, file = outFile)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) .Internal(printDeferredWarnings())
}18: invisible(structure(msg, class = "try-error", condition = e))FUN(X[[i]], ...)})
19: 16: lapply(seq_len(cores), inner.do)
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)20:
mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, 17: mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
21: 18: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)FUN(X[[i]], ...)
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",
22: "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, Qn = 1, rmst_tau = end_of_study) 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, Call <- match.call() treatment = 1), prop_loss = 0, alternative = "greater", Arguments <- capture_arguments(sim_trials, environment()) h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, method <- normalize_analysis_method(method) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", Arguments$method <- method imputed_final = FALSE, empty_interval = c("prior", "propagate", empty_interval <- match.arg(empty_interval) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", binary_imputation <- match.arg(binary_imputation) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", backend <- match.arg(backend) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Arguments$empty_interval <- empty_interval Qn = 1, rmst_tau = end_of_study) Arguments$binary_imputation <- binary_imputation{ Arguments$backend <- backend Call <- match.call() requested_backend <- backend Arguments <- capture_arguments(sim_trials, environment()) caller_rng_kind <- RNGkind() method <- normalize_analysis_method(method) validate_positive_integer_scalar(N_trials, "N_trials") Arguments$method <- method single_arm <- is.null(hazard_control) if (!single_arm) { empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) rand_ratio <- validate_randomization_args(N_total, block, backend <- match.arg(backend) rand_ratio, allocation_name = "rand_ratio") Arguments$empty_interval <- empty_interval Arguments$rand_ratio <- rand_ratio Arguments$binary_imputation <- binary_imputation } Arguments$backend <- backend prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) requested_backend <- backend Arguments$prop_loss <- prop_loss caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") validate_logical_scalar(return_trace, "return_trace") single_arm <- is.null(hazard_control) validate_positive_integer_scalar(N_impute, "N_impute") if (!single_arm) { validate_positive_integer_scalar(N_mcmc, "N_mcmc") rand_ratio <- validate_randomization_args(N_total, block, validate_single_probability(mc_conf_level, "mc_conf_level", rand_ratio, allocation_name = "rand_ratio") upper_open = TRUE) Arguments$rand_ratio <- rand_ratio if (mc_conf_level <= 0.5) { } stop("'mc_conf_level' must be greater than 0.5 and less than 1") prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) } Arguments$prop_loss <- prop_loss validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > validate_logical_scalar(return_trace, "return_trace") 0), N_impute = N_impute) validate_positive_integer_scalar(N_impute, "N_impute") if (identical(method, "rmst")) { validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_single_probability(mc_conf_level, "mc_conf_level", validate_rmst_args(rmst_tau, end_of_study, h0) upper_open = TRUE) validate_analysis_configuration(method, alternative, if (mc_conf_level <= 0.5) { is.null(hazard_control), imputed_final) stop("'mc_conf_level' must be greater than 0.5 and less than 1") } } validate_positive_integer_scalar(ncores, "ncores") validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > execution <- resolve_sim_execution(backend = backend, ncores = ncores, 0), N_impute = N_impute) N_trials = N_trials) if (identical(method, "rmst")) { backend <- execution$backend validate_endpoint_time(end_of_study, cutpoints, "end_of_study") workers <- execution$workers validate_rmst_args(rmst_tau, end_of_study, h0) stream_seed <- NULL validate_analysis_configuration(method, alternative, if (!is.null(seed)) { is.null(hazard_control), imputed_final) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || } !is.finite(seed) || seed != floor(seed)) { validate_positive_integer_scalar(ncores, "ncores") stop("'seed' must be NULL or a single integer value") execution <- resolve_sim_execution(backend = backend, ncores = ncores, } N_trials = N_trials) old_kind <- RNGkind() backend <- execution$backend old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, workers <- execution$workers inherits = FALSE) stream_seed <- NULL if (old_seed_exists) { if (!is.null(seed)) { old_seed <- get(".Random.seed", envir = .GlobalEnv, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || inherits = FALSE) !is.finite(seed) || seed != floor(seed)) { } stop("'seed' must be NULL or a single integer value") on.exit({ } do.call(RNGkind, as.list(old_kind)) old_kind <- RNGkind() 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, if (old_seed_exists) { inherits = FALSE)) { 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) { } assign(".Random.seed", old_seed, envir = .GlobalEnv) else if (backend == "psock") { } else if (exists(".Random.seed", envir = .GlobalEnv, stream_seed <- sample.int(.Machine$integer.max - 1L, inherits = FALSE)) { size = 1L) rm(".Random.seed", envir = .GlobalEnv) trial_streams <- make_rng_streams(stream_seed, N_trials) } } }, add = TRUE) else { stream_seed <- seed trial_streams <- NULL trial_streams <- make_rng_streams(stream_seed, N_trials) } } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { else if (backend == "psock") { caller_rng_kind[1] stream_seed <- sample.int(.Machine$integer.max - 1L, } else { size = 1L) "L'Ecuyer-CMRG" trial_streams <- make_rng_streams(stream_seed, N_trials) }, seed_policy = if (!is.null(seed)) { } "explicit_preserve_caller" else { } else if (backend == "psock") { trial_streams <- NULL "caller_derived_psock" } } else { rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { "caller_state" caller_rng_kind[1] }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), } else { stream_seed = stream_seed) "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") { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) make_psock_callable("survival_adapt") } } 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, tryCatch({ bin_method = bin_method, binary_imputation = binary_imputation, 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, N_total = N_total, lambda = lambda, lambda_time = lambda_time, empty_interval = empty_interval, return_trace = return_trace, interim_look = interim_look, end_of_study = end_of_study, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, prior_surv = prior_surv, prior_bin = prior_bin, rmst_tau = rmst_tau) bin_method = bin_method, binary_imputation = binary_imputation, attr(result, "arguments") <- NULL block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, if (inherits(result, "goldilocks_trial")) { alternative = alternative, h0 = h0, Fn = Fn, attr(result$summary, "arguments") <- NULL Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, } N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, list(trial = as.integer(x), result = result) method = method, imputed_final = imputed_final, }, error = function(error) { empty_interval = empty_interval, return_trace = return_trace, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, message = conditionMessage(error)) rmst_tau = rmst_tau) }) attr(result, "arguments") <- NULL } if (inherits(result, "goldilocks_trial")) { trial_index <- seq_len(N_trials) attr(result$summary, "arguments") <- NULL trial_results <- switch(backend, sequential = lapply(trial_index, } survival_adapt_wrapper), fork = pbmclapply(trial_index, list(trial = as.integer(x), result = result) survival_adapt_wrapper, mc.cores = workers), psock = { }, error = function(error) { active_cluster <- make_sim_cluster(workers) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], on.exit(stop_sim_cluster(active_cluster), add = TRUE) message = conditionMessage(error)) initialize_sim_cluster(active_cluster) }) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) } }) trial_index <- seq_len(N_trials) failed <- vapply(trial_results, function(x) is.null(x$result), trial_results <- switch(backend, sequential = lapply(trial_index, logical(1)) survival_adapt_wrapper), fork = pbmclapply(trial_index, failed_results <- trial_results[failed] survival_adapt_wrapper, mc.cores = workers), psock = { failures <- data.frame(trial = vapply(failed_results, `[[`, active_cluster <- make_sim_cluster(workers) integer(1), "trial"), error_class = vapply(failed_results, on.exit(stop_sim_cluster(active_cluster), add = TRUE) `[[`, character(1), "error_class"), message = vapply(failed_results, initialize_sim_cluster(active_cluster) `[[`, character(1), "message"), stringsAsFactors = FALSE) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) if (all(failed)) { }) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failed <- vapply(trial_results, function(x) is.null(x$result), failures$message[1L])) logical(1)) error$failures <- failures failed_results <- trial_results[failed] class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) failures <- data.frame(trial = vapply(failed_results, `[[`, } integer(1), "trial"), error_class = vapply(failed_results, successful_trials <- trial_results[!failed] `[[`, character(1), "error_class"), message = vapply(failed_results, successful_results <- lapply(successful_trials, `[[`, "result") `[[`, character(1), "message"), stringsAsFactors = FALSE) if (return_trace) { if (all(failed)) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) traces <- bind_rows(lapply(seq_along(successful_results), error$failures <- failures function(i) { class(error) <- c("goldilocks_all_trials_failed", class(error)) trace <- successful_results[[i]]$trace stop(error) trial <- successful_trials[[i]]$trial } trace$trial <- rep.int(trial, nrow(trace)) successful_trials <- trial_results[!failed] trace[c("trial", setdiff(names(trace), "trial"))] successful_results <- lapply(successful_trials, `[[`, "result") })) if (return_trace) { out <- list(sims = sims, traces = traces, failures = failures, sims <- bind_rows(lapply(successful_results, function(x) x$summary)) call = Call) traces <- bind_rows(lapply(seq_along(successful_results), } function(i) { else { trace <- successful_results[[i]]$trace sims <- bind_rows(successful_results) trial <- successful_trials[[i]]$trial out <- list(sims = sims, failures = failures, call = Call) trace$trial <- rep.int(trial, nrow(trace)) } trace[c("trial", setdiff(names(trace), "trial"))] attr(out$sims, "arguments") <- NULL })) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, out <- list(sims = sims, traces = traces, failures = failures, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, call = Call) end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], } "decision_design", exact = TRUE) else { attr(out, "prior_design") <- attr(successful_results[[1]], sims <- bind_rows(successful_results) "prior_design", exact = TRUE) out <- list(sims = sims, failures = failures, call = Call) attr(out, "rng_metadata") <- rng_metadata } attr(out, "arguments") <- Arguments attr(out$sims, "arguments") <- NULL attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, backend = backend, selection_reason = execution$reason, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, requested_ncores = as.integer(ncores), workers = as.integer(workers), end_of_study = end_of_study) tasks = as.integer(N_trials)) attr(out, "decision_design") <- attr(successful_results[[1]], if (nrow(failures) > 0L) { "decision_design", exact = TRUE) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", attr(out, "prior_design") <- attr(successful_results[[1]], "for details.", call. = FALSE) "prior_design", exact = TRUE) } attr(out, "rng_metadata") <- rng_metadata return(out) attr(out, "arguments") <- Arguments})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, backend = backend, selection_reason = execution$reason, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, requested_ncores = as.integer(ncores), workers = as.integer(workers), 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, tasks = as.integer(N_trials)) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, if (nrow(failures) > 0L) { 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, "for details.", call. = FALSE) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, } 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, return(out) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, seed = 3425430) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333,
prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 23: 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, seed = 3425430))) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank",
imputed_final = FALSE, hazard_treatment = c(0.005796, 0.0016824: ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, eval(expr, envir) seed = 3425430)
25: 23: eval(expr, envir)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week,
hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 26: seed = 3425430)))withVisible(eval(expr, envir))
24: 27: eval(expr, envir)withCallingHandlers(code, error = function (e)
rlang::entrace(e), message = function (cnd) 25: {eval(expr, envir) watcher$capture_plot_and_output()
if (on_message$capture) {26: watcher$push(cnd)withVisible(eval(expr, envir)) }
if (on_message$silence) {27: invokeRestart("muffleMessage")withCallingHandlers(code, error = function (e) }rlang::entrace(e), message = function (cnd) }, warning = function (cnd) {{ watcher$capture_plot_and_output() if (getOption("warn") >= 2 || getOption("warn") < 0) { if (on_message$capture) { return() watcher$push(cnd) } } watcher$capture_plot_and_output() if (on_message$silence) { if (on_warning$capture) { invokeRestart("muffleMessage") cnd <- sanitize_call(cnd) } watcher$push(cnd)}, warning = function (cnd) }{ if (on_warning$silence) { if (getOption("warn") >= 2 || getOption("warn") < 0) { invokeRestart("muffleWarning") return() } }}, error = function (cnd) watcher$capture_plot_and_output(){ if (on_warning$capture) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) cnd <- sanitize_call(cnd) watcher$push(cnd) watcher$push(cnd) } switch(on_error, continue = invokeRestart("eval_continue"), if (on_warning$silence) { stop = invokeRestart("eval_stop"), error = NULL)}) invokeRestart("muffleWarning")
}28: }, error = function (cnd) eval(call){
watcher$capture_plot_and_output()29: cnd <- sanitize_call(cnd)eval(call) watcher$push(cnd)
switch(on_error, continue = invokeRestart("eval_continue"), 30: stop = invokeRestart("eval_stop"), error = NULL)with_handlers({}) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))28: watcher$capture_plot_and_output()eval(call) watcher$print_value(ev$value, ev$visible, envir)
}29: TRUEeval(call)}, handlers)
30: 31: with_handlers({doWithOneRestart(return(expr), restart) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))32: watcher$capture_plot_and_output()withOneRestart(expr, restarts[[1L]]) watcher$print_value(ev$value, ev$visible, envir)
}33: TRUEwithRestartList(expr, restarts[-nr])}, handlers)
34: 31: doWithOneRestart(return(expr), restart)doWithOneRestart(return(expr), restart)
35: 32: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])withOneRestart(expr, restarts[[1L]])
36: 33: withRestartList(expr, restarts)withRestartList(expr, restarts[-nr])
37: 34: withRestarts(with_handlers({doWithOneRestart(return(expr), restart) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))35: watcher$capture_plot_and_output()withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) watcher$print_value(ev$value, ev$visible, envir)
}36: TRUEwithRestartList(expr, restarts)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
37: 38: withRestarts(with_handlers({evaluate::evaluate(...) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))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 { TRUE if (options$error && options$include) }, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) 0L
else 2L38: }, output_handler = knit_handlers(options$render, options))evaluate::evaluate(...)
40: 39: in_dir(input_dir(), expr)evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning,
keep_message = if (is.numeric(options$message)) TRUE else options$message, 41: stop_on_error = if (is.numeric(options$error)) options$error else {in_input_dir(evaluate(code, envir = env, new_device = FALSE, if (options$error && options$include) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 0L keep_message = if (is.numeric(options$message)) TRUE else options$message, else 2L stop_on_error = if (is.numeric(options$error)) options$error else { }, output_handler = knit_handlers(options$render, options)) if (options$error && options$include)
0L40: else 2Lin_dir(input_dir(), expr) }, output_handler = knit_handlers(options$render, options)))
41: 42: in_input_dir(evaluate(code, envir = env, new_device = FALSE, eng_r(options) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning,
keep_message = if (is.numeric(options$message)) TRUE else options$message, 43: stop_on_error = if (is.numeric(options$error)) options$error else {block_exec(params) if (options$error && options$include)
0L44: else 2Lcall_block(x) }, output_handler = knit_handlers(options$render, options)))
45: 42: process_group(group)eng_r(options)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 43: error = function(e) { if (progress && is.function(pb$interrupt)) block_exec(params) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 44: error <<- format(e)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() error = function(e) { if (is_R_CMD_build() || is_R_CMD_check()) if (progress && is.function(pb$interrupt)) error <<- format(e) pb$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) {47: if (progress && is.function(pb$interrupt)) with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), pb$interrupt() error = function(e) { if (is_R_CMD_build() || is_R_CMD_check()) if (progress && is.function(pb$interrupt)) error <<- format(e) pb$interrupt() }), list(rlang_trace_top_env = knit_global())), function(loc) { if (is_R_CMD_build() || is_R_CMD_check()) setwd(wd) error <<- format(e) write_utf8(res, output %n% stdout()) }), list(rlang_trace_top_env = knit_global())) paste0("\nQuitting from ", loc, if (!is.null(error))
paste0("\n", rule(), error, "\n", rule()))48: }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {49: if (progress && is.function(pb$interrupt)) process_file(text, output) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 50: error <<- format(e)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) }), list(rlang_trace_top_env = knit_global())), function(loc) {
setwd(wd)51: write_utf8(res, output %n% stdout())rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), paste0("\nQuitting from ", loc, if (!is.null(error)) output_dir = getwd(), ...) 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$weave(file, quiet = quiet, encoding = enc)
50: 54: doTryCatch(return(expr), name, parentenv, handler)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
55: 51: tryCatchOne(expr, names, parentenv, handlers[[1L]])rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)56:
tryCatchList(expr, classes, parentenv, handlers)52:
vweave_rmarkdown(...)57:
tryCatch({53: engine$weave(file, quiet = quiet, encoding = enc)engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)54: if (!have.makefile && vignette_is_tex(output)) {doTryCatch(return(expr), name, parentenv, handler) texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf", 55: engine = engine)tryCatchOne(expr, names, parentenv, handlers[[1L]]) }
}, error = function(e) {56: OK <<- FALSEtryCatchList(expr, classes, parentenv, handlers) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))57: })tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)58: setwd(startdir)tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", output <- find_vignette_product(name, by = "weave", engine = engine) TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmp7tamQf/filea1134006b8f6.rds") if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)An irrecoverable exception occurred. R is aborting now ...
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/Rtmp7tamQf/filea1134006b8f6.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-11-07 14:08:21.284 R[47629:377625] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-11-07 14:08:23.144 R[49037:379623] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-11-07 14:08:34.847 R[51009:382930] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-11-07 14:08:36.898 R[52387:385028] 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