Last updated on 2026-10-24 12:51:33 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 28.40 | 542.16 | 570.56 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 19.07 | 379.77 | 398.84 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 21.00 | 368.08 | 389.08 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 365.48 | 385.48 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 42.00 | 605.00 | 647.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 23.55 | 532.84 | 556.39 | 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 | 41.00 | 430.00 | 471.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 7.00 | 85.00 | 92.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 50.00 | 583.00 | 633.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpAemlT2/file7cae4e41a3bd.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpAemlT2/file7cae4e41a3bd.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
2026-10-24 20:36:20.652 R[34387:358397] XType: Using static font registry.
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
Traceback:
1: 3: impute_predictive_arm(time = data_in$time[current_treatment], impute_predictive_draws(data_in = data_interim, hazards = post_lambda, hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) binary_imputation = predictive_binary_imputation, check_futility = check_futility)
2: 4: fill_predictive_imputations(imputations = current, rows = current_treatment, evaluate_interim_decision(data_interim = data_interim, look = i, values = impute_predictive_arm(time = data_in$time[current_treatment], planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, hazards = treatment_hazards, random_input = random_inputs$current_treatment, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints,
single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, 3: prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, binary_imputation = predictive_binary_imputation, check_futility = check_futility) method = method, binary_imputation = binary_imputation, check_futility = check_futility,
Qn = Qn[i]) 4:
evaluate_interim_decision(data_interim = data_interim, look = i, 5: planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, active_followup = active_followup_at(data_total, look_time), cutpoints = cutpoints, N_total = N_total, lambda = lambda, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, prop_loss = prop_loss, alternative = alternative, h0 = h0, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, method = method, binary_imputation = binary_imputation, check_futility = check_futility, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, Qn = Qn[i]) imputed_final = imputed_final, empty_interval = empty_interval,
return_trace = return_trace, prior_surv_final = prior_surv_final, 5: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control,
cutpoints = cutpoints, N_total = N_total, lambda = lambda, 6: lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, doTryCatch(return(expr), name, parentenv, handler) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method,
binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, 7: prop_loss = prop_loss, alternative = alternative, h0 = h0, tryCatchOne(expr, names, parentenv, handlers[[1L]]) Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute,
N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, 8: imputed_final = imputed_final, empty_interval = empty_interval, tryCatchList(expr, classes, parentenv, handlers) return_trace = return_trace, prior_surv_final = prior_surv_final,
generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) 9:
tryCatch({ 6: if (!is.null(trial_streams)) {doTryCatch(return(expr), name, parentenv, handler) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)
} 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: 14: FUN(X[[i]], ...)tryCatchList(expr, classes, parentenv, handlers)
11: 15: lapply(X = S, FUN = FUN, ...)tryCatch(expr, error = function(e) {
call <- conditionCall(e)12: if (!is.null(call)) {doTryCatch(return(expr), name, parentenv, handler) if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L)13: dcall <- deparse(call, nlines = 1L)tryCatchOne(expr, names, parentenv, handlers[[1L]]) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L14: sm <- strsplit(conditionMessage(e), "\n")[[1L]]tryCatchList(expr, classes, parentenv, handlers) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 15: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], tryCatch(expr, error = function(e) { type = "b") call <- conditionCall(e) if (w > LONG) if (!is.null(call)) { prefix <- paste0(prefix, "\n ") if (identical(call[[1L]], quote(doTryCatch))) } call <- sys.call(-4L) else prefix <- "Error : " dcall <- deparse(call, nlines = 1L) msg <- paste0(prefix, conditionMessage(e), "\n") prefix <- paste("Error in", dcall, ": ") .Internal(seterrmessage(msg[1L])) LONG <- 75L if (!silent && isTRUE(getOption("show.error.messages"))) { sm <- strsplit(conditionMessage(e), "\n")[[1L]] cat(msg, file = outFile) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") .Internal(printDeferredWarnings()) if (is.na(w)) } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], invisible(structure(msg, class = "try-error", condition = e)) type = "b")}) if (w > LONG)
prefix <- paste0(prefix, "\n ")16: }try(lapply(X = S, FUN = FUN, ...), silent = TRUE) else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")17: .Internal(seterrmessage(msg[1L]))sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)18: .Internal(printDeferredWarnings())FUN(X[[i]], ...) }
invisible(structure(msg, class = "try-error", condition = e))19: })lapply(seq_len(cores), inner.do)
16: 20: 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)17:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))21:
pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)18:
FUN(X[[i]], ...)22:
(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, 19: N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, lapply(seq_len(cores), inner.do) 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, 20: treatment = 1), prop_loss = 0, alternative = "greater", mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank",
imputed_final = FALSE, empty_interval = c("prior", "propagate", 21: "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time",
"bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, 22: Qn = 1, rmst_tau = end_of_study) (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, { N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, Call <- match.call() end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, Arguments <- capture_arguments(sim_trials, environment()) 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, method <- normalize_analysis_method(method) treatment = 1), prop_loss = 0, alternative = "greater", Arguments$method <- method h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, empty_interval <- match.arg(empty_interval) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", binary_imputation <- match.arg(binary_imputation) imputed_final = FALSE, empty_interval = c("prior", "propagate", backend <- match.arg(backend) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", Arguments$empty_interval <- empty_interval "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", Arguments$binary_imputation <- binary_imputation "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Arguments$backend <- backend Qn = 1, rmst_tau = end_of_study) requested_backend <- backend{ caller_rng_kind <- RNGkind() Call <- match.call() validate_positive_integer_scalar(N_trials, "N_trials") Arguments <- capture_arguments(sim_trials, environment()) single_arm <- is.null(hazard_control) method <- normalize_analysis_method(method) if (!single_arm) { Arguments$method <- method rand_ratio <- validate_randomization_args(N_total, block, empty_interval <- match.arg(empty_interval) rand_ratio, allocation_name = "rand_ratio") binary_imputation <- match.arg(binary_imputation) Arguments$rand_ratio <- rand_ratio backend <- match.arg(backend) } Arguments$empty_interval <- empty_interval prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$binary_imputation <- binary_imputation Arguments$prop_loss <- prop_loss Arguments$backend <- backend validate_logical_scalar(return_trace, "return_trace") requested_backend <- backend validate_positive_integer_scalar(N_impute, "N_impute") caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_positive_integer_scalar(N_trials, "N_trials") validate_single_probability(mc_conf_level, "mc_conf_level", single_arm <- is.null(hazard_control) upper_open = TRUE) if (!single_arm) { if (mc_conf_level <= 0.5) { rand_ratio <- validate_randomization_args(N_total, block, stop("'mc_conf_level' must be greater than 0.5 and less than 1") } rand_ratio, allocation_name = "rand_ratio") validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > Arguments$rand_ratio <- rand_ratio 0), N_impute = N_impute) } if (identical(method, "rmst")) { prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) validate_endpoint_time(end_of_study, cutpoints, "end_of_study") Arguments$prop_loss <- prop_loss validate_rmst_args(rmst_tau, end_of_study, h0) validate_logical_scalar(return_trace, "return_trace") validate_analysis_configuration(method, alternative, validate_positive_integer_scalar(N_impute, "N_impute") is.null(hazard_control), imputed_final) validate_positive_integer_scalar(N_mcmc, "N_mcmc") } validate_single_probability(mc_conf_level, "mc_conf_level", validate_positive_integer_scalar(ncores, "ncores") upper_open = TRUE) execution <- resolve_sim_execution(backend = backend, ncores = ncores, if (mc_conf_level <= 0.5) { N_trials = N_trials) stop("'mc_conf_level' must be greater than 0.5 and less than 1") backend <- execution$backend } workers <- execution$workers validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > stream_seed <- NULL 0), N_impute = N_impute) if (!is.null(seed)) { if (identical(method, "rmst")) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || validate_endpoint_time(end_of_study, cutpoints, "end_of_study") !is.finite(seed) || seed != floor(seed)) { validate_rmst_args(rmst_tau, end_of_study, h0) stop("'seed' must be NULL or a single integer value") validate_analysis_configuration(method, alternative, } is.null(hazard_control), imputed_final) old_kind <- RNGkind() } old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, validate_positive_integer_scalar(ncores, "ncores") inherits = FALSE) execution <- resolve_sim_execution(backend = backend, ncores = ncores, if (old_seed_exists) { N_trials = N_trials) old_seed <- get(".Random.seed", envir = .GlobalEnv, backend <- execution$backend inherits = FALSE) workers <- execution$workers } stream_seed <- NULL on.exit({ if (!is.null(seed)) { do.call(RNGkind, as.list(old_kind)) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || if (old_seed_exists) { !is.finite(seed) || seed != floor(seed)) { assign(".Random.seed", old_seed, envir = .GlobalEnv) stop("'seed' must be NULL or a single integer value") } else if (exists(".Random.seed", envir = .GlobalEnv, } inherits = FALSE)) { old_kind <- RNGkind() rm(".Random.seed", envir = .GlobalEnv) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, } inherits = FALSE) }, add = TRUE) if (old_seed_exists) { stream_seed <- seed old_seed <- get(".Random.seed", envir = .GlobalEnv, trial_streams <- make_rng_streams(stream_seed, N_trials) inherits = FALSE) } } else if (backend == "psock") { on.exit({ stream_seed <- sample.int(.Machine$integer.max - 1L, do.call(RNGkind, as.list(old_kind)) size = 1L) if (old_seed_exists) { trial_streams <- make_rng_streams(stream_seed, N_trials) assign(".Random.seed", old_seed, envir = .GlobalEnv) } } else if (exists(".Random.seed", envir = .GlobalEnv, else { inherits = FALSE)) { trial_streams <- NULL rm(".Random.seed", envir = .GlobalEnv) } } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { }, add = TRUE) caller_rng_kind[1] stream_seed <- seed } else { trial_streams <- make_rng_streams(stream_seed, N_trials) "L'Ecuyer-CMRG" } }, seed_policy = if (!is.null(seed)) { else if (backend == "psock") { "explicit_preserve_caller" stream_seed <- sample.int(.Machine$integer.max - 1L, } else if (backend == "psock") { size = 1L) "caller_derived_psock" trial_streams <- make_rng_streams(stream_seed, N_trials) } else { } "caller_state" else { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), trial_streams <- NULL stream_seed = stream_seed) } survival_adapt_fn <- if (backend == "psock") { rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { make_psock_callable("survival_adapt") caller_rng_kind[1] } } else { else { "L'Ecuyer-CMRG" survival_adapt }, seed_policy = if (!is.null(seed)) { } "explicit_preserve_caller" survival_adapt_wrapper <- function(x) { } else if (backend == "psock") { tryCatch({ "caller_derived_psock" if (!is.null(trial_streams)) { } else { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) "caller_state" } }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), result <- survival_adapt_fn(hazard_treatment = hazard_treatment, stream_seed = stream_seed) hazard_control = hazard_control, cutpoints = cutpoints, survival_adapt_fn <- if (backend == "psock") { N_total = N_total, lambda = lambda, lambda_time = lambda_time, make_psock_callable("survival_adapt") interim_look = interim_look, end_of_study = end_of_study, } prior_surv = prior_surv, prior_bin = prior_bin, else { bin_method = bin_method, binary_imputation = binary_imputation, survival_adapt block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, } alternative = alternative, h0 = h0, Fn = Fn, survival_adapt_wrapper <- function(x) { Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, tryCatch({ N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, if (!is.null(trial_streams)) { method = method, imputed_final = imputed_final, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) empty_interval = empty_interval, return_trace = return_trace, } prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, rmst_tau = rmst_tau) hazard_control = hazard_control, cutpoints = cutpoints, attr(result, "arguments") <- NULL N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (inherits(result, "goldilocks_trial")) { interim_look = interim_look, end_of_study = end_of_study, attr(result$summary, "arguments") <- NULL prior_surv = prior_surv, prior_bin = prior_bin, } bin_method = bin_method, binary_imputation = binary_imputation, list(trial = as.integer(x), result = result) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, }, error = function(error) { alternative = alternative, h0 = h0, Fn = Fn, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, message = conditionMessage(error)) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }) method = method, imputed_final = imputed_final, } empty_interval = empty_interval, return_trace = return_trace, trial_index <- seq_len(N_trials) prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, trial_results <- switch(backend, sequential = lapply(trial_index, rmst_tau = rmst_tau) survival_adapt_wrapper), fork = pbmclapply(trial_index, attr(result, "arguments") <- NULL survival_adapt_wrapper, mc.cores = workers), psock = { if (inherits(result, "goldilocks_trial")) { active_cluster <- make_sim_cluster(workers) attr(result$summary, "arguments") <- NULL on.exit(stop_sim_cluster(active_cluster), add = TRUE) } initialize_sim_cluster(active_cluster) list(trial = as.integer(x), result = result) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }, error = function(error) { }) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], failed <- vapply(trial_results, function(x) is.null(x$result), message = conditionMessage(error)) logical(1)) }) failed_results <- trial_results[failed] } failures <- data.frame(trial = vapply(failed_results, `[[`, trial_index <- seq_len(N_trials) integer(1), "trial"), error_class = vapply(failed_results, trial_results <- switch(backend, sequential = lapply(trial_index, `[[`, character(1), "error_class"), message = vapply(failed_results, survival_adapt_wrapper), fork = pbmclapply(trial_index, `[[`, character(1), "message"), stringsAsFactors = FALSE) survival_adapt_wrapper, mc.cores = workers), psock = { if (all(failed)) { active_cluster <- make_sim_cluster(workers) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", on.exit(stop_sim_cluster(active_cluster), add = TRUE) failures$message[1L])) initialize_sim_cluster(active_cluster) error$failures <- failures run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) class(error) <- c("goldilocks_all_trials_failed", class(error)) }) stop(error) failed <- vapply(trial_results, function(x) is.null(x$result), } logical(1)) successful_trials <- trial_results[!failed] failed_results <- trial_results[failed] successful_results <- lapply(successful_trials, `[[`, "result") failures <- data.frame(trial = vapply(failed_results, `[[`, if (return_trace) { integer(1), "trial"), error_class = vapply(failed_results, sims <- bind_rows(lapply(successful_results, function(x) x$summary)) `[[`, character(1), "error_class"), message = vapply(failed_results, traces <- bind_rows(lapply(seq_along(successful_results), `[[`, character(1), "message"), stringsAsFactors = FALSE) function(i) { if (all(failed)) { trace <- successful_results[[i]]$trace error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", trial <- successful_trials[[i]]$trial failures$message[1L])) trace$trial <- rep.int(trial, nrow(trace)) error$failures <- failures trace[c("trial", setdiff(names(trace), "trial"))] class(error) <- c("goldilocks_all_trials_failed", class(error)) })) stop(error) out <- list(sims = sims, traces = traces, failures = failures, } call = Call) successful_trials <- trial_results[!failed] } successful_results <- lapply(successful_trials, `[[`, "result") else { if (return_trace) { sims <- bind_rows(successful_results) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) out <- list(sims = sims, failures = failures, call = Call) traces <- bind_rows(lapply(seq_along(successful_results), } function(i) { attr(out$sims, "arguments") <- NULL trace <- successful_results[[i]]$trace attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, trial <- successful_trials[[i]]$trial N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, trace$trial <- rep.int(trial, nrow(trace)) end_of_study = end_of_study) trace[c("trial", setdiff(names(trace), "trial"))] attr(out, "decision_design") <- attr(successful_results[[1]], })) "decision_design", exact = TRUE) out <- list(sims = sims, traces = traces, failures = failures, attr(out, "prior_design") <- attr(successful_results[[1]], call = Call) "prior_design", exact = TRUE) } attr(out, "rng_metadata") <- rng_metadata else { attr(out, "arguments") <- Arguments sims <- bind_rows(successful_results) attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, out <- list(sims = sims, failures = failures, call = Call) backend = backend, selection_reason = execution$reason, } requested_ncores = as.integer(ncores), workers = as.integer(workers), attr(out$sims, "arguments") <- NULL tasks = as.integer(N_trials)) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, if (nrow(failures) > 0L) { N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", end_of_study = end_of_study) "for details.", call. = FALSE) attr(out, "decision_design") <- attr(successful_results[[1]], } "decision_design", exact = TRUE) return(out) attr(out, "prior_design") <- attr(successful_results[[1]], })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, "prior_design", exact = TRUE) lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, attr(out, "rng_metadata") <- rng_metadata 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, attr(out, "arguments") <- Arguments 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, backend = backend, selection_reason = execution$reason, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, requested_ncores = as.integer(ncores), workers = as.integer(workers), treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, tasks = as.integer(N_trials)) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, if (nrow(failures) > 0L) { 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", "for details.", call. = FALSE) imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 } ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, return(out) seed = 3425430)})(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, 23: 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, seed = 3425430))) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1,
treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, 24: Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, eval(expr, envir) 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300,
mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", 25: imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168eval(expr, envir) ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2,
seed = 3425430)26:
withVisible(eval(expr, envir))23:
do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, 27: hazard_control = hazard_control_week, N_trials = 20, ncores = 2, withCallingHandlers(code, error = function (e) seed = 3425430)))rlang::entrace(e), message = function (cnd)
{24: watcher$capture_plot_and_output()eval(expr, envir) if (on_message$capture) {
watcher$push(cnd)25: }eval(expr, envir) if (on_message$silence) {
invokeRestart("muffleMessage")26: }withVisible(eval(expr, envir))}, warning = function (cnd)
{27: if (getOption("warn") >= 2 || getOption("warn") < 0) {withCallingHandlers(code, error = function (e) return()rlang::entrace(e), message = function (cnd) }{ watcher$capture_plot_and_output() watcher$capture_plot_and_output() if (on_warning$capture) { if (on_message$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) watcher$push(cnd) } } if (on_message$silence) { if (on_warning$silence) { invokeRestart("muffleMessage") invokeRestart("muffleWarning") } }}, warning = function (cnd) }, error = function (cnd) {{ if (getOption("warn") >= 2 || getOption("warn") < 0) { watcher$capture_plot_and_output() return() cnd <- sanitize_call(cnd) } watcher$push(cnd) watcher$capture_plot_and_output() switch(on_error, continue = invokeRestart("eval_continue"), if (on_warning$capture) { stop = invokeRestart("eval_stop"), error = NULL) cnd <- sanitize_call(cnd)}) watcher$push(cnd)
}28: if (on_warning$silence) {eval(call) invokeRestart("muffleWarning")
}29: }, error = function (cnd) eval(call){
watcher$capture_plot_and_output()30: cnd <- sanitize_call(cnd)with_handlers({ watcher$push(cnd) for (expr in tle$exprs) { switch(on_error, continue = invokeRestart("eval_continue"), ev <- withVisible(eval(expr, envir)) stop = invokeRestart("eval_stop"), error = NULL) watcher$capture_plot_and_output()}) watcher$print_value(ev$value, ev$visible, envir)
}28: TRUEeval(call)}, handlers)
29: 31: eval(call)doWithOneRestart(return(expr), restart)
30: 32: with_handlers({withOneRestart(expr, restarts[[1L]]) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))33: watcher$capture_plot_and_output()withRestartList(expr, restarts[-nr]) watcher$print_value(ev$value, ev$visible, envir)
34: }doWithOneRestart(return(expr), restart) TRUE
}, handlers)35:
withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])31:
doWithOneRestart(return(expr), restart)36:
withRestartList(expr, restarts)32:
withOneRestart(expr, restarts[[1L]])37:
withRestarts(with_handlers({33: for (expr in tle$exprs) {withRestartList(expr, restarts[-nr]) ev <- withVisible(eval(expr, envir))
34: watcher$capture_plot_and_output()doWithOneRestart(return(expr), restart) watcher$print_value(ev$value, ev$visible, envir)
}35: TRUEwithOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
36: 38: withRestartList(expr, restarts)evaluate::evaluate(...)
37: 39: withRestarts(with_handlers({evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, for (expr in tle$exprs) { keep_message = if (is.numeric(options$message)) TRUE else options$message, ev <- withVisible(eval(expr, envir)) stop_on_error = if (is.numeric(options$error)) options$error else { watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } if (options$error && options$include) TRUE 0L}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
else 2L38: }, output_handler = knit_handlers(options$render, options))evaluate::evaluate(...)
40:
in_dir(input_dir(), expr)39:
41: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_message = if (is.numeric(options$message)) TRUE else options$message, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, stop_on_error = if (is.numeric(options$error)) options$error else { keep_message = if (is.numeric(options$message)) TRUE else options$message, if (options$error && options$include) stop_on_error = if (is.numeric(options$error)) options$error else { 0L if (options$error && options$include) 0L else 2L else 2L }, output_handler = knit_handlers(options$render, options)) }, output_handler = knit_handlers(options$render, options)))
42: 40: in_dir(input_dir(), expr)eng_r(options)
41: 43: block_exec(params)in_input_dir(evaluate(code, envir = env, new_device = FALSE,
44: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, call_block(x) keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 45: 0Lprocess_group(group)
else 2L46: }, output_handler = knit_handlers(options$render, options)))withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) {
if (progress && is.function(pb$interrupt)) 42: pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e)eng_r(options)
})
43: 47: block_exec(params)
with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 44: error = function(e) { if (progress && is.function(pb$interrupt)) call_block(x) pb$interrupt()
45: if (is_R_CMD_build() || is_R_CMD_check()) process_group(group) error <<- format(e)
46: }), list(rlang_trace_top_env = knit_global()))withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
48: error = function(e) {xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (progress && is.function(pb$interrupt)) error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) pb$interrupt() error <<- format(e) if (is_R_CMD_build() || is_R_CMD_check()) })
error <<- format(e)47: }), list(rlang_trace_top_env = knit_global())), function(loc) {with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), setwd(wd) error = function(e) { write_utf8(res, output %n% stdout()) if (progress && is.function(pb$interrupt)) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule())) pb$interrupt()}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
if (is_R_CMD_build() || is_R_CMD_check()) 49: error <<- format(e)process_file(text, output)
}), list(rlang_trace_top_env = knit_global()))50:
knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)48:
xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 51: error = function(e) { if (progress && is.function(pb$interrupt)) rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), pb$interrupt() output_dir = getwd(), ...)
if (is_R_CMD_build() || is_R_CMD_check()) 52: error <<- format(e)vweave_rmarkdown(...)
}), list(rlang_trace_top_env = knit_global())), function(loc) {53: setwd(wd)engine$weave(file, quiet = quiet, encoding = enc)
write_utf8(res, output %n% stdout())54: paste0("\nQuitting from ", loc, if (!is.null(error)) doTryCatch(return(expr), name, parentenv, handler)
paste0("\n", rule(), error, "\n", rule()))55: }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: 49: tryCatchList(expr, classes, parentenv, handlers)
process_file(text, output)57:
tryCatch({ engine$weave(file, quiet = quiet, encoding = enc)50: setwd(startdir)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {51: texi2pdf(file = output, clean = FALSE, quiet = quiet)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output <- find_vignette_product(name, by = "texi2pdf", output_dir = getwd(), ...) engine = engine)
}52: }, error = function(e) {vweave_rmarkdown(...)
53: OK <<- FALSEengine$weave(file, quiet = quiet, encoding = enc) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
54: file, conditionMessage(e)))doTryCatch(return(expr), name, parentenv, handler)})
55:
tryCatchOne(expr, names, parentenv, handlers[[1L]])58:
tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 56: TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpAemlT2/file7cae16e34d88.rds")tryCatchList(expr, classes, parentenv, handlers)
An irrecoverable exception occurred. R is aborting now ...
57: tryCatch({ engine$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/RtmpAemlT2/file7cae16e34d88.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
2026-10-24 20:36:26.671 R[34889:359510] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-24 20:36:28.311 R[34989:359766] 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
2026-10-24 20:36:39.280 R[38294:364782] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-24 20:36:40.940 R[39249:366239] 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