Last updated on 2026-10-21 18:51:02 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 25.96 | 544.78 | 570.74 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 19.37 | 385.76 | 405.13 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | 352.05 | 372.05 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 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 | 27.43 | 522.46 | 549.89 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.23 | 545.79 | 571.02 | OK | |
| r-release-macos-arm64 | 1.0.0 | 6.00 | 96.00 | 102.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 20.00 | 473.00 | 493.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 42.00 | 426.00 | 468.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 7.00 | 111.00 | 118.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 55.00 | 585.00 | 640.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpVcajz7/file8d6b3d4bc961.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpVcajz7/file8d6b3d4bc961.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
2026-10-21 22:43:52.438 R[38893:367056] 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))
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$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
Traceback:
1: 21: impute_predictive_arm(time = data_in$time[current_treatment], pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) hazards = treatment_hazards, random_input = random_inputs$current_treatment,
end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
22: 2: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, fill_predictive_imputations(imputations = current, rows = current_treatment, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, values = impute_predictive_arm(time = data_in$time[current_treatment], 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, hazards = treatment_hazards, random_input = random_inputs$current_treatment, treatment = 1), prop_loss = 0, alternative = "greater", end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, 3: N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", impute_predictive_draws(data_in = data_interim, hazards = post_lambda, imputed_final = FALSE, empty_interval = c("prior", "propagate", end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, binary_imputation = predictive_binary_imputation, check_futility = check_futility) Qn = 1, rmst_tau = end_of_study)
{ Call <- match.call() 4: Arguments <- capture_arguments(sim_trials, environment())evaluate_interim_decision(data_interim = data_interim, look = i, method <- normalize_analysis_method(method) Arguments$method <- method planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, empty_interval <- match.arg(empty_interval) active_followup = active_followup_at(data_total, look_time), binary_imputation <- match.arg(binary_imputation) end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, backend <- match.arg(backend) single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, Arguments$empty_interval <- empty_interval prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, Arguments$binary_imputation <- binary_imputation h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, Arguments$backend <- backend N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, requested_backend <- backend method = method, binary_imputation = binary_imputation, check_futility = check_futility, caller_rng_kind <- RNGkind() Qn = Qn[i]) validate_positive_integer_scalar(N_trials, "N_trials")
single_arm <- is.null(hazard_control) 5: if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, rand_ratio, allocation_name = "rand_ratio") cutpoints = cutpoints, N_total = N_total, lambda = lambda, Arguments$rand_ratio <- rand_ratio 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, prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, Arguments$prop_loss <- prop_loss prop_loss = prop_loss, alternative = alternative, h0 = h0, validate_logical_scalar(return_trace, "return_trace") Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, validate_positive_integer_scalar(N_impute, "N_impute") N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, validate_positive_integer_scalar(N_mcmc, "N_mcmc") imputed_final = imputed_final, empty_interval = empty_interval, validate_single_probability(mc_conf_level, "mc_conf_level", return_trace = return_trace, prior_surv_final = prior_surv_final, upper_open = TRUE) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) if (mc_conf_level <= 0.5) {
stop("'mc_conf_level' must be greater than 0.5 and less than 1") 6: } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > doTryCatch(return(expr), name, parentenv, handler) 0), N_impute = N_impute)
if (identical(method, "rmst")) { 7: validate_endpoint_time(end_of_study, cutpoints, "end_of_study")tryCatchOne(expr, names, parentenv, handlers[[1L]]) validate_rmst_args(rmst_tau, end_of_study, h0)
validate_analysis_configuration(method, alternative, 8: is.null(hazard_control), imputed_final) }tryCatchList(expr, classes, parentenv, handlers) validate_positive_integer_scalar(ncores, "ncores")
execution <- resolve_sim_execution(backend = backend, ncores = ncores, 9: N_trials = N_trials)tryCatch({ backend <- execution$backend if (!is.null(trial_streams)) { workers <- execution$workers assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) stream_seed <- NULL } if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, !is.finite(seed) || seed != floor(seed)) { N_total = N_total, lambda = lambda, lambda_time = lambda_time, stop("'seed' must be NULL or a single integer value") } old_kind <- RNGkind() interim_look = interim_look, end_of_study = end_of_study, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, inherits = FALSE) binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, if (old_seed_exists) { h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, old_seed <- get(".Random.seed", envir = .GlobalEnv, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, inherits = FALSE) } method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, on.exit({ generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) do.call(RNGkind, as.list(old_kind)) attr(result, "arguments") <- NULL if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) if (inherits(result, "goldilocks_trial")) { } else if (exists(".Random.seed", envir = .GlobalEnv, attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { inherits = FALSE)) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], rm(".Random.seed", envir = .GlobalEnv) message = conditionMessage(error)) }}) }, add = TRUE)
stream_seed <- seed10: trial_streams <- make_rng_streams(stream_seed, N_trials)FUN(X[[i]], ...) }
else if (backend == "psock") {11: stream_seed <- sample.int(.Machine$integer.max - 1L, lapply(X = S, FUN = FUN, ...) size = 1L)
trial_streams <- make_rng_streams(stream_seed, N_trials)12: }doTryCatch(return(expr), name, parentenv, handler) else {
trial_streams <- NULL13: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {
caller_rng_kind[1] } else {14: "L'Ecuyer-CMRG"tryCatchList(expr, classes, parentenv, handlers)
}, seed_policy = if (!is.null(seed)) {15: "explicit_preserve_caller"tryCatch(expr, error = function(e) { call <- conditionCall(e) } else if (backend == "psock") { if (!is.null(call)) { "caller_derived_psock" } else { if (identical(call[[1L]], quote(doTryCatch))) "caller_state" call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), prefix <- paste("Error in", dcall, ": ") stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { LONG <- 75L make_psock_callable("survival_adapt") sm <- strsplit(conditionMessage(e), "\n")[[1L]] } w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") else { if (is.na(w)) survival_adapt w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") survival_adapt_wrapper <- function(x) { if (w > LONG) tryCatch({ prefix <- paste0(prefix, "\n ") if (!is.null(trial_streams)) { } assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) else prefix <- "Error : " } msg <- paste0(prefix, conditionMessage(e), "\n") result <- survival_adapt_fn(hazard_treatment = hazard_treatment, .Internal(seterrmessage(msg[1L])) 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, if (!silent && isTRUE(getOption("show.error.messages"))) { block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, cat(msg, file = outFile) alternative = alternative, h0 = h0, Fn = Fn, .Internal(printDeferredWarnings()) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, } N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, invisible(structure(msg, class = "try-error", condition = e)) 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)16: attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) {try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
attr(result$summary, "arguments") <- NULL }17: list(trial = as.integer(x), result = result) }, error = function(error) {sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))
}) }18: 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 = {FUN(X[[i]], ...) active_cluster <- make_sim_cluster(workers)
19: on.exit(stop_sim_cluster(active_cluster), add = TRUE)lapply(seq_len(cores), inner.do) initialize_sim_cluster(active_cluster)
run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper)20: })mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, failed <- vapply(trial_results, function(x) is.null(x$result), mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
logical(1))21: failed_results <- trial_results[failed]pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
failures <- data.frame(trial = vapply(failed_results, `[[`, 22: integer(1), "trial"), error_class = vapply(failed_results, (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, `[[`, character(1), "error_class"), message = vapply(failed_results, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, `[[`, character(1), "message"), stringsAsFactors = FALSE) end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, if (all(failed)) { 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, failures$message[1L])) N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", error$failures <- failures imputed_final = FALSE, empty_interval = c("prior", "propagate", class(error) <- c("goldilocks_all_trials_failed", class(error)) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", stop(error) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { } Call <- match.call() successful_trials <- trial_results[!failed] Arguments <- capture_arguments(sim_trials, environment()) successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { method <- normalize_analysis_method(method) Arguments$method <- method sims <- bind_rows(lapply(successful_results, function(x) x$summary)) empty_interval <- match.arg(empty_interval) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { binary_imputation <- match.arg(binary_imputation) trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval trace[c("trial", setdiff(names(trace), "trial"))] Arguments$binary_imputation <- binary_imputation })) Arguments$backend <- backend out <- list(sims = sims, traces = traces, failures = failures, requested_backend <- backend call = Call) caller_rng_kind <- RNGkind() } validate_positive_integer_scalar(N_trials, "N_trials") else { single_arm <- is.null(hazard_control) sims <- bind_rows(successful_results) if (!single_arm) { out <- list(sims = sims, failures = failures, call = Call) rand_ratio <- validate_randomization_args(N_total, block, } rand_ratio, allocation_name = "rand_ratio") attr(out$sims, "arguments") <- NULL Arguments$rand_ratio <- rand_ratio } attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) Arguments$prop_loss <- prop_loss attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) validate_logical_scalar(return_trace, "return_trace") attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments validate_positive_integer_scalar(N_impute, "N_impute") attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, validate_positive_integer_scalar(N_mcmc, "N_mcmc") backend = backend, selection_reason = execution$reason, validate_single_probability(mc_conf_level, "mc_conf_level", requested_ncores = as.integer(ncores), workers = as.integer(workers), upper_open = TRUE) tasks = as.integer(N_trials)) if (mc_conf_level <= 0.5) { if (nrow(failures) > 0L) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", } "for details.", call. = FALSE) validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) } if (identical(method, "rmst")) { return(out)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, validate_endpoint_time(end_of_study, cutpoints, "end_of_study") lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, validate_rmst_args(rmst_tau, end_of_study, h0) 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, validate_analysis_configuration(method, alternative, 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, is.null(hazard_control), imputed_final) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, } validate_positive_integer_scalar(ncores, "ncores") treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, backend <- execution$backend 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, workers <- execution$workers mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 stream_seed <- NULL ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, if (!is.null(seed)) { seed = 3425430) 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") }23: old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, inherits = FALSE) hazard_control = hazard_control_week, N_trials = 20, ncores = 2, if (old_seed_exists) { seed = 3425430))) old_seed <- get(".Random.seed", envir = .GlobalEnv,
inherits = FALSE) }24: on.exit({eval(expr, envir) do.call(RNGkind, as.list(old_kind))
if (old_seed_exists) {25: assign(".Random.seed", old_seed, envir = .GlobalEnv)eval(expr, envir) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) {
rm(".Random.seed", envir = .GlobalEnv) }26: }, add = TRUE)withVisible(eval(expr, envir)) stream_seed <- seed
27: trial_streams <- make_rng_streams(stream_seed, N_trials)withCallingHandlers(code, error = function (e) } else if (backend == "psock") {rlang::entrace(e), message = function (cnd) stream_seed <- sample.int(.Machine$integer.max - 1L, { size = 1L) watcher$capture_plot_and_output() trial_streams <- make_rng_streams(stream_seed, N_trials) if (on_message$capture) { } watcher$push(cnd) else { } if (on_message$silence) { trial_streams <- NULL invokeRestart("muffleMessage") } } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {}, warning = function (cnd) caller_rng_kind[1] } else { "L'Ecuyer-CMRG"{ }, seed_policy = if (!is.null(seed)) { if (getOption("warn") >= 2 || getOption("warn") < 0) { "explicit_preserve_caller" } else if (backend == "psock") { return() "caller_derived_psock" } else { } "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) watcher$capture_plot_and_output() survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") if (on_warning$capture) { } cnd <- sanitize_call(cnd) else { watcher$push(cnd) survival_adapt } } if (on_warning$silence) { survival_adapt_wrapper <- function(x) { invokeRestart("muffleWarning") tryCatch({ } if (!is.null(trial_streams)) {}, error = function (cnd) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv){ watcher$capture_plot_and_output() } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, cnd <- sanitize_call(cnd) hazard_control = hazard_control, cutpoints = cutpoints, watcher$push(cnd) N_total = N_total, lambda = lambda, lambda_time = lambda_time, switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL) interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, }) alternative = alternative, h0 = h0, Fn = Fn,
Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, 28: method = method, imputed_final = imputed_final, eval(call) empty_interval = empty_interval, return_trace = return_trace,
29: prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, eval(call) rmst_tau = rmst_tau)
attr(result, "arguments") <- NULL30: if (inherits(result, "goldilocks_trial")) {with_handlers({ attr(result$summary, "arguments") <- NULL for (expr in tle$exprs) { } ev <- withVisible(eval(expr, envir)) list(trial = as.integer(x), result = result) }, error = function(error) { watcher$capture_plot_and_output() list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], watcher$print_value(ev$value, ev$visible, envir) message = conditionMessage(error)) }) } TRUE }}, handlers) trial_index <- seq_len(N_trials)
trial_results <- switch(backend, sequential = lapply(trial_index, 31: survival_adapt_wrapper), fork = pbmclapply(trial_index, doWithOneRestart(return(expr), restart) survival_adapt_wrapper, mc.cores = workers), psock = {
active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE)32: withOneRestart(expr, restarts[[1L]]) initialize_sim_cluster(active_cluster)
33: run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper)withRestartList(expr, restarts[-nr]) })
failed <- vapply(trial_results, function(x) is.null(x$result), 34: logical(1))doWithOneRestart(return(expr), restart) failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`,
integer(1), "trial"), error_class = vapply(failed_results, 35: `[[`, character(1), "error_class"), message = vapply(failed_results, withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) `[[`, character(1), "message"), stringsAsFactors = FALSE)
if (all(failed)) {36: error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", withRestartList(expr, restarts) failures$message[1L])) error$failures <- failures
37: class(error) <- c("goldilocks_all_trials_failed", class(error))withRestarts(with_handlers({ stop(error) for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) } successful_trials <- trial_results[!failed] watcher$capture_plot_and_output() successful_results <- lapply(successful_trials, `[[`, "result") watcher$print_value(ev$value, ev$visible, envir) if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) } traces <- bind_rows(lapply(seq_along(successful_results), TRUE function(i) { trace <- successful_results[[i]]$trace}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
trial <- successful_trials[[i]]$trial38: evaluate::evaluate(...) trace$trial <- rep.int(trial, nrow(trace))
trace[c("trial", setdiff(names(trace), "trial"))] }))39: out <- list(sims = sims, traces = traces, failures = failures, evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, call = Call) keep_message = if (is.numeric(options$message)) TRUE else options$message, } stop_on_error = if (is.numeric(options$error)) options$error else { else { if (options$error && options$include) sims <- bind_rows(successful_results) 0L out <- list(sims = sims, failures = failures, call = Call) } else 2L attr(out$sims, "arguments") <- NULL }, output_handler = knit_handlers(options$render, options))
attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, 40: end_of_study = end_of_study)in_dir(input_dir(), expr) attr(out, "decision_design") <- attr(successful_results[[1]],
"decision_design", exact = TRUE)41: attr(out, "prior_design") <- attr(successful_results[[1]], in_input_dir(evaluate(code, envir = env, new_device = FALSE, "prior_design", exact = TRUE) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, attr(out, "rng_metadata") <- rng_metadata keep_message = if (is.numeric(options$message)) TRUE else options$message, attr(out, "arguments") <- Arguments stop_on_error = if (is.numeric(options$error)) options$error else { attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, if (options$error && options$include) requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { 0L warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", else 2L "for details.", call. = FALSE) }, output_handler = knit_handlers(options$render, options))) } return(out)
42: })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, eng_r(options) lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333,
8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 43: 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, block_exec(params) prior_surv = c(1, 144.927536231884, 1, 144.927536231884,
1, 285.714285714286), block = 3, rand_ratio = c(control = 1, 44: treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, call_block(x) 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, 45: mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", process_group(group)
imputed_final = FALSE, hazard_treatment = c(0.005796, 0.0016846: ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), seed = 3425430) error = function(e) {
if (progress && is.function(pb$interrupt)) 23: do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, pb$interrupt() hazard_control = hazard_control_week, N_trials = 20, ncores = 2, if (is_R_CMD_build() || is_R_CMD_check()) seed = 3425430))) error <<- format(e) })
24: 47: eval(expr, envir)with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) { if (progress && is.function(pb$interrupt)) 25: pb$interrupt()eval(expr, envir) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)26: }), list(rlang_trace_top_env = knit_global()))
withVisible(eval(expr, envir))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) 27: pb$interrupt()withCallingHandlers(code, error = function (e) if (is_R_CMD_build() || is_R_CMD_check()) rlang::entrace(e), message = function (cnd) error <<- format(e){ watcher$capture_plot_and_output() }), list(rlang_trace_top_env = knit_global())), function(loc) { if (on_message$capture) { setwd(wd) watcher$push(cnd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule())) }}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
if (on_message$silence) { invokeRestart("muffleMessage")49: }}, warning = function (cnd) process_file(text, output){
if (getOption("warn") >= 2 || getOption("warn") < 0) {50: return()knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) }
watcher$capture_plot_and_output()51: if (on_warning$capture) {rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), cnd <- sanitize_call(cnd) output_dir = getwd(), ...) watcher$push(cnd) }
if (on_warning$silence) { invokeRestart("muffleWarning") }52: }, error = function (cnd) vweave_rmarkdown(...)
{53: watcher$capture_plot_and_output()engine$weave(file, quiet = quiet, encoding = enc) cnd <- sanitize_call(cnd)
watcher$push(cnd)54: switch(on_error, continue = invokeRestart("eval_continue"), doTryCatch(return(expr), name, parentenv, handler) stop = invokeRestart("eval_stop"), error = NULL)
})55:
tryCatchOne(expr, names, parentenv, handlers[[1L]])28:
56: eval(call)
tryCatchList(expr, classes, parentenv, handlers)29:
eval(call)57:
tryCatch({30: engine$weave(file, quiet = quiet, encoding = enc)with_handlers({ setwd(startdir) for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) output <- find_vignette_product(name, by = "weave", engine = engine) watcher$capture_plot_and_output() if (!have.makefile && vignette_is_tex(output)) { watcher$print_value(ev$value, ev$visible, envir) } texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", TRUE engine = engine)}, handlers) }
}, error = function(e) { OK <<- FALSE31: message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", doWithOneRestart(return(expr), restart) file, conditionMessage(e)))
})32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])58:
tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 34: TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpVcajz7/file8d6b6332ab61.rds")doWithOneRestart(return(expr), restart)
An irrecoverable exception occurred. R is aborting now ...
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$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("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/RtmpVcajz7/file8d6b6332ab61.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
2026-10-21 22:44:01.161 R[40201:370058] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-21 22:44:04.427 R[40397:370714] 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
2026-10-21 22:44:23.389 R[41688:374323] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-21 22:44:26.074 R[42034:375185] 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