Last updated on 2026-10-29 01:57:54 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 26.38 | 538.00 | 564.38 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 19.53 | 377.57 | 397.10 | 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 | 360.17 | 380.17 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 44.00 | 514.00 | 558.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 23.55 | 532.84 | 556.39 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.07 | 526.02 | 551.09 | 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 | 48.00 | 433.00 | 481.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 8.00 | 89.00 | 97.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 54.00 | 578.00 | 632.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpXlZ71m/file660b478d7764.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpXlZ71m/file660b478d7764.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
2026-10-29 13:13:10.054 R[26780:342170] 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$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L
Traceback:
1: sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, if (is.na(w)) end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b") 2: if (w > LONG) fill_predictive_imputations(imputations = current, rows = current_treatment, prefix <- paste0(prefix, "\n ") values = impute_predictive_arm(time = data_in$time[current_treatment], } hazards = treatment_hazards, random_input = random_inputs$current_treatment, else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) .Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) { 3: cat(msg, file = outFile)impute_predictive_draws(data_in = data_interim, hazards = post_lambda, .Internal(printDeferredWarnings()) end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, } binary_imputation = predictive_binary_imputation, check_futility = check_futility) invisible(structure(msg, class = "try-error", condition = e))
}) 4:
evaluate_interim_decision(data_interim = data_interim, look = i, 16: planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, try(lapply(X = S, FUN = FUN, ...), silent = TRUE) active_followup = active_followup_at(data_total, look_time),
end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, 17: single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) 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, 18: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, FUN(X[[i]], ...) method = method, binary_imputation = binary_imputation, check_futility = check_futility,
Qn = Qn[i])19:
lapply(seq_len(cores), inner.do) 5:
survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, 20: cutpoints = cutpoints, N_total = N_total, lambda = lambda, mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method,
binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, 21: prop_loss = prop_loss, alternative = alternative, h0 = h0, pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) 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, 22: imputed_final = imputed_final, empty_interval = empty_interval, (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, return_trace = return_trace, prior_surv_final = prior_surv_final, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) 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, 6: treatment = 1), prop_loss = 0, alternative = "greater", doTryCatch(return(expr), name, parentenv, handler) 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", 7: imputed_final = FALSE, empty_interval = c("prior", "propagate", tryCatchOne(expr, names, parentenv, handlers[[1L]]) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto",
"fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", 8: "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, tryCatchList(expr, classes, parentenv, handlers) Qn = 1, rmst_tau = end_of_study)
{ 9: Call <- match.call()tryCatch({ Arguments <- capture_arguments(sim_trials, environment()) if (!is.null(trial_streams)) { method <- normalize_analysis_method(method) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) Arguments$method <- method } empty_interval <- match.arg(empty_interval) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, binary_imputation <- match.arg(binary_imputation) hazard_control = hazard_control, cutpoints = cutpoints, backend <- match.arg(backend) N_total = N_total, lambda = lambda, lambda_time = lambda_time, Arguments$empty_interval <- empty_interval interim_look = interim_look, end_of_study = end_of_study, Arguments$binary_imputation <- binary_imputation prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, Arguments$backend <- backend binary_imputation = binary_imputation, block = block, requested_backend <- backend rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, caller_rng_kind <- RNGkind() h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, validate_positive_integer_scalar(N_trials, "N_trials") N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, single_arm <- is.null(hazard_control) method = method, imputed_final = imputed_final, empty_interval = empty_interval, if (!single_arm) { return_trace = return_trace, prior_surv_final = prior_surv_final, rand_ratio <- validate_randomization_args(N_total, block, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) rand_ratio, allocation_name = "rand_ratio") attr(result, "arguments") <- NULL Arguments$rand_ratio <- rand_ratio if (inherits(result, "goldilocks_trial")) { } attr(result$summary, "arguments") <- NULL prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) } Arguments$prop_loss <- prop_loss list(trial = as.integer(x), result = result) validate_logical_scalar(return_trace, "return_trace")}, error = function(error) { validate_positive_integer_scalar(N_impute, "N_impute") list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], validate_positive_integer_scalar(N_mcmc, "N_mcmc") message = conditionMessage(error)) validate_single_probability(mc_conf_level, "mc_conf_level", }) upper_open = TRUE)
if (mc_conf_level <= 0.5) {10: stop("'mc_conf_level' must be greater than 0.5 and less than 1")FUN(X[[i]], ...) }
validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 11: 0), N_impute = N_impute)lapply(X = S, FUN = FUN, ...) if (identical(method, "rmst")) {
validate_endpoint_time(end_of_study, cutpoints, "end_of_study")12: validate_rmst_args(rmst_tau, end_of_study, h0)doTryCatch(return(expr), name, parentenv, handler) validate_analysis_configuration(method, alternative,
is.null(hazard_control), imputed_final)13: }tryCatchOne(expr, names, parentenv, handlers[[1L]]) validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores,
14: N_trials = N_trials) backend <- execution$backendtryCatchList(expr, classes, parentenv, handlers) workers <- execution$workers stream_seed <- NULL
if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || 15: !is.finite(seed) || seed != floor(seed)) {tryCatch(expr, error = function(e) { stop("'seed' must be NULL or a single integer value") call <- conditionCall(e) } if (!is.null(call)) { old_kind <- RNGkind() if (identical(call[[1L]], quote(doTryCatch))) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) call <- sys.call(-4L) if (old_seed_exists) { dcall <- deparse(call, nlines = 1L) old_seed <- get(".Random.seed", envir = .GlobalEnv, prefix <- paste("Error in", dcall, ": ") inherits = FALSE) LONG <- 75L } on.exit({ sm <- strsplit(conditionMessage(e), "\n")[[1L]] do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") assign(".Random.seed", old_seed, envir = .GlobalEnv) if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } else if (exists(".Random.seed", envir = .GlobalEnv, type = "b") if (w > LONG) inherits = FALSE)) { prefix <- paste0(prefix, "\n ") rm(".Random.seed", envir = .GlobalEnv) } }, add = TRUE) } else prefix <- "Error : " stream_seed <- seed msg <- paste0(prefix, conditionMessage(e), "\n") trial_streams <- make_rng_streams(stream_seed, N_trials) } .Internal(seterrmessage(msg[1L])) else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, if (!silent && isTRUE(getOption("show.error.messages"))) { size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) cat(msg, file = outFile) .Internal(printDeferredWarnings()) } else { } invisible(structure(msg, class = "try-error", condition = e)) trial_streams <- NULL }})
rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {16: caller_rng_kind[1] } else {try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
"L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) {17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) "explicit_preserve_caller"
18: } else if (backend == "psock") { "caller_derived_psock"FUN(X[[i]], ...)
} else { "caller_state"19: }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), lapply(seq_len(cores), inner.do) stream_seed = stream_seed)
survival_adapt_fn <- if (backend == "psock") {20: make_psock_callable("survival_adapt")mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, } else { mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) survival_adapt
} survival_adapt_wrapper <- function(x) {21: tryCatch({pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)
22: } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, hazard_control = hazard_control, cutpoints = cutpoints, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, N_total = N_total, lambda = lambda, lambda_time = lambda_time, 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, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", bin_method = bin_method, binary_imputation = binary_imputation, imputed_final = FALSE, empty_interval = c("prior", "propagate", block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, Qn = 1, rmst_tau = end_of_study) rmst_tau = rmst_tau) attr(result, "arguments") <- NULL{ if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL Call <- match.call() } list(trial = as.integer(x), result = result) Arguments <- capture_arguments(sim_trials, environment()) }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], method <- normalize_analysis_method(method) message = conditionMessage(error)) Arguments$method <- method }) empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) } backend <- match.arg(backend) Arguments$empty_interval <- empty_interval trial_index <- seq_len(N_trials) Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend trial_results <- switch(backend, sequential = lapply(trial_index, requested_backend <- backend survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { caller_rng_kind <- RNGkind() active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, }) failed <- vapply(trial_results, function(x) is.null(x$result), rand_ratio, allocation_name = "rand_ratio") logical(1)) Arguments$rand_ratio <- rand_ratio } failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") `[[`, character(1), "message"), stringsAsFactors = FALSE) validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", upper_open = TRUE) if (mc_conf_level <= 0.5) { failures$message[1L])) stop("'mc_conf_level' must be greater than 0.5 and less than 1") } error$failures <- failures validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) if (identical(method, "rmst")) { } successful_trials <- trial_results[!failed] validate_endpoint_time(end_of_study, cutpoints, "end_of_study") successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { validate_rmst_args(rmst_tau, end_of_study, h0) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) function(i) { trace <- successful_results[[i]]$trace } validate_positive_integer_scalar(ncores, "ncores") trial <- successful_trials[[i]]$trial execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] backend <- execution$backend workers <- execution$workers })) out <- list(sims = sims, traces = traces, failures = failures, stream_seed <- NULL call = Call) } if (!is.null(seed)) { else { sims <- bind_rows(successful_results) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { out <- list(sims = sims, failures = failures, call = Call) } stop("'seed' must be NULL or a single integer value") } attr(out$sims, "arguments") <- NULL old_kind <- RNGkind() attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], inherits = FALSE) "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata inherits = FALSE) } attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, on.exit({ do.call(RNGkind, as.list(old_kind)) backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { } else if (exists(".Random.seed", envir = .GlobalEnv, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) inherits = FALSE)) { } return(out)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, rm(".Random.seed", envir = .GlobalEnv) 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, } 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, }, add = TRUE) stream_seed <- seed 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, trial_streams <- make_rng_streams(stream_seed, N_trials) 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, } mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, trial_streams <- make_rng_streams(stream_seed, N_trials) } seed = 3425430)
else {23: trial_streams <- NULLdo.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, } seed = 3425430))) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {
caller_rng_kind[1]24: eval(expr, envir) } else {
"L'Ecuyer-CMRG"25: }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller"eval(expr, envir)
} else if (backend == "psock") {26: "caller_derived_psock" } else {withVisible(eval(expr, envir)) "caller_state"
}, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), 27: stream_seed = stream_seed)withCallingHandlers(code, error = function (e) survival_adapt_fn <- if (backend == "psock") {rlang::entrace(e), message = function (cnd) make_psock_callable("survival_adapt"){ watcher$capture_plot_and_output() } if (on_message$capture) { else { watcher$push(cnd) } survival_adapt if (on_message$silence) { } survival_adapt_wrapper <- function(x) { invokeRestart("muffleMessage") tryCatch({ } if (!is.null(trial_streams)) {}, warning = function (cnd) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv){ } if (getOption("warn") >= 2 || getOption("warn") < 0) { return() result <- survival_adapt_fn(hazard_treatment = hazard_treatment, } watcher$capture_plot_and_output() hazard_control = hazard_control, cutpoints = cutpoints, if (on_warning$capture) { N_total = N_total, lambda = lambda, lambda_time = lambda_time, cnd <- sanitize_call(cnd) interim_look = interim_look, end_of_study = end_of_study, watcher$push(cnd) prior_surv = prior_surv, prior_bin = prior_bin, } if (on_warning$silence) { bin_method = bin_method, binary_imputation = binary_imputation, invokeRestart("muffleWarning") block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, } alternative = alternative, h0 = h0, Fn = Fn, }, error = function (cnd) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, { N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, watcher$capture_plot_and_output() method = method, imputed_final = imputed_final, cnd <- sanitize_call(cnd) empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, watcher$push(cnd) rmst_tau = rmst_tau) switch(on_error, continue = invokeRestart("eval_continue"), attr(result, "arguments") <- NULL stop = invokeRestart("eval_stop"), error = NULL) if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL}) }
list(trial = as.integer(x), result = result)28: }, error = function(error) {eval(call) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L],
message = conditionMessage(error))29: })eval(call) }
trial_index <- seq_len(N_trials)30: trial_results <- switch(backend, sequential = lapply(trial_index, with_handlers({ survival_adapt_wrapper), fork = pbmclapply(trial_index, for (expr in tle$exprs) { survival_adapt_wrapper, mc.cores = workers), psock = { ev <- withVisible(eval(expr, envir)) active_cluster <- make_sim_cluster(workers) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) on.exit(stop_sim_cluster(active_cluster), add = TRUE) } initialize_sim_cluster(active_cluster) TRUE}, handlers) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper)
})31: doWithOneRestart(return(expr), restart) failed <- vapply(trial_results, function(x) is.null(x$result),
logical(1))32: failed_results <- trial_results[failed]withOneRestart(expr, restarts[[1L]])
33: failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, withRestartList(expr, restarts[-nr]) `[[`, 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: ", 34: failures$message[1L]))doWithOneRestart(return(expr), restart) error$failures <- failures
class(error) <- c("goldilocks_all_trials_failed", class(error))35: stop(error)withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
} successful_trials <- trial_results[!failed]36: withRestartList(expr, restarts) successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) {
sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), 37: function(i) {withRestarts(with_handlers({ for (expr in tle$exprs) { trace <- successful_results[[i]]$trace ev <- withVisible(eval(expr, envir)) trial <- successful_trials[[i]]$trial watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } trace$trial <- rep.int(trial, nrow(trace)) TRUE trace[c("trial", setdiff(names(trace), "trial"))]}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) })) out <- list(sims = sims, traces = traces, failures = failures, call = Call)
38: } else {evaluate::evaluate(...)
sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call)39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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$error else { attr(out$sims, "arguments") <- NULL if (options$error && options$include) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, 0L N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, else 2L end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], }, output_handler = knit_handlers(options$render, options)) "decision_design", exact = TRUE)
40: attr(out, "prior_design") <- attr(successful_results[[1]], in_dir(input_dir(), expr) "prior_design", exact = TRUE)
attr(out, "rng_metadata") <- rng_metadata41: attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, in_input_dir(evaluate(code, envir = env, new_device = FALSE, backend = backend, selection_reason = execution$reason, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, requested_ncores = as.integer(ncores), workers = as.integer(workers), stop_on_error = if (is.numeric(options$error)) options$error else { tasks = as.integer(N_trials)) if (options$error && options$include) 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: eng_r(options)})(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,
43: 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 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: call_block(x) treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0,
45: 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, process_group(group) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank",
46: imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, seed = 3425430) error = function(e) {
if (progress && is.function(pb$interrupt)) pb$interrupt()23: if (is_R_CMD_build() || is_R_CMD_check()) do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, error <<- format(e) seed = 3425430))) })
24: 47: eval(expr, envir)
with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) {25: eval(expr, envir) if (progress && is.function(pb$interrupt)) pb$interrupt()
26: if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e)withVisible(eval(expr, envir))
}), list(rlang_trace_top_env = knit_global()))
27: withCallingHandlers(code, error = function (e) 48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), rlang::entrace(e), message = function (cnd) error = function(e) {{ if (progress && is.function(pb$interrupt)) watcher$capture_plot_and_output() pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) if (on_message$capture) { watcher$push(cnd) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { } if (on_message$silence) { setwd(wd) write_utf8(res, output %n% stdout()) invokeRestart("muffleMessage") } paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, warning = function (cnd) {}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
if (getOption("warn") >= 2 || getOption("warn") < 0) {49: return()process_file(text, output)
}50: watcher$capture_plot_and_output()knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) if (on_warning$capture) {
cnd <- sanitize_call(cnd) watcher$push(cnd)51: } if (on_warning$silence) {rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), invokeRestart("muffleWarning") output_dir = getwd(), ...) }
}, error = function (cnd) 52: vweave_rmarkdown(...){ watcher$capture_plot_and_output()
cnd <- sanitize_call(cnd)53: engine$weave(file, quiet = quiet, encoding = enc) watcher$push(cnd)
switch(on_error, continue = invokeRestart("eval_continue"), 54: stop = invokeRestart("eval_stop"), error = NULL)doTryCatch(return(expr), name, parentenv, handler)})
55:
28: tryCatchOne(expr, names, parentenv, handlers[[1L]])
eval(call)
56: 29: tryCatchList(expr, classes, parentenv, handlers)eval(call)
57: 30: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc)with_handlers({ for (expr in tle$exprs) { setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) ev <- withVisible(eval(expr, envir)) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) watcher$capture_plot_and_output() output <- find_vignette_product(name, by = "texi2pdf", watcher$print_value(ev$value, ev$visible, envir) engine = engine) } } TRUE}, error = function(e) { OK <<- FALSE}, handlers)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))31: doWithOneRestart(return(expr), restart)})
58: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 32: TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpXlZ71m/file660b41ae2a2f.rds")
withOneRestart(expr, restarts[[1L]])An irrecoverable exception occurred. R is aborting now ...
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$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("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/RtmpXlZ71m/file660b41ae2a2f.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
2026-10-29 13:13:16.241 R[27361:343239] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-29 13:13:17.912 R[27422:343421] 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
2026-10-29 13:13:29.755 R[27929:344892] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-29 13:13:31.630 R[28105:345410] 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