Last updated on 2026-11-12 15:01:03 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 27.15 | 540.40 | 567.55 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.59 | 372.42 | 391.01 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | 355.55 | 375.55 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 20.00 | 366.04 | 386.04 | OK | |
| r-devel-windows-x86_64 | 1.0.0 | 43.00 | 596.00 | 639.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 26.91 | 519.09 | 546.00 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.95 | 529.38 | 555.33 | 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 | 438.00 | 480.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 7.00 | 87.00 | 94.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.0 | 19.00 | 406.00 | 425.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 52.00 | 576.00 | 628.00 | 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpNxfxNi/file2492319bb655.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpNxfxNi/file2492319bb655.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-11-12 22:22:48.295 R[13961:318278] 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$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
Traceback:
1: 19: impute_predictive_arm(time = data_in$time[current_treatment], lapply(seq_len(cores), inner.do)
hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
20: 2: fill_predictive_imputations(imputations = current, rows = current_treatment, mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
21: 3: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)impute_predictive_draws(data_in = data_interim, hazards = post_lambda,
end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, 22: binary_imputation = predictive_binary_imputation, check_futility = check_futility)
(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, 4: evaluate_interim_decision(data_interim = data_interim, look = i, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, imputed_final = FALSE, empty_interval = c("prior", "propagate", N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", method = method, binary_imputation = binary_imputation, check_futility = check_futility, "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", Qn = Qn[i])
"bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, 5: Qn = 1, rmst_tau = end_of_study) 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, Call <- match.call() binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, Arguments <- capture_arguments(sim_trials, environment()) prop_loss = prop_loss, alternative = alternative, h0 = h0, method <- normalize_analysis_method(method) Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, Arguments$method <- method N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, empty_interval <- match.arg(empty_interval) imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, binary_imputation <- match.arg(binary_imputation) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) backend <- match.arg(backend)
Arguments$empty_interval <- empty_interval 6: doTryCatch(return(expr), name, parentenv, handler) Arguments$binary_imputation <- binary_imputation
Arguments$backend <- backend 7: requested_backend <- backendtryCatchOne(expr, names, parentenv, handlers[[1L]]) caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials")
8: single_arm <- is.null(hazard_control)tryCatchList(expr, classes, parentenv, handlers) if (!single_arm) {
rand_ratio <- validate_randomization_args(N_total, block, 9: tryCatch({ rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) result <- survival_adapt_fn(hazard_treatment = hazard_treatment, Arguments$prop_loss <- prop_loss hazard_control = hazard_control, cutpoints = cutpoints, validate_logical_scalar(return_trace, "return_trace") N_total = N_total, lambda = lambda, lambda_time = lambda_time, validate_positive_integer_scalar(N_impute, "N_impute") interim_look = interim_look, end_of_study = end_of_study, validate_positive_integer_scalar(N_mcmc, "N_mcmc") prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, validate_single_probability(mc_conf_level, "mc_conf_level", binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, upper_open = TRUE) h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, if (mc_conf_level <= 0.5) { method = method, imputed_final = imputed_final, empty_interval = empty_interval, stop("'mc_conf_level' must be greater than 0.5 and less than 1") return_trace = return_trace, prior_surv_final = prior_surv_final, } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) 0), N_impute = N_impute) if (identical(method, "rmst")) { attr(result, "arguments") <- NULL validate_endpoint_time(end_of_study, cutpoints, "end_of_study") if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL validate_rmst_args(rmst_tau, end_of_study, h0) } validate_analysis_configuration(method, alternative, list(trial = as.integer(x), result = result) is.null(hazard_control), imputed_final)}, error = function(error) { } validate_positive_integer_scalar(ncores, "ncores") list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) execution <- resolve_sim_execution(backend = backend, ncores = ncores, })
N_trials = N_trials) backend <- execution$backend10: FUN(X[[i]], ...) workers <- execution$workers stream_seed <- NULL
11: if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || lapply(X = S, FUN = FUN, ...)
!is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value")12: doTryCatch(return(expr), name, parentenv, handler) } old_kind <- RNGkind()
old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, 13: inherits = FALSE) if (old_seed_exists) {tryCatchOne(expr, names, parentenv, handlers[[1L]])
old_seed <- get(".Random.seed", envir = .GlobalEnv, 14: inherits = FALSE) }tryCatchList(expr, classes, parentenv, handlers) on.exit({
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) do.call(RNGkind, as.list(old_kind)) dcall <- deparse(call, nlines = 1L) if (old_seed_exists) { prefix <- paste("Error in", dcall, ": ") assign(".Random.seed", old_seed, envir = .GlobalEnv) LONG <- 75L } else if (exists(".Random.seed", envir = .GlobalEnv, sm <- strsplit(conditionMessage(e), "\n")[[1L]] inherits = FALSE)) { w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") rm(".Random.seed", envir = .GlobalEnv) if (is.na(w)) } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], }, add = TRUE) type = "b") stream_seed <- seed if (w > LONG) trial_streams <- make_rng_streams(stream_seed, N_trials) prefix <- paste0(prefix, "\n ") } } else if (backend == "psock") { else prefix <- "Error : " stream_seed <- sample.int(.Machine$integer.max - 1L, msg <- paste0(prefix, conditionMessage(e), "\n") size = 1L) .Internal(seterrmessage(msg[1L])) trial_streams <- make_rng_streams(stream_seed, N_trials) if (!silent && isTRUE(getOption("show.error.messages"))) { } cat(msg, file = outFile) else { .Internal(printDeferredWarnings()) trial_streams <- NULL } invisible(structure(msg, class = "try-error", condition = e)) }}) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) {
caller_rng_kind[1]16: } else {try(lapply(X = S, FUN = FUN, ...), silent = TRUE) "L'Ecuyer-CMRG"
}, seed_policy = if (!is.null(seed)) {17: "explicit_preserve_caller"sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) } else if (backend == "psock") {
"caller_derived_psock"18: } else {FUN(X[[i]], ...) "caller_state"
}, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), 19: stream_seed = stream_seed)lapply(seq_len(cores), inner.do) survival_adapt_fn <- if (backend == "psock") {
make_psock_callable("survival_adapt")20: }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
}21: survival_adapt_wrapper <- function(x) {pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) tryCatch({
if (!is.null(trial_streams)) {22: assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, } N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, hazard_control = hazard_control, cutpoints = cutpoints, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, N_total = N_total, lambda = lambda, lambda_time = lambda_time, treatment = 1), prop_loss = 0, alternative = "greater", interim_look = interim_look, end_of_study = end_of_study, h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, prior_surv = prior_surv, prior_bin = prior_bin, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", bin_method = bin_method, binary_imputation = binary_imputation, "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, alternative = alternative, h0 = h0, Fn = Fn, Qn = 1, rmst_tau = end_of_study) { Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, Call <- match.call() N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, Arguments <- capture_arguments(sim_trials, environment()) method = method, imputed_final = imputed_final, method <- normalize_analysis_method(method) Arguments$method <- method empty_interval = empty_interval, return_trace = return_trace, empty_interval <- match.arg(empty_interval) prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) rmst_tau = rmst_tau) Arguments$empty_interval <- empty_interval attr(result, "arguments") <- NULL Arguments$binary_imputation <- binary_imputation if (inherits(result, "goldilocks_trial")) { Arguments$backend <- backend attr(result$summary, "arguments") <- NULL requested_backend <- backend caller_rng_kind <- RNGkind() } list(trial = as.integer(x), result = result) validate_positive_integer_scalar(N_trials, "N_trials") }, error = function(error) { single_arm <- is.null(hazard_control) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], if (!single_arm) { message = conditionMessage(error)) rand_ratio <- validate_randomization_args(N_total, block, }) rand_ratio, allocation_name = "rand_ratio") } trial_index <- seq_len(N_trials) Arguments$rand_ratio <- rand_ratio trial_results <- switch(backend, sequential = lapply(trial_index, } survival_adapt_wrapper), fork = pbmclapply(trial_index, prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) survival_adapt_wrapper, mc.cores = workers), psock = { Arguments$prop_loss <- prop_loss active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) validate_logical_scalar(return_trace, "return_trace") initialize_sim_cluster(active_cluster) validate_positive_integer_scalar(N_impute, "N_impute") run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", failed <- vapply(trial_results, function(x) is.null(x$result), upper_open = TRUE) logical(1)) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") failed_results <- trial_results[failed] } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > failures <- data.frame(trial = vapply(failed_results, `[[`, 0), N_impute = N_impute) integer(1), "trial"), error_class = vapply(failed_results, if (identical(method, "rmst")) { `[[`, character(1), "error_class"), message = vapply(failed_results, validate_endpoint_time(end_of_study, cutpoints, "end_of_study") `[[`, character(1), "message"), stringsAsFactors = FALSE) validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", } validate_positive_integer_scalar(ncores, "ncores") failures$message[1L])) error$failures <- failures execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) class(error) <- c("goldilocks_all_trials_failed", class(error)) backend <- execution$backend stop(error) } workers <- execution$workers successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") stream_seed <- NULL if (return_trace) { if (!is.null(seed)) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || traces <- bind_rows(lapply(seq_along(successful_results), !is.finite(seed) || seed != floor(seed)) { function(i) { stop("'seed' must be NULL or a single integer value") trace <- successful_results[[i]]$trace } trial <- successful_trials[[i]]$trial old_kind <- RNGkind() trace$trial <- rep.int(trial, nrow(trace)) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, trace[c("trial", setdiff(names(trace), "trial"))] inherits = FALSE) })) if (old_seed_exists) { out <- list(sims = sims, traces = traces, failures = failures, old_seed <- get(".Random.seed", envir = .GlobalEnv, call = Call) inherits = FALSE) } } else { on.exit({ sims <- bind_rows(successful_results) do.call(RNGkind, as.list(old_kind)) out <- list(sims = sims, failures = failures, call = Call) if (old_seed_exists) { } assign(".Random.seed", old_seed, envir = .GlobalEnv) attr(out$sims, "arguments") <- NULL } else if (exists(".Random.seed", envir = .GlobalEnv, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, inherits = FALSE)) { N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, rm(".Random.seed", envir = .GlobalEnv) end_of_study = end_of_study) } attr(out, "decision_design") <- attr(successful_results[[1]], }, add = TRUE) "decision_design", exact = TRUE) stream_seed <- seed attr(out, "prior_design") <- attr(successful_results[[1]], trial_streams <- make_rng_streams(stream_seed, N_trials) "prior_design", exact = TRUE) } attr(out, "rng_metadata") <- rng_metadata else if (backend == "psock") { attr(out, "arguments") <- Arguments stream_seed <- sample.int(.Machine$integer.max - 1L, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, size = 1L) backend = backend, selection_reason = execution$reason, trial_streams <- make_rng_streams(stream_seed, N_trials) requested_ncores = as.integer(ncores), workers = as.integer(workers), } tasks = as.integer(N_trials)) else { if (nrow(failures) > 0L) { trial_streams <- NULL warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", } "for details.", call. = FALSE) rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { } caller_rng_kind[1] return(out) } else {})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, "L'Ecuyer-CMRG" lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, }, seed_policy = if (!is.null(seed)) { 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, "explicit_preserve_caller" 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, } else if (backend == "psock") { 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, "caller_derived_psock" treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, } else { Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, "caller_state" 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", stream_seed = stream_seed) imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 survival_adapt_fn <- if (backend == "psock") { ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, make_psock_callable("survival_adapt") seed = 3425430) }
else {23: survival_adaptdo.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, } hazard_control = hazard_control_week, N_trials = 20, ncores = 2, survival_adapt_wrapper <- function(x) { seed = 3425430))) tryCatch({
if (!is.null(trial_streams)) {24: assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)eval(expr, envir) }
result <- survival_adapt_fn(hazard_treatment = hazard_treatment, 25: hazard_control = hazard_control, cutpoints = cutpoints, eval(expr, envir) N_total = N_total, lambda = lambda, lambda_time = lambda_time,
interim_look = interim_look, end_of_study = end_of_study, 26: prior_surv = prior_surv, prior_bin = prior_bin, withVisible(eval(expr, envir)) bin_method = bin_method, binary_imputation = binary_imputation,
block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, 27: alternative = alternative, h0 = h0, Fn = Fn, withCallingHandlers(code, error = function (e) Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, rlang::entrace(e), message = function (cnd) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, { method = method, imputed_final = imputed_final, watcher$capture_plot_and_output() empty_interval = empty_interval, return_trace = return_trace, if (on_message$capture) { prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, watcher$push(cnd) rmst_tau = rmst_tau) } attr(result, "arguments") <- NULL if (on_message$silence) { if (inherits(result, "goldilocks_trial")) { invokeRestart("muffleMessage") attr(result$summary, "arguments") <- NULL } }}, warning = function (cnd) list(trial = as.integer(x), result = result){ }, error = function(error) { if (getOption("warn") >= 2 || getOption("warn") < 0) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], return() message = conditionMessage(error)) } }) watcher$capture_plot_and_output() } if (on_warning$capture) { trial_index <- seq_len(N_trials) cnd <- sanitize_call(cnd) trial_results <- switch(backend, sequential = lapply(trial_index, watcher$push(cnd) survival_adapt_wrapper), fork = pbmclapply(trial_index, } survival_adapt_wrapper, mc.cores = workers), psock = { if (on_warning$silence) { active_cluster <- make_sim_cluster(workers) invokeRestart("muffleWarning") on.exit(stop_sim_cluster(active_cluster), add = TRUE) } initialize_sim_cluster(active_cluster)}, error = function (cnd) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper){ }) watcher$capture_plot_and_output() failed <- vapply(trial_results, function(x) is.null(x$result), cnd <- sanitize_call(cnd) logical(1)) watcher$push(cnd) failed_results <- trial_results[failed] switch(on_error, continue = invokeRestart("eval_continue"), failures <- data.frame(trial = vapply(failed_results, `[[`, stop = invokeRestart("eval_stop"), error = NULL) integer(1), "trial"), error_class = vapply(failed_results, }) `[[`, character(1), "error_class"), message = vapply(failed_results,
`[[`, character(1), "message"), stringsAsFactors = FALSE)28: if (all(failed)) {eval(call) error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ",
failures$message[1L]))29: error$failures <- failureseval(call) class(error) <- c("goldilocks_all_trials_failed", class(error))
stop(error)30: }with_handlers({ successful_trials <- trial_results[!failed] for (expr in tle$exprs) { successful_results <- lapply(successful_trials, `[[`, "result") ev <- withVisible(eval(expr, envir)) if (return_trace) { watcher$capture_plot_and_output() sims <- bind_rows(lapply(successful_results, function(x) x$summary)) watcher$print_value(ev$value, ev$visible, envir) traces <- bind_rows(lapply(seq_along(successful_results), } function(i) { TRUE trace <- successful_results[[i]]$trace}, handlers) trial <- successful_trials[[i]]$trial
trace$trial <- rep.int(trial, nrow(trace))31: trace[c("trial", setdiff(names(trace), "trial"))]doWithOneRestart(return(expr), restart) }))
out <- list(sims = sims, traces = traces, failures = failures, 32: call = Call)withOneRestart(expr, restarts[[1L]]) }
else {33: sims <- bind_rows(successful_results)withRestartList(expr, restarts[-nr]) out <- list(sims = sims, failures = failures, call = Call)
}34: attr(out$sims, "arguments") <- NULLdoWithOneRestart(return(expr), restart) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda,
N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, 35: end_of_study = end_of_study)withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) attr(out, "decision_design") <- attr(successful_results[[1]],
"decision_design", exact = TRUE)36: attr(out, "prior_design") <- attr(successful_results[[1]], withRestartList(expr, restarts) "prior_design", exact = TRUE)
attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments37: attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, withRestarts(with_handlers({ backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), for (expr in tle$exprs) { tasks = as.integer(N_trials)) ev <- withVisible(eval(expr, envir)) if (nrow(failures) > 0L) { watcher$capture_plot_and_output() warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", watcher$print_value(ev$value, ev$visible, envir) "for details.", call. = FALSE) } } TRUE return(out)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)})(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, 38: 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, evaluate::evaluate(...) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333,
prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 39: 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, keep_message = if (is.numeric(options$message)) TRUE else options$message, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, stop_on_error = if (is.numeric(options$error)) options$error else { 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, if (options$error && options$include) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", 0L imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 else 2L ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, }, output_handler = knit_handlers(options$render, options)) seed = 3425430)
40: 23: in_dir(input_dir(), expr)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week,
hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 41: seed = 3425430)))in_input_dir(evaluate(code, envir = env, new_device = FALSE,
keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 24: keep_message = if (is.numeric(options$message)) TRUE else options$message, eval(expr, envir) stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 25: 0Leval(expr, envir) else 2L
}, output_handler = knit_handlers(options$render, options)))26:
withVisible(eval(expr, envir))42:
eng_r(options)27:
withCallingHandlers(code, error = function (e) 43: rlang::entrace(e), message = function (cnd) block_exec(params){
watcher$capture_plot_and_output()44: if (on_message$capture) {call_block(x) watcher$push(cnd)
}45: if (on_message$silence) {process_group(group) invokeRestart("muffleMessage")
}46: }, warning = function (cnd) withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), { error = function(e) { if (getOption("warn") >= 2 || getOption("warn") < 0) { if (progress && is.function(pb$interrupt)) return() pb$interrupt() } if (is_R_CMD_build() || is_R_CMD_check()) watcher$capture_plot_and_output() error <<- format(e) if (on_warning$capture) { }) cnd <- sanitize_call(cnd)
watcher$push(cnd)47: }with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (on_warning$silence) { error = function(e) { invokeRestart("muffleWarning") if (progress && is.function(pb$interrupt)) } pb$interrupt()}, error = function (cnd) if (is_R_CMD_build() || is_R_CMD_check()) { error <<- format(e) watcher$capture_plot_and_output() }), list(rlang_trace_top_env = knit_global())) cnd <- sanitize_call(cnd)
watcher$push(cnd)48: switch(on_error, continue = invokeRestart("eval_continue"), xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), stop = invokeRestart("eval_stop"), error = NULL) error = function(e) {}) if (progress && is.function(pb$interrupt))
pb$interrupt()28: if (is_R_CMD_build() || is_R_CMD_check()) eval(call) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) {29: setwd(wd)eval(call) write_utf8(res, output %n% stdout())
paste0("\nQuitting from ", loc, if (!is.null(error)) 30: paste0("\n", rule(), error, "\n", rule()))with_handlers({}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))49: watcher$capture_plot_and_output()process_file(text, output) watcher$print_value(ev$value, ev$visible, envir)
}50: TRUEknitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)}, handlers)
51: 31: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), doWithOneRestart(return(expr), restart) output_dir = getwd(), ...)
32: 52: withOneRestart(expr, restarts[[1L]])vweave_rmarkdown(...)
33: 53: withRestartList(expr, restarts[-nr])engine$weave(file, quiet = quiet, encoding = enc)
34: 54: doWithOneRestart(return(expr), restart)doTryCatch(return(expr), name, parentenv, handler)
35: 55: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
36: withRestartList(expr, restarts)
56: 37: withRestarts(with_handlers({ for (expr in tle$exprs) {tryCatchList(expr, classes, parentenv, handlers) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()57: watcher$print_value(ev$value, ev$visible, envir)tryCatch({ } engine$weave(file, quiet = quiet, encoding = enc) TRUE setwd(startdir)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {38: texi2pdf(file = output, clean = FALSE, quiet = quiet)evaluate::evaluate(...) output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)39: }evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, }, error = function(e) { keep_message = if (is.numeric(options$message)) TRUE else options$message, OK <<- FALSE stop_on_error = if (is.numeric(options$error)) options$error else { message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", if (options$error && options$include) file, conditionMessage(e))) 0L}) else 2L
}, output_handler = knit_handlers(options$render, options))58:
tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 40: TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpNxfxNi/file24927bda269.rds")in_dir(input_dir(), expr)
An irrecoverable exception occurred. R is aborting now ...
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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/RtmpNxfxNi/file24927bda269.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-11-12 22:22:54.628 R[14166:318915] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-11-12 22:22:56.432 R[14226:319096] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-11-12 22:23:08.087 R[14633:320344] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-11-12 22:23:09.917 R[14773:320772] 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