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### Running command:
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### /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data censcyt
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* checking for file ‘censcyt/DESCRIPTION’ ... OK
* preparing ‘censcyt’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ...sh: line 1: 40394 Segmentation fault: 11 '/Library/Frameworks/R.framework/Resources/bin/Rscript' --vanilla --default-packages= -e "tools::buildVignettes(dir = '.', tangle = TRUE)" > '/tmp/RtmpYbvCQl/xshell9c035923f63c' 2>&1
ERROR
--- re-building ‘censored_covariate.Rmd’ using rmarkdown
2024-03-27 16:14:52.844 R[40394:1357763791] XType: com.apple.fonts is not accessible.
2024-03-27 16:14:52.845 R[40394:1357763791] XType: XTFontStaticRegistry is enabled.
*** caught segfault ***
address 0x0, cause 'unknown'
Traceback:
1: .Call(merPredDCreate, as(X, "matrix"), Lambdat, LamtUt, Lind, RZX, Ut, Utr, V, VtV, Vtr, Xwts, Zt, beta0, delb, delu, theta, u0)
2: initializePtr()
3: .Object$initialize(...)
4: initialize(value, ...)
5: initialize(value, ...)
6: methods::new(def, ...)
7: (new("refMethodDef", .Data = function (...) { methods::new(def, ...)}, mayCall = c("methods", "new"), name = "new", refClassName = "refGeneratorSlot", superClassMethod = ""))(Zt = new("dgCMatrix", i = c(26L, 24L, 2L, 33L, 31L, 17L, 38L, 7L, 35L, 15L, 43L, 20L, 49L, 5L, 48L, 3L, 40L, 11L, 36L, 46L, 23L, 21L, 29L, 39L, 1L, 30L, 42L, 28L, 47L, 27L, 22L, 4L, 34L, 45L, 19L, 18L, 41L, 9L, 12L, 6L, 8L, 16L, 25L, 37L, 32L, 44L, 14L, 10L, 0L, 13L), p = 0:50, Dim = c(50L, 50L), Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50"), c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50")), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), factors = list()), theta = 1, Lambdat = new("dgCMatrix", i = 0:49, p = 0:50, Dim = c(50L, 50L), Dimnames = list(NULL, NULL), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), factors = list()), Lind = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), n = 50L, X = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.00399494590237737, 0.876960574486011, 0.00658195674547719, 0.00786697631701827, 0.0090918242931366, 0.0146137401461601, 0.0170951313339174, 0.336990794166923, 0.0290132522772983, 0.031725528959428, 0.0383799616247416, 2.01986489250305, 0.0885395100340247, 0.0899496711790562, 0.0988534230514452, 0.824081514920008, 0.129139679949731, 0.925526209133578, 1.88007667838576, 1.88007667838576, 0.824081514920008, 2.01986489250305, 0.824081514920008, 0.354189054574817, 0.824081514920008, 1.88007667838576, 0.760430300566268, 0.387182583566755, 0.336990794166923, 0.354189054574817, 0.383541618329485, 0.387182583566755, 0.838040319229376, 0.876960574486011, 0.519341270904988, 2.01986489250305, 1.88007667838576, 0.760430300566268, 2.01986489250305, 0.824081514920008, 0.838040319229376, 0.876960574486011, 1.88007667838576, 0.925526209133578, 2.01986489250305, 2.01986489250305, 2.01986489250305, 1.88007667838576, 2.01986489250305, 2.01986489250305 ))
8: do.call(merPredD$new, c(reTrms[c("Zt", "theta", "Lambdat", "Lind")], n = nrow(X), list(X = X)))
9: (function (fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, maxit = 100L, control = glmerControl(), ...) { stopifnot(length(nAGQ <- as.integer(nAGQ)) == 1L, 0L <= nAGQ, nAGQ <= 25L) verbose <- as.integer(verbose) maxit <- as.integer(maxit) rho <- list2env(list(verbose = verbose, maxit = maxit, tolPwrss = control$tolPwrss, compDev = control$compDev), parent = parent.frame()) rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt", "theta", "Lambdat", "Lind")], n = nrow(X), list(X = X))) rho$resp <- if (missing(fr)) mkRespMod(family = family, ...) else mkRespMod(fr, family = family) nAGQinit <- if (control$nAGQ0initStep) 0L else 1L if (length(y <- rho$resp$y) > 0) { checkResponse(y, control$checkControl) rho$verbose <- as.integer(verbose) .Call(glmerLaplace, rho$pp$ptr(), rho$resp$ptr(), nAGQinit, control$tolPwrss, maxit, verbose) rho$lp0 <- rho$pp$linPred(1) rho$pwrssUpdate <- glmerPwrssUpdate } rho$lower <- reTrms$lower mkdevfun(rho, nAGQinit, maxit = maxit, verbose = verbose, control = control)})(fr = list(y = c(0.125938611513635, 0.157628618379472, 0.17426273458445, 0.161290322580645, 0.185829096276857, 0.241776315789474, 0.1135897084987, 0.0580758017492711, 0.162432388133093, 0.225304547099264, 0.250677099006921, 0.0592329335110321, 0.0896057347670251, 0.166909620991254, 0.171294000991572, 0.215735248204808, 0.132324049462614, 0.13762962962963, 0.199574014909478, 0.125, 0.241638579206637, 0.154743054555923, 0.133450704225352, 0.16600790513834, 0.295535882463741, 0.216188835984196, 0.124707716289945, 0.146399310047434, 0.100620513444458, 0.186256301406209, 0.265508253692441, 0.0504451038575668, 0.257445992585964, 0.102633969118983, 0.174977150401137, 0.256616194865043, 0.233493022287024, 0.158005803218148, 0.126749219772476, 0.185, 0.199641362821279, 0.128379183041562, 0.0678054732438557, 0.155749851807943, 0.193301163780869, 0.246636771300448, 0.130852609230082, 0.192893401015228, 0.0904212503353904, 0.0905292479108635), X_obs_est = c(0.00399494590237737, 0.876960574486011, 0.00658195674547719, 0.00786697631701827, 0.0090918242931366, 0.0146137401461601, 0.0170951313339174, 0.336990794166923, 0.0290132522772983, 0.031725528959428, 0.0383799616247416, 2.01986489250305, 0.0885395100340247, 0.0899496711790562, 0.0988534230514452, 0.824081514920008, 0.129139679949731, 0.925526209133578, 1.88007667838576, 1.88007667838576, 0.824081514920008, 2.01986489250305, 0.824081514920008, 0.354189054574817, 0.824081514920008, 1.88007667838576, 0.760430300566268, 0.387182583566755, 0.336990794166923, 0.354189054574817, 0.383541618329485, 0.387182583566755, 0.838040319229376, 0.876960574486011, 0.519341270904988, 2.01986489250305, 1.88007667838576, 0.760430300566268, 2.01986489250305, 0.824081514920008, 0.838040319229376, 0.876960574486011, 1.88007667838576, 0.925526209133578, 2.01986489250305, 2.01986489250305, 2.01986489250305, 1.88007667838576, 2.01986489250305, 2.01986489250305), sample_id = c(27L, 25L, 3L, 34L, 32L, 18L, 39L, 8L, 36L, 16L, 44L, 21L, 50L, 6L, 49L, 4L, 41L, 12L, 37L, 47L, 24L, 22L, 30L, 40L, 2L, 31L, 43L, 29L, 48L, 28L, 23L, 5L, 35L, 46L, 20L, 19L, 42L, 10L, 13L, 7L, 9L, 17L, 26L, 38L, 33L, 45L, 15L, 11L, 1L, 14L), `(weights)` = c(7591, 7911, 4103, 2666, 6097, 8512, 7307, 8575, 6101, 8291, 3323, 6753, 5580, 9604, 4034, 6406, 8653, 6750, 9390, 5496, 3857, 6947, 2840, 5313, 5309, 9871, 1283, 4638, 8219, 3769, 5755, 1685, 7823, 2202, 9847, 7595, 4801, 3791, 9933, 1200, 6692, 8397, 5737, 6748, 3523, 4014, 8949, 7683, 3727, 2154)), X = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.00399494590237737, 0.876960574486011, 0.00658195674547719, 0.00786697631701827, 0.0090918242931366, 0.0146137401461601, 0.0170951313339174, 0.336990794166923, 0.0290132522772983, 0.031725528959428, 0.0383799616247416, 2.01986489250305, 0.0885395100340247, 0.0899496711790562, 0.0988534230514452, 0.824081514920008, 0.129139679949731, 0.925526209133578, 1.88007667838576, 1.88007667838576, 0.824081514920008, 2.01986489250305, 0.824081514920008, 0.354189054574817, 0.824081514920008, 1.88007667838576, 0.760430300566268, 0.387182583566755, 0.336990794166923, 0.354189054574817, 0.383541618329485, 0.387182583566755, 0.838040319229376, 0.876960574486011, 0.519341270904988, 2.01986489250305, 1.88007667838576, 0.760430300566268, 2.01986489250305, 0.824081514920008, 0.838040319229376, 0.876960574486011, 1.88007667838576, 0.925526209133578, 2.01986489250305, 2.01986489250305, 2.01986489250305, 1.88007667838576, 2.01986489250305, 2.01986489250305), reTrms = list(Zt = new("dgCMatrix", i = c(26L, 24L, 2L, 33L, 31L, 17L, 38L, 7L, 35L, 15L, 43L, 20L, 49L, 5L, 48L, 3L, 40L, 11L, 36L, 46L, 23L, 21L, 29L, 39L, 1L, 30L, 42L, 28L, 47L, 27L, 22L, 4L, 34L, 45L, 19L, 18L, 41L, 9L, 12L, 6L, 8L, 16L, 25L, 37L, 32L, 44L, 14L, 10L, 0L, 13L), p = 0:50, Dim = c(50L, 50L), Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50"), c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50")), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), factors = list()), theta = 1, Lind = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Gp = c(0L, 50L), lower = 0, Lambdat = new("dgCMatrix", i = 0:49, p = 0:50, Dim = c(50L, 50L), Dimnames = list(NULL, NULL), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), factors = list()), flist = list(sample_id = c(27L, 25L, 3L, 34L, 32L, 18L, 39L, 8L, 36L, 16L, 44L, 21L, 50L, 6L, 49L, 4L, 41L, 12L, 37L, 47L, 24L, 22L, 30L, 40L, 2L, 31L, 43L, 29L, 48L, 28L, 23L, 5L, 35L, 46L, 20L, 19L, 42L, 10L, 13L, 7L, 9L, 17L, 26L, 38L, 33L, 45L, 15L, 11L, 1L, 14L)), cnms = list(sample_id = "(Intercept)"), Ztlist = list(`1 | sample_id` = new("dgCMatrix", i = c(26L, 24L, 2L, 33L, 31L, 17L, 38L, 7L, 35L, 15L, 43L, 20L, 49L, 5L, 48L, 3L, 40L, 11L, 36L, 46L, 23L, 21L, 29L, 39L, 1L, 30L, 42L, 28L, 47L, 27L, 22L, 4L, 34L, 45L, 19L, 18L, 41L, 9L, 12L, 6L, 8L, 16L, 25L, 37L, 32L, 44L, 14L, 10L, 0L, 13L ), p = 0:50, Dim = c(50L, 50L), Dimnames = list(c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50"), c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50")), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), factors = list())), nl = c(sample_id = 50L)), family = list(family = "binomial", link = "logit", linkfun = function (mu) .Call(C_logit_link, mu), linkinv = function (eta) .Call(C_logit_linkinv, eta), variance = function (mu) mu * (1 - mu), dev.resids = function (y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt), aic = function (y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) }, mu.eta = function (eta) .Call(C_logit_mu_eta, eta), initialize = { if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if ("binomial" == "binomial" && any(abs(m - round(m)) > 0.001)) warning(gettextf("non-integer #successes in a %s glm!", "binomial"), domain = NA) } else if (NCOL(y) == 2) { if ("binomial" == "binomial" && any(abs(y - round(y)) > 0.001)) warning(gettextf("non-integer counts in a %s glm!", "binomial"), domain = NA) n <- (y1 <- y[, 1L]) + y[, 2L] y <- y1/n if (any(n0 <- n == 0)) y[n0] <- 0 weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop(gettextf("for the '%s' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures", "binomial"), domain = NA) }, validmu = function (mu) all(is.finite(mu)) && all(mu > 0 & mu < 1), valideta = function (eta) TRUE, simulate = function (object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts }, dispersion = 1), wmsgs = character(0), verbose = 0L, control = list( optimizer = c("bobyqa", "Nelder_Mead"), restart_edge = FALSE, boundary.tol = 1e-05, calc.derivs = TRUE, use.last.params = FALSE, checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop", check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop", check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop", check.response.not.const = "stop"), checkConv = list( check.conv.grad = list(action = "warning", tol = 0.002, relTol = NULL), check.conv.singular = list(action = "message", tol = 1e-04), check.conv.hess = list(action = "warning", tol = 1e-06)), optCtrl = list(), tolPwrss = 1e-07, compDev = TRUE, nAGQ0initStep = TRUE), nAGQ = 0L)
10: do.call(mkGlmerDevfun, c(glmod, list(verbose = verbose, control = control, nAGQ = nAGQinit)))
11: glmer(formula = "y ~ X_obs_est + (1 | sample_id)", data = list( y = c(0.125938611513635, 0.157628618379472, 0.17426273458445, 0.161290322580645, 0.185829096276857, 0.241776315789474, 0.1135897084987, 0.0580758017492711, 0.162432388133093, 0.225304547099264, 0.250677099006921, 0.0592329335110321, 0.0896057347670251, 0.166909620991254, 0.171294000991572, 0.215735248204808, 0.132324049462614, 0.13762962962963, 0.199574014909478, 0.125, 0.241638579206637, 0.154743054555923, 0.133450704225352, 0.16600790513834, 0.295535882463741, 0.216188835984196, 0.124707716289945, 0.146399310047434, 0.100620513444458, 0.186256301406209, 0.265508253692441, 0.0504451038575668, 0.257445992585964, 0.102633969118983, 0.174977150401137, 0.256616194865043, 0.233493022287024, 0.158005803218148, 0.126749219772476, 0.185, 0.199641362821279, 0.128379183041562, 0.0678054732438557, 0.155749851807943, 0.193301163780869, 0.246636771300448, 0.130852609230082, 0.192893401015228, 0.0904212503353904, 0.0905292479108635), weights = c(7591, 7911, 4103, 2666, 6097, 8512, 7307, 8575, 6101, 8291, 3323, 6753, 5580, 9604, 4034, 6406, 8653, 6750, 9390, 5496, 3857, 6947, 2840, 5313, 5309, 9871, 1283, 4638, 8219, 3769, 5755, 1685, 7823, 2202, 9847, 7595, 4801, 3791, 9933, 1200, 6692, 8397, 5737, 6748, 3523, 4014, 8949, 7683, 3727, 2154), X_obs = c(0.00399494590237737, 0.00651034153997898, 0.00658195674547719, 0.00786697631701827, 0.0090918242931366, 0.0146137401461601, 0.0170951313339174, 0.0231671622022986, 0.0290132522772983, 0.031725528959428, 0.0383799616247416, 0.0844376387788657, 0.0885395100340247, 0.0899496711790562, 0.0988534230514452, 0.115335379734737, 0.129139679949731, 0.130152224097401, 0.158453234082159, 0.168543034698814, 0.174358193552069, 0.193496348866272, 0.195486567448825, 0.20379509171471, 0.215479204452434, 0.263485631905496, 0.274596519302577, 0.324248936939786, 0.336990794166923, 0.354189054574817, 0.383541618329485, 0.387182583566755, 0.483200381044298, 0.514578070025891, 0.519341270904988, 0.719932226235158, 0.752054111874313, 0.760430300566268, 0.814178285146964, 0.824081514920008, 0.838040319229376, 0.876960574486011, 0.91387699568078, 0.925526209133578, 0.990691290195, 0.994260393972409, 1.4562055504972, 1.88007667838576, 1.95569157592001, 2.01986489250305), sample_id = c(27L, 25L, 3L, 34L, 32L, 18L, 39L, 8L, 36L, 16L, 44L, 21L, 50L, 6L, 49L, 4L, 41L, 12L, 37L, 47L, 24L, 22L, 30L, 40L, 2L, 31L, 43L, 29L, 48L, 28L, 23L, 5L, 35L, 46L, 20L, 19L, 42L, 10L, 13L, 7L, 9L, 17L, 26L, 38L, 33L, 45L, 15L, 11L, 1L, 14L), Event_ind = c(1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L), id = c(27L, 25L, 3L, 34L, 32L, 18L, 39L, 8L, 36L, 16L, 44L, 21L, 50L, 6L, 49L, 4L, 41L, 12L, 37L, 47L, 24L, 22L, 30L, 40L, 2L, 31L, 43L, 29L, 48L, 28L, 23L, 5L, 35L, 46L, 20L, 19L, 42L, 10L, 13L, 7L, 9L, 17L, 26L, 38L, 33L, 45L, 15L, 11L, 1L, 14L), X_obs_est = c(0.00399494590237737, 0.876960574486011, 0.00658195674547719, 0.00786697631701827, 0.0090918242931366, 0.0146137401461601, 0.0170951313339174, 0.336990794166923, 0.0290132522772983, 0.031725528959428, 0.0383799616247416, 2.01986489250305, 0.0885395100340247, 0.0899496711790562, 0.0988534230514452, 0.824081514920008, 0.129139679949731, 0.925526209133578, 1.88007667838576, 1.88007667838576, 0.824081514920008, 2.01986489250305, 0.824081514920008, 0.354189054574817, 0.824081514920008, 1.88007667838576, 0.760430300566268, 0.387182583566755, 0.336990794166923, 0.354189054574817, 0.383541618329485, 0.387182583566755, 0.838040319229376, 0.876960574486011, 0.519341270904988, 2.01986489250305, 1.88007667838576, 0.760430300566268, 2.01986489250305, 0.824081514920008, 0.838040319229376, 0.876960574486011, 1.88007667838576, 0.925526209133578, 2.01986489250305, 2.01986489250305, 2.01986489250305, 1.88007667838576, 2.01986489250305, 2.01986489250305 )), family = "binomial", weights = c(7591, 7911, 4103, 2666, 6097, 8512, 7307, 8575, 6101, 8291, 3323, 6753, 5580, 9604, 4034, 6406, 8653, 6750, 9390, 5496, 3857, 6947, 2840, 5313, 5309, 9871, 1283, 4638, 8219, 3769, 5755, 1685, 7823, 2202, 9847, 7595, 4801, 3791, 9933, 1200, 6692, 8397, 5737, 6748, 3523, 4014, 8949, 7683, 3727, 2154))
12: do.call(regression_type, args)
13: doTryCatch(return(expr), name, parentenv, handler)
14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
15: tryCatchList(expr, classes, parentenv, handlers)
16: tryCatch({ args <- args_for_fitting(csi_out, formula, regression_type, family, weights) do.call(regression_type, args)}, error = function(e) { csi_out_red <- dplyr::filter(csi_out, is.finite(csi_out[[est_name]])) if (verbose) message(paste("NaN, NA of Inf present, removing", dim(csi_out)[1] - dim(csi_out_red)[1], "/", dim(csi_out)[1], "values for fitting")) args <- args_for_fitting(csi_out_red, formula, regression_type, family, weights) do.call(regression_type, args)})
17: FUN(X[[i]], ...)
18: lapply(imputed_datasets, function(csi_out) { if (is(csi_out, "vector")) { if (sum(censored_bool) != length(csi_out)) { censored_bool <- set_last_as_observed(data, censored_variable, censoring_indicator)[[censoring_indicator]] == 0 stopifnot(sum(censored_bool) == length(csi_out)) } est_col <- data[[censored_variable]] est_col[censored_bool] <- csi_out csi_out <- cbind(data, est_col) colnames(csi_out)[dim(csi_out)[2]] <- est_name } max_est_name <- max(na.omit(csi_out[[est_name]])) cond_2 <- ifelse(is.na(max_est_name), FALSE, abs(max_est_name) < 1000 * abs(max(csi_out[[censored_variable]]))) if (!any(is.na(csi_out[[censored_variable]])) & cond_2 & (sum(csi_out[[censoring_indicator]]) > 2)) { if (binarise_covariate) { csi_out[[est_name]] <- binarised_covariate(csi_out[[est_name]]) } return(tryCatch({ args <- args_for_fitting(csi_out, formula, regression_type, family, weights) do.call(regression_type, args) }, error = function(e) { csi_out_red <- dplyr::filter(csi_out, is.finite(csi_out[[est_name]])) if (verbose) message(paste("NaN, NA of Inf present, removing", dim(csi_out)[1] - dim(csi_out_red)[1], "/", dim(csi_out)[1], "values for fitting")) args <- args_for_fitting(csi_out_red, formula, regression_type, family, weights) do.call(regression_type, args) })) }})
19: conditional_multiple_imputation_fitting(data = data, imputed_datasets = all_csi_out, censored_variable = censored_variable, censoring_indicator = censoring_indicator, response = response, covariates = covariates, id = id, formula = formula_uncens_est_name, regression_type = regression_type, mi_reps = mi_reps, verbose = verbose, weights = weights, contrasts = contrasts, family = family, binarise_covariate = binarise_covariate)
20: conditional_multiple_imputation(data = data_i, formula = formula$formula, mi_reps = mi_reps, imputation_method = imputation_method_cmi, regression_type = "glmer", family = "binomial", verbose = verbose, weights = "weights", contrasts = contrast)
21: withCallingHandlers(expr, warning = function(w) if (inherits(w, classes)) tryInvokeRestart("muffleWarning"))
22: suppressWarnings(conditional_multiple_imputation(data = data_i, formula = formula$formula, mi_reps = mi_reps, imputation_method = imputation_method_cmi, regression_type = "glmer", family = "binomial", verbose = verbose, weights = "weights", contrasts = contrast))
23: withCallingHandlers(expr, message = function(c) if (inherits(c, classes)) tryInvokeRestart("muffleMessage"))
24: suppressMessages(suppressWarnings(conditional_multiple_imputation(data = data_i, formula = formula$formula, mi_reps = mi_reps, imputation_method = imputation_method_cmi, regression_type = "glmer", family = "binomial", verbose = verbose, weights = "weights", contrasts = contrast)))
25: doTryCatch(return(expr), name, parentenv, handler)
26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: tryCatchList(expr, classes, parentenv, handlers)
28: tryCatch(suppressMessages(suppressWarnings(conditional_multiple_imputation(data = data_i, formula = formula$formula, mi_reps = mi_reps, imputation_method = imputation_method_cmi, regression_type = "glmer", family = "binomial", verbose = verbose, weights = "weights", contrasts = contrast))), error = function(e) NA)
29: FUN(...)
30: withCallingHandlers({ ERROR_CALL_DEPTH <<- (function() sys.nframe() - 1L)() FUN(...)}, error = function(e) { annotated_condition <- handle_error(e) stop(annotated_condition)}, warning = handle_warning)
31: doTryCatch(return(expr), name, parentenv, handler)
32: tryCatchOne(expr, names, parentenv, handlers[[1L]])
33: tryCatchList(expr, classes, parentenv, handlers)
34: tryCatch({ withCallingHandlers({ ERROR_CALL_DEPTH <<- (function() sys.nframe() - 1L)() FUN(...) }, error = function(e) { annotated_condition <- handle_error(e) stop(annotated_condition) }, warning = handle_warning)}, error = identity)
35: FUN(X[[i]], ...)
36: (function (X, FUN, ...) { FUN <- match.fun(FUN) if (!is.vector(X) || is.object(X)) X <- as.list(X) .Internal(lapply(X, FUN))})(X = 1:6, FUN = function (...) { if (!identical(timeout, WORKER_TIMEOUT)) { setTimeLimit(timeout, timeout, TRUE) on.exit(setTimeLimit(Inf, Inf, FALSE)) } if (!is.null(globalOptions)) base::options(globalOptions) if (stop.on.error && ERROR_OCCURRED) { UNEVALUATED } else { .rng_reset_generator("L'Ecuyer-CMRG", SEED) output <- tryCatch({ withCallingHandlers({ ERROR_CALL_DEPTH <<- (function() sys.nframe() - 1L)() FUN(...) }, error = function(e) { annotated_condition <- handle_error(e) stop(annotated_condition) }, warning = handle_warning) }, error = identity) if (force.GC) gc(verbose = FALSE, full = FALSE) SEED <<- .rng_next_substream(SEED) output }})
37: do.call(lapply, args)
38: BiocParallel:::.workerLapply_impl(...)
39: (function (...) BiocParallel:::.workerLapply_impl(...))(X = 1:6, FUN = function (i) { y <- counts[i, ]/weights data_i <- cbind(y, weights, formula$data) colnames(data_i)[c(1, 2)] <- c(cmi_input$response, "weights") if (imputation_method %in% c("mrl", "rs", "km", "km_exp", "km_wei", "km_os", "pmm")) { imputation_method_cmi <- ifelse(binarise_covariate, paste0(imputation_method, "_bin"), imputation_method) out_test <- tryCatch(suppressMessages(suppressWarnings(conditional_multiple_imputation(data = data_i, formula = formula$formula, mi_reps = mi_reps, imputation_method = imputation_method_cmi, regression_type = "glmer", family = "binomial", verbose = verbose, weights = "weights", contrasts = contrast))), error = function(e) NA) pooled_results <- mice::pool(out_test$fits) fraction_missing_information_u <- plogis(qlogis(max(pooled_results$pooled$fmi)) + qnorm(0.975) * sqrt(2/pooled_results$m)) that_many_imps <- ceiling(1 + 1/2 * (fraction_missing_information_u/0.05)^2) p_val <- tryCatch(summary(pooled_results)$p.value[which(contrast == 1)], error = function(e) NA) } else if (imputation_method == "cc") { p_val <- tryCatch({ fit <- suppressMessages(suppressWarnings(complete_case(data = data_i, censored_variable = cmi_input[["censored_variable"]], censoring_indicator = cmi_input[["censoring_indicator"]], formula = formula_glmm, regression_type = "glmer", weights = "weights", family = "binomial", binarise_covariate)$fits)) test <- multcomp::glht(fit, contrast) summary(test)$test$pvalues }, error = function(e) NA) } that_many_imps <- ifelse(exists("that_many_imps"), that_many_imps, NA) return(c(p_val, that_many_imps))}, ARGS = list(), OPTIONS = list(log = FALSE, threshold = "INFO", stop.on.error = TRUE, as.error = TRUE, timeout = NA_integer_, force.GC = FALSE, globalOptions = NULL), BPRNGSEED = c(10407L, 547683944L, -2089615031L, -1274083675L, 2098980991L, -2103009100L, 686002167L), GLOBALS = list(), PACKAGES = character(0))
40: do.call(msg$data$fun, msg$data$args)
41: doTryCatch(return(expr), name, parentenv, handler)
42: tryCatchOne(expr, names, parentenv, handlers[[1L]])
43: tryCatchList(expr, classes, parentenv, handlers)
44: tryCatch({ do.call(msg$data$fun, msg$data$args)}, error = function(e) { list(.error_worker_comm(e, "worker evaluation failed"))})
45: .bpworker_EXEC(msg, bplog(backend$BPPARAM))
46: .recv_any(manager$backend)
47: .recv_any(manager$backend)
48: .manager_recv(manager)
49: .manager_recv(manager)
50: .collect_result(manager, reducer, progress, BPPARAM)
51: .bploop_impl(ITER = ITER, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, BPREDO = BPREDO, reducer = reducer, progress.length = length(redo_index))
52: bploop.lapply(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, ...)
53: bploop(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, ...)
54: .bpinit(manager = manager, X = X, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, BPREDO = BPREDO)
55: BiocParallel::bplapply(X, FUN, BPPARAM = BPPARAM)
56: BiocParallel::bplapply(X, FUN, BPPARAM = BPPARAM)
57: maybe_parallel_lapply(seq_along(cluster_id), BPPARAM = BPPARAM, function(i) { y <- counts[i, ]/weights data_i <- cbind(y, weights, formula$data) colnames(data_i)[c(1, 2)] <- c(cmi_input$response, "weights") if (imputation_method %in% c("mrl", "rs", "km", "km_exp", "km_wei", "km_os", "pmm")) { imputation_method_cmi <- ifelse(binarise_covariate, paste0(imputation_method, "_bin"), imputation_method) out_test <- tryCatch(suppressMessages(suppressWarnings(conditional_multiple_imputation(data = data_i, formula = formula$formula, mi_reps = mi_reps, imputation_method = imputation_method_cmi, regression_type = "glmer", family = "binomial", verbose = verbose, weights = "weights", contrasts = contrast))), error = function(e) NA) pooled_results <- mice::pool(out_test$fits) fraction_missing_information_u <- plogis(qlogis(max(pooled_results$pooled$fmi)) + qnorm(0.975) * sqrt(2/pooled_results$m)) that_many_imps <- ceiling(1 + 1/2 * (fraction_missing_information_u/0.05)^2) p_val <- tryCatch(summary(pooled_results)$p.value[which(contrast == 1)], error = function(e) NA) } else if (imputation_method == "cc") { p_val <- tryCatch({ fit <- suppressMessages(suppressWarnings(complete_case(data = data_i, censored_variable = cmi_input[["censored_variable"]], censoring_indicator = cmi_input[["censoring_indicator"]], formula = formula_glmm, regression_type = "glmer", weights = "weights", family = "binomial", binarise_covariate)$fits)) test <- multcomp::glht(fit, contrast) summary(test)$test$pvalues }, error = function(e) NA) } that_many_imps <- ifelse(exists("that_many_imps"), that_many_imps, NA) return(c(p_val, that_many_imps)) })
58: testDA_censoredGLMM(d_counts = d_counts, formula = da_formula, contrast = contrast, mi_reps = 30, imputation_method = "km", verbose = TRUE, BPPARAM = BiocParallel::MulticoreParam(workers = 1))
59: eval(expr, envir, enclos)
60: eval(expr, envir, enclos)
61: eval_with_user_handlers(expr, envir, enclos, user_handlers)
62: withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers))
63: withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler)
64: handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler))
65: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler)))
66: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, debug = debug, last = i == length(out), use_try = stop_on_error != 2L, keep_warning = keep_warning, keep_message = keep_message, log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, include_timing = include_timing)
67: evaluate::evaluate(...)
68: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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))
69: in_dir(input_dir(), expr)
70: 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$render, options)))
71: eng_r(options)
72: block_exec(params)
73: call_block(x)
74: process_group.block(group)
75: process_group(group)
76: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e))
77: withCallingHandlers(expr, error = function(e) { loc = paste0(current_lines(), label, sprintf(" (%s)", knit_concord$get("infile"))) message(one_string(handler(e, loc)))})
78: handle_error(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e)), function(e, loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from lines ", loc) }, if (labels[i] != "") sprintf(" [%s]", labels[i]))
79: process_file(text, output)
80: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
81: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
82: vweave_rmarkdown(...)
83: engine$weave(file, quiet = quiet, encoding = enc)
84: doTryCatch(return(expr), name, parentenv, handler)
85: tryCatchOne(expr, names, parentenv, handlers[[1L]])
86: tryCatchList(expr, classes, parentenv, handlers)
87: tryCatch({ engine$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) } outputs <- c(outputs, output)}, error = function(e) { thisOK <<- FALSE fails <<- c(fails, file) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
88: tools::buildVignettes(dir = ".", tangle = TRUE)
An irrecoverable exception occurred. R is aborting now ...
