##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R-4.4-devel-2024.03.20/bin/R CMD build --keep-empty-dirs --no-resave-data benchdamic
###
##############################################################################
##############################################################################


* checking for file ‘benchdamic/DESCRIPTION’ ... OK
* preparing ‘benchdamic’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘intro.Rmd’ using rmarkdown

 *** caught segfault ***
address 0x40ff4000000000c4, cause 'memory not mapped'

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$initialize(...)
 4: initialize(value, ...)
 5: initialize(value, ...)
 6: methods::new(def, ...)
 7: (new("refMethodDef", .Data = function (...) {    methods::new(def, ...)}, mayCall = c("methods", "new"), name = "new", refClassName = "refGeneratorSlot",     superClassMethod = ""))(Zt = new("dgCMatrix", i = c(5L, 5L, 4L, 4L, 7L, 7L, 9L, 9L, 6L, 6L, 8L, 8L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 2L, 2L, 1L, 1L, 14L, 14L, 0L, 0L, 3L, 3L), p = 0:30,     Dim = c(15L, 30L), Dimnames = list(c("289996019", "355657046",     "404239096", "517810313", "763880905", "764002286", "764224817",     "764285508", "764305738", "764487809", "764508039", "764669880",     "765135172", "765701615", "863126187"), c("700023502", "700023503",     "700023596", "700023597", "700024199", "700024200", "700024305",     "700024306", "700024410", "700024411", "700024554", "700024555",     "700024698", "700024699", "700024915", "700024916", "700037245",     "700037246", "700038401", "700038402", "700038587", "700038588",     "700038911", "700038912", "700103572", "700103573", "700106347",     "700106348", "700106405", "700106406")), x = c(1, 1, 1, 1,     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,     1, 1, 1, 1, 1, 1, 1), factors = list()), theta = 1, Lambdat = new("dgCMatrix",     i = 0:14, p = 0:15, Dim = c(15L, 15L), Dimnames = list(NULL,         NULL), x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,     1), factors = list()), Lind = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), n = 30L, X = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1))
 8: do.call(merPredD$new, c(reTrms[c("Zt", "theta", "Lambdat", "Lind")],     n = nrow(X), list(X = X)))
 9: (function (fr, X, reTrms, REML = TRUE, start = NULL, verbose = 0,     control = lmerControl(), ...) {    p <- ncol(X)    rho <- new.env(parent = parent.env(environment()))    rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt", "theta",         "Lambdat", "Lind")], n = nrow(X), list(X = X)))    REMLpass <- if (REML)         p    else 0L    rho$resp <- if (missing(fr))         mkRespMod(REML = REMLpass, ...)    else mkRespMod(fr, REML = REMLpass)    pp <- resp <- NULL    rho$lmer_Deviance <- lmer_Deviance    devfun <- function(theta) .Call(lmer_Deviance, pp$ptr(),         resp$ptr(), as.double(theta))    environment(devfun) <- rho    if (is.null(start) && all(reTrms$cnms == "(Intercept)") &&         length(reTrms$flist) == length(reTrms$lower) && !is.null(y <- model.response(fr))) {        v <- sapply(reTrms$flist, function(f) var(ave(y, f)))        v.e <- var(y) - sum(v)        if (!is.na(v.e) && v.e > 0) {            v.rel <- v/v.e            if (all(v.rel >= reTrms$lower^2))                 rho$pp$setTheta(sqrt(v.rel))        }    }    if (length(rho$resp$y) > 0)         devfun(rho$pp$theta)    rho$lower <- reTrms$lower    devfun})(fr = list(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), HMP_BODY_SUBSITE = c(2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L), RSID = c(6L, 6L, 5L, 5L, 8L, 8L, 10L, 10L, 7L, 7L, 9L, 9L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 3L, 3L, 2L, 2L, 15L, 15L, 1L, 1L, 4L, 4L)), 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, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1), reTrms = list(Zt = new("dgCMatrix", i = c(5L, 5L, 4L, 4L, 7L, 7L, 9L, 9L, 6L, 6L, 8L, 8L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 2L, 2L, 1L, 1L, 14L, 14L, 0L, 0L, 3L, 3L), p = 0:30,     Dim = c(15L, 30L), Dimnames = list(c("289996019", "355657046",     "404239096", "517810313", "763880905", "764002286", "764224817",     "764285508", "764305738", "764487809", "764508039", "764669880",     "765135172", "765701615", "863126187"), c("700023502", "700023503",     "700023596", "700023597", "700024199", "700024200", "700024305",     "700024306", "700024410", "700024411", "700024554", "700024555",     "700024698", "700024699", "700024915", "700024916", "700037245",     "700037246", "700038401", "700038402", "700038587", "700038588",     "700038911", "700038912", "700103572", "700103573", "700106347",     "700106348", "700106405", "700106406")), 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), factors = list()), theta = 1, Lind = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Gp = c(0L, 15L), lower = 0, Lambdat = new("dgCMatrix", i = 0:14, p = 0:15,     Dim = c(15L, 15L), Dimnames = list(NULL, NULL), x = c(1,     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), factors = list()),     flist = list(RSID = c(6L, 6L, 5L, 5L, 8L, 8L, 10L, 10L, 7L,     7L, 9L, 9L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 3L, 3L,     2L, 2L, 15L, 15L, 1L, 1L, 4L, 4L)), cnms = list(RSID = "(Intercept)"),     Ztlist = list(`1 | RSID` = new("dgCMatrix", i = c(5L, 5L,     4L, 4L, 7L, 7L, 9L, 9L, 6L, 6L, 8L, 8L, 10L, 10L, 11L, 11L,     12L, 12L, 13L, 13L, 2L, 2L, 1L, 1L, 14L, 14L, 0L, 0L, 3L,     3L), p = 0:30, Dim = c(15L, 30L), Dimnames = list(c("289996019",     "355657046", "404239096", "517810313", "763880905", "764002286",     "764224817", "764285508", "764305738", "764487809", "764508039",     "764669880", "765135172", "765701615", "863126187"), c("700023502",     "700023503", "700023596", "700023597", "700024199", "700024200",     "700024305", "700024306", "700024410", "700024411", "700024554",     "700024555", "700024698", "700024699", "700024915", "700024916",     "700037245", "700037246", "700038401", "700038402", "700038587",     "700038588", "700038911", "700038912", "700103572", "700103573",     "700106347", "700106348", "700106405", "700106406")), 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), factors = list())), nl = c(RSID = 15L)),     REML = TRUE, wmsgs = character(0), start = NULL, verbose = 0L,     control = list(optimizer = "nloptwrap", restart_edge = TRUE,         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"),         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()))
10: do.call(mkLmerDevfun, c(lmod, list(start = start, verbose = verbose,     control = control)))
11: lme4::lmer(formula = tformula, data = test_data, control = lme_control,     na.action = na.omit)
12: eval(expr, p)
13: eval(expr, p)
14: eval.parent(mc)
15: tfun(formula = tformula, data = test_data, na.action = na.omit,     control = lme_control)
16: doTryCatch(return(expr), name, parentenv, handler)
17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
18: tryCatchList(expr, classes, parentenv, handlers)
19: 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))})
20: try(tfun(formula = tformula, data = test_data, na.action = na.omit,     control = lme_control), silent = TRUE)
21: eval(xpr, envir = envir)
22: eval(xpr, envir = envir)
23: doTryCatch(return(expr), name, parentenv, handler)
24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
25: tryCatchList(expr, classes, parentenv, handlers)
26: tryCatch(eval(xpr, envir = envir), error = function(e) e)
27: doTryCatch(return(expr), name, parentenv, handler)
28: tryCatchOne(expr, names, parentenv, handlers[[1L]])
29: tryCatchList(expr, classes, parentenv, handlers)
30: tryCatch({    repeat {        args <- nextElem(it)        if (obj$verbose) {            cat(sprintf("evaluation # %d:\n", i))            print(args)        }        for (a in names(args)) assign(a, args[[a]], pos = envir,             inherits = FALSE)        r <- tryCatch(eval(xpr, envir = envir), error = function(e) e)        if (obj$verbose) {            cat("result of evaluating expression:\n")            print(r)        }        tryCatch(accumulator(list(r), i), error = function(e) {            cat("error calling combine function:\n")            print(e)            NULL        })        i <- i + 1    }}, error = function(e) {    if (!identical(conditionMessage(e), "StopIteration"))         stop(simpleError(conditionMessage(e), expr))})
31: e$fun(obj, substitute(ex), parent.frame(), e$data)
32: foreach(idx1 = seq_len(n_tax), .combine = comb, .multicombine = TRUE) %dopar%     {        alr_data = apply(comp_table, 1, function(x) x - comp_table[idx1,             ])        alr_data = cbind(alr_data, meta_data)        p_vec = rep(NA, n_tax)        beta_vec = rep(NA, n_tax)        idx2 = NULL        if (is.null(rand_formula)) {            for (idx2 in seq_len(n_tax)) {                test_data = data.frame(x = alr_data[, idx2],                   meta_data, check.names = FALSE)                lm_fit = suppressWarnings(tfun(tformula, data = test_data))                p_vec[idx2] = summary(lm_fit)$coef[2, "Pr(>|t|)"]                beta_vec[idx2] = summary(lm_fit)$coef[2, "t value"]            }        }        else {            for (idx2 in seq_len(n_tax)) {                test_data = data.frame(x = alr_data[, idx2],                   meta_data, check.names = FALSE)                lme_fit = try(tfun(formula = tformula, data = test_data,                   na.action = na.omit, control = lme_control),                   silent = TRUE)                if (inherits(lme_fit, "try-error")) {                  p_vec[idx2] = NA                  beta_vec[idx2] = NA                }                else {                  summary_fit = summary(lme_fit)                  p_vec[idx2] = summary_fit$coefficients[2, "Pr(>|t|)"]                  beta_vec[idx2] = summary_fit$coefficients[2,                     "Estimate"]                }            }        }        list(p_vec, beta_vec)    }
33: ancom(phyloseq = phyloseq_obj, adj_formula = adj_formula, rand_formula = rand_formula,     lme_control = lme_control, p_adj_method = p_adj_method, prv_cut = 0,     lib_cut = 0, main_var = contrast[1], struc_zero = struc_zero,     neg_lb = neg_lb, alpha = alpha, n_cl = n_cl)
34: withCallingHandlers(expr, warning = function(w) if (inherits(w,     classes)) tryInvokeRestart("muffleWarning"))
35: suppressWarnings(ancom(phyloseq = phyloseq_obj, adj_formula = adj_formula,     rand_formula = rand_formula, lme_control = lme_control, p_adj_method = p_adj_method,     prv_cut = 0, lib_cut = 0, main_var = contrast[1], struc_zero = struc_zero,     neg_lb = neg_lb, alpha = alpha, n_cl = n_cl))
36: withCallingHandlers(expr, message = function(c) if (inherits(c,     classes)) tryInvokeRestart("muffleMessage"))
37: suppressMessages(suppressWarnings(ancom(phyloseq = phyloseq_obj,     adj_formula = adj_formula, rand_formula = rand_formula, lme_control = lme_control,     p_adj_method = p_adj_method, prv_cut = 0, lib_cut = 0, main_var = contrast[1],     struc_zero = struc_zero, neg_lb = neg_lb, alpha = alpha,     n_cl = n_cl)))
38: DA_ANCOM(object = new("phyloseq", otu_table = new("otu_table",     .Data = c(62, 724, 8, 132, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0,     0, 3, 0, 0, 0, 248, 137, 273, 348, 0, 12, 44, 141, 140, 49,     5, 0, 338, 2, 2708, 0, 0, 61, 23, 16, 240, 2, 21, 77, 3,     2, 7, 16, 621, 43, 412, 129, 380, 0, 0, 12, 4, 0, 0, 0, 0,     0, 0, 0, 4, 3, 1, 5, 296, 154, 206, 403, 0, 85, 70, 741,     64, 271, 3, 0, 134, 6, 1371, 0, 0, 14, 9, 18, 294, 1, 61,     100, 31, 12, 2, 16, 445, 271, 536, 21, 102, 0, 0, 0, 0, 3,     0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 152, 318, 1229, 0, 0, 22,     49, 101, 1, 14, 0, 1168, 145, 62, 0, 0, 8, 25, 0, 243, 0,     137, 56, 0, 27, 30, 39, 480, 172, 766, 13, 210, 0, 0, 0,     0, 7, 0, 0, 0, 13, 0, 0, 7, 0, 0, 2, 18, 372, 430, 1801,     4, 9, 45, 157, 101, 31, 24, 0, 530, 110, 48, 0, 1, 2, 34,     0, 251, 0, 193, 45, 1, 31, 24, 38, 432, 142, 639, 18, 189,     0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 4, 0, 4, 0, 1, 43, 144, 539,     495, 2, 0, 160, 233, 690, 0, 23, 0, 94, 31, 36, 0, 0, 29,     66, 0, 12, 2, 291, 175, 12, 9, 11, 4, 214, 338, 530, 14,     163, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 23, 78,     498, 370, 6, 0, 40, 221, 599, 2, 21, 0, 293, 34, 23, 0, 0,     29, 180, 0, 9, 3, 145, 94, 6, 28, 3, 3, 223, 78, 31, 35,     51, 1, 2, 0, 0, 29, 2, 1, 7, 5, 3, 1, 4, 0, 1, 0, 25, 487,     1214, 699, 0, 49, 22, 20, 20, 28, 3, 0, 517, 3, 409, 81,     0, 3, 0, 3, 384, 0, 41, 48, 0, 30, 0, 47, 1795, 46, 147,     48, 554, 0, 0, 2, 0, 30, 3, 4, 17, 10, 16, 1, 8, 0, 0, 0,     12, 187, 262, 644, 0, 123, 33, 253, 26, 315, 25, 0, 88, 3,     110, 128, 0, 6, 43, 0, 31, 0, 3, 31, 7, 49, 0, 5, 469, 497,     618, 2, 183, 1, 0, 0, 0, 9, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0,     147, 195, 420, 1172, 0, 2, 19, 54, 100, 3, 17, 0, 654, 18,     179, 0, 0, 22, 169, 17, 587, 0, 53, 5, 0, 52, 9, 60, 502,     425, 559, 3, 156, 0, 0, 0, 0, 25, 0, 0, 1, 0, 0, 0, 17, 0,     0, 0, 117, 100, 250, 952, 0, 0, 37, 59, 119, 4, 26, 1, 438,     31, 132, 0, 7, 20, 145, 13, 188, 0, 105, 13, 2, 41, 13, 43,     303, 116, 1122, 0, 20, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0,     0, 0, 0, 2, 47, 165, 1912, 0, 0, 59, 13, 103, 0, 8, 0, 961,     246, 562, 1, 0, 63, 207, 3, 375, 0, 1923, 9, 0, 8, 95, 19,     1408, 708, 2642, 1, 384, 0, 0, 0, 1, 37, 0, 0, 0, 0, 0, 0,     14, 6, 0, 1, 12, 45, 443, 2161, 0, 0, 233, 171, 679, 1, 11,     3, 1457, 319, 1149, 29, 0, 461, 55, 12, 391, 0, 1499, 161,     0, 59, 88, 34, 1709, 187, 356, 7, 239, 0, 1, 0, 0, 64, 0,     0, 5, 10, 0, 0, 6, 0, 0, 1, 84, 113, 430, 753, 0, 0, 39,     195, 362, 13, 23, 0, 172, 82, 93, 0, 0, 26, 28, 3, 446, 0,     302, 50, 0, 8, 2, 5, 838, 144, 228, 7, 336, 0, 2, 0, 2, 35,     0, 0, 2, 0, 0, 0, 12, 0, 0, 0, 8, 53, 62, 413, 0, 0, 21,     218, 111, 33, 6, 0, 394, 8, 1152, 0, 0, 2, 175, 1, 187, 0,     81, 64, 1, 10, 117, 11, 1325, 8, 704, 0, 25, 0, 0, 2, 0,     3, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 782, 6, 198, 0, 6, 14,     65, 35, 10, 21, 0, 882, 0, 192, 0, 0, 9, 27, 0, 309, 0, 6,     0, 0, 18, 8, 28, 416, 19, 971, 1, 148, 0, 0, 1, 0, 83, 0,     0, 0, 1, 0, 2, 19, 1, 0, 0, 18, 279, 21, 286, 0, 14, 37,     292, 33, 38, 33, 0, 465, 5, 23, 0, 0, 18, 99, 0, 151, 0,     37, 1, 0, 26, 4, 25, 290, 2, 510, 0, 103, 7, 0, 0, 0, 9,     0, 0, 4, 0, 0, 1, 0, 0, 0, 0, 2, 387, 480, 2186, 0, 0, 12,     296, 1, 0, 8, 2, 73, 758, 5, 0, 0, 0, 4, 0, 114, 0, 420,     5, 10, 11, 0, 13, 494, 3, 97, 0, 933, 0, 0, 0, 0, 18, 0,     0, 0, 0, 0, 63, 2, 0, 1, 1, 4, 823, 158, 2893, 0, 0, 122,     1292, 0, 8, 11, 0, 99, 248, 2, 0, 0, 1, 12, 0, 108, 2, 1559,     47, 85, 43, 0, 33, 883, 338, 897, 47, 446, 0, 0, 1, 1, 54,     3, 0, 3, 2, 0, 1, 16, 0, 0, 0, 25, 25, 328, 383, 0, 17, 148,     120, 350, 175, 16, 0, 167, 33, 6, 16, 0, 45, 99, 0, 544,     0, 258, 573, 10, 5, 8, 35, 634, 551, 1166, 178, 217, 0, 0,     1, 0, 10, 0, 0, 6, 0, 0, 0, 14, 0, 0, 0, 2, 11, 316, 88,     0, 39, 47, 163, 187, 266, 18, 0, 154, 22, 18, 26, 0, 7, 208,     0, 58, 0, 26, 488, 5, 27, 4, 7, 200, 489, 2781, 5, 52, 0,     1, 0, 0, 6, 0, 0, 0, 0, 0, 2, 2, 1, 0, 1, 311, 345, 1186,     3061, 0, 0, 36, 397, 287, 5, 246, 0, 1923, 52, 98, 14, 2,     54, 191, 25, 231, 0, 171, 33, 0, 93, 156, 42, 2469, 163,     835, 12, 420, 0, 0, 0, 3, 51, 0, 0, 7, 0, 8, 0, 22, 9, 0,     1, 132, 99, 188, 628, 0, 163, 74, 868, 71, 249, 126, 0, 287,     7, 32, 0, 3, 9, 40, 1, 23, 22, 44, 125, 29, 15, 15, 8, 859,     93, 574, 23, 82, 0, 0, 0, 0, 2, 0, 3, 0, 0, 0, 0, 7, 9, 0,     0, 6, 3, 691, 565, 0, 0, 29, 117, 124, 8, 5, 0, 56, 71, 2,     0, 0, 2, 0, 0, 46, 0, 281, 123, 2, 6, 0, 5, 392, 35, 678,     32, 446, 1, 1, 0, 1, 15, 0, 7, 0, 0, 0, 0, 44, 8, 0, 3, 21,     13, 820, 837, 0, 3, 46, 757, 87, 18, 7, 0, 11, 10, 0, 0,     0, 3, 0, 0, 76, 0, 230, 165, 10, 14, 0, 8, 202, 1005, 2241,     7, 153, 0, 0, 0, 0, 6, 0, 1, 0, 0, 0, 13, 4, 0, 0, 8, 1,     188, 1903, 716, 0, 2, 240, 447, 3195, 10, 49, 0, 390, 738,     10, 0, 0, 11, 319, 1, 346, 0, 1521, 372, 5, 17, 0, 31, 2562,     148, 1069, 34, 684, 0, 0, 0, 4, 7, 0, 5, 0, 0, 0, 15, 13,     21, 1, 17, 0, 119, 580, 524, 0, 2, 126, 243, 837, 79, 8,     1, 49, 607, 2, 0, 0, 7, 15, 0, 164, 10, 1515, 261, 54, 16,     0, 12, 1343, 294, 1288, 1, 21, 0, 1, 0, 0, 1, 0, 0, 0, 0,     0, 1, 0, 0, 0, 0, 425, 352, 1767, 1143, 0, 0, 16, 19, 415,     0, 36, 1, 1451, 38, 1648, 65, 0, 98, 23, 15, 142, 0, 223,     2, 0, 24, 110, 23, 906, 1249, 1328, 79, 102, 0, 1, 0, 2,     11, 0, 0, 0, 0, 0, 0, 2, 1, 0, 17, 47, 30, 1579, 728, 0,     6, 114, 294, 585, 20, 8, 0, 278, 33, 190, 1, 1, 26, 21, 1,     115, 0, 567, 534, 6, 15, 6, 34, 449, 3, 1232, 0, 5, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 180, 1, 312, 0,     0, 12, 35, 117, 0, 6, 1, 1625, 33, 210, 0, 0, 5, 0, 8, 190,     0, 139, 0, 0, 3, 15, 70, 537, 3, 1298, 0, 0, 0, 0, 0, 0,     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 640, 1, 621, 0, 0, 3,     1, 247, 0, 9, 0, 3036, 72, 354, 0, 0, 3, 0, 8, 317, 0, 156,     0, 0, 2, 64, 145, 1212), taxa_are_rows = TRUE), tax_table = new("taxonomyTable",     .Data = c("Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteria",     "Bacteria", "Bacteria", "Bacteria", "Bacteria", "Bacteroidetes",     "Bacteroidetes", "Bacteroidetes", "Bacteroidetes", "Bacteroidetes",     "Firmicutes", "Tenericutes", "Firmicutes", "Firmicutes",     "Firmicutes", "Firmicutes", "Firmicutes", "Firmicutes", "Firmicutes",     "Firmicutes", "Firmicutes", "Firmicutes", "Actinobacteria",     "Actinobacteria", "Actinobacteria", "Actinobacteria", "Actinobacteria",     "Actinobacteria", "Actinobacteria", "Synergistetes", "Proteobacteria",     "Fusobacteria", "Fusobacteria", "Spirochaetes", "Proteobacteria",     "Proteobacteria", "Proteobacteria", "Proteobacteria", "Proteobacteria",     "Proteobacteria", "Proteobacteria", "Proteobacteria", "Proteobacteria",     "Proteobacteria", "Proteobacteria", "Firmicutes", "Firmicutes",     "Firmicutes", "Firmicutes", "Firmicutes", "Firmicutes", "Firmicutes",     "Firmicutes", "Bacteroidia", "Flavobacteria", "Bacteroidia",     "Bacteroidia", "Bacteroidia", "Bacilli", "Mollicutes", "Clostridia",     "Clostridia", "Clostridia", "Clostridia", "Clostridia", "Clostridia",     "Clostridia", "Clostridia", "Clostridia", "Clostridia", "Actinobacteria",     "Actinobacteria", "Actinobacteria", "Actinobacteria", "Actinobacteria",     "Actinobacteria", "Actinobacteria", "Synergistia", "Epsilonproteobacteria",     "Fusobacteria", "Fusobacteria", "Spirochaetes", "Betaproteobacteria",     "Betaproteobacteria", "Betaproteobacteria", "Betaproteobacteria",     "Betaproteobacteria", "Betaproteobacteria", "Betaproteobacteria",     "Gammaproteobacteria", "Gammaproteobacteria", "Gammaproteobacteria",     "Gammaproteobacteria", "Clostridia", "Clostridia", "Clostridia",     "Clostridia", "Bacilli", "Bacilli", "Bacilli", "Bacilli",     "Bacteroidales", "Flavobacteriales", "Bacteroidales", "Bacteroidales",     "Bacteroidales", "Lactobacillales", "Mycoplasmatales", "Clostridiales",     "Clostridiales", "Clostridiales", "Clostridiales", "Clostridiales",     "Clostridiales", "Clostridiales", "Clostridiales", "Clostridiales",     "Clostridiales", "Coriobacteriales", "Coriobacteriales",     "Actinomycetales", "Actinomycetales", "Actinomycetales",     "Actinomycetales", "Bifidobacteriales", "Synergistales",     "Campylobacterales", "Fusobacteriales", "Fusobacteriales",     "Spirochaetales", "Neisseriales", "Neisseriales", "Neisseriales",     "Neisseriales", "Burkholderiales", "Burkholderiales", "Rhodocyclales",     "Cardiobacteriales", "Pasteurellales", "Pasteurellales",     "Pasteurellales", "Clostridiales", "Clostridiales", "Clostridiales",     "Clostridiales", "Gemellales", "Lactobacillales", "Lactobacillales",     "Lactobacillales", "Porphyromonadaceae", "Flavobacteriaceae",     "Porphyromonadaceae", "Prevotellaceae", "Bacteroidaceae",     "Lactobacillaceae", "Mycoplasmataceae", "Clostridiales Family XIII. Incertae Sedis",     "Eubacteriaceae", "Peptostreptococcaceae", "Peptostreptococcaceae",     "Peptococcaceae", "Lachnospiraceae", "Lachnospiraceae", "Lachnospiraceae",     "Lachnospiraceae", "Lachnospiraceae", "Coriobacteriaceae",     "Coriobacteriaceae", "Propionibacteriaceae", "Micrococcaceae",     "Corynebacteriaceae", "Actinomycetaceae", "Bifidobacteriaceae",     "Dethiosulfovibrionaceae", "Campylobacteraceae", "Fusobacteriaceae",     "Fusobacteriaceae", "Spirochaetaceae", "Neisseriaceae", "Neisseriaceae",     "Neisseriaceae", "Neisseriaceae", "Burkholderiaceae", "Comamonadaceae",     "Rhodocyclaceae", "Cardiobacteriaceae", "Pasteurellaceae",     "Pasteurellaceae", "Pasteurellaceae", "Veillonellaceae",     "Veillonellaceae", "Veillonellaceae", "Veillonellaceae",     "Gemellaceae", "Aerococcaceae", "Carnobacteriaceae", "Streptococcaceae",     "Porphyromonas", "Capnocytophaga", "Tannerella", "Prevotella",     "Bacteroides", "Lactobacillus", "Mycoplasma", "Mogibacterium",     "Eubacterium", "Filifactor", "Peptostreptococcus", "Peptococcus",     "Johnsonella", "Butyrivibrio", "Moryella", "Catonella", "Oribacterium",     "Slackia", "Atopobium", "Propionibacterium", "Rothia", "Corynebacterium",     "Actinomyces", "Scardovia", "TG5", "Campylobacter", "Fusobacterium",     "Leptotrichia", "Treponema", "Eikenella", "Simonsiella",     "Neisseria", "Kingella", "Lautropia", "Brachymonas", "Rhodocyclus",     "Cardiobacterium", "Aggregatibacter", "Actinobacillus", "Haemophilus",     "Anaeroglobus", "Veillonella", "Selenomonas", "Dialister",     "Gemella", "Abiotrophia", "Granulicatella", "Streptococcus"    )), sam_data = new("sample_data", .Data = list(c(6L, 6L, 5L, 5L, 8L, 8L, 10L, 10L, 7L, 7L, 9L, 9L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 3L, 3L, 2L, 2L, 15L, 15L, 1L, 1L, 4L, 4L),     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), c("Male", "Male", "Male", "Male", "Male", "Male", "Male",     "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",     "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",     "Male", "Male", "Male", "Male", "Male", "Male", "Male"),     c("WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC",     "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC",     "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC",     "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC", "WUGC"),     c("Oral", "Oral", "Oral", "Oral", "Oral", "Oral", "Oral",     "Oral", "Oral", "Oral", "Oral", "Oral", "Oral", "Oral", "Oral",     "Oral", "Oral", "Oral", "Oral", "Oral", "Oral", "Oral", "Oral",     "Oral", "Oral", "Oral", "Oral", "Oral", "Oral", "Oral"),     c(2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,     2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,     2L), c("SRS014873", "SRS014875", "SRS014964", "SRS014966",     "SRS015574", "SRS015576", "SRS015681", "SRS015683", "SRS015786",     "SRS015788", "SRS015929", "SRS015931", "SRS016072", "SRS016074",     "SRS016281", "SRS016283", "SRS018803", "SRS018805", "SRS042619",     "SRS044435", "SRS046986", "SRS057069", "SRS050155", "SRS043032",     "SRS048383", "SRS052682", "SRS055531", "SRS049521", "SRS049648",     "SRS053814"), c(0.777630873753143, 0.786472287361574, 0.906796391871063,     0.923217695841301, 1.54399995525132, 1.35085744373699, 1.32405011404354,     1.97473582743833, 1.29287060827516, 1.60709711803974, 0.837933340278819,     0.610507134743912, 1.13723944076883, 1.00974952425423, 1.01848609546504,     1.10419426006893, 0.713442517326854, 0.675302343031048, 1.16482939779994,     1.54563617041772, 1.07722462404126, 1.28733305323415, 0.89517329027175,     1.05972300825373, 0.758838160825813, 1.01802307626761, 0.462626327216744,     1.00487578015778, 0.685805570256115, 0.928370557650138),     c(0.782952137641904, 0.899011026900801, 0.681219417862869,     1.19846013019707, 1.17757985401734, 1.01111337492461, 1.20593426472046,     1.19001309416578, 0.89625925954053, 0.932662763997248, 0.898589544722116,     2.32074818815014, 0.999819001442702, 0.793013814468651, 0.667826034202701,     0.810110629539811, 0.571850909162408, 0.944755418121269,     1.30395793376774, 0.95573788467356, 1.69063369547443, 1.26267323956642,     0.524519726901756, 0.78520771691694, 1.17517912130772, 1.56926494720658,     1.09929113059858, 1.1617451144102, 0.680448986229038, 1.43567159114354    ), c(167, 245, 244, 259, 167, 176, 154, 240, 234, 254, 255,     409, 227, 161, 95, 173, 71, 188, 223, 204, 333, 266, 83,     115, 176, 190, 222, 175, 93, 166)), names = c("RSID", "VISITNO", "SEX", "RUN_CENTER", "HMP_BODY_SITE", "HMP_BODY_SUBSITE", "SRS_SAMPLE_ID", "NF.TMM", "NF.poscounts", "NF.CSS"), row.names = c("700023502", "700023503", "700023596", "700023597", "700024199", "700024200", "700024305", "700024306", "700024410", "700024411", "700024554", "700024555", "700024698", "700024699", "700024915", "700024916", "700037245", "700037246", "700038401", "700038402", "700038587", "700038588", "700038911", "700038912", "700103572", "700103573", "700106347", "700106348", "700106405", "700106406"), .S3Class = "data.frame"),     phy_tree = list(edge = c(49L, 50L, 51L, 51L, 52L, 52L, 53L,     53L, 54L, 54L, 50L, 55L, 56L, 56L, 57L, 58L, 58L, 59L, 60L,     61L, 62L, 62L, 63L, 63L, 61L, 60L, 59L, 64L, 65L, 66L, 66L,     67L, 68L, 68L, 67L, 65L, 64L, 69L, 70L, 71L, 72L, 72L, 71L,     73L, 73L, 74L, 74L, 75L, 75L, 76L, 76L, 70L, 77L, 77L, 78L,     79L, 79L, 80L, 80L, 78L, 69L, 81L, 82L, 83L, 84L, 85L, 85L,     84L, 83L, 82L, 86L, 86L, 87L, 87L, 81L, 88L, 88L, 89L, 89L,     90L, 90L, 57L, 91L, 92L, 92L, 93L, 93L, 91L, 55L, 94L, 94L,     95L, 95L, 49L, 50L, 51L, 1L, 52L, 2L, 53L, 3L, 54L, 4L, 5L,     55L, 56L, 6L, 57L, 58L, 7L, 59L, 60L, 61L, 62L, 8L, 63L,     9L, 10L, 11L, 12L, 64L, 65L, 66L, 13L, 67L, 68L, 14L, 15L,     16L, 17L, 69L, 70L, 71L, 72L, 18L, 19L, 73L, 20L, 74L, 21L,     75L, 22L, 76L, 23L, 24L, 77L, 25L, 78L, 79L, 26L, 80L, 27L,     28L, 29L, 81L, 82L, 83L, 84L, 85L, 30L, 31L, 32L, 33L, 86L,     34L, 87L, 35L, 36L, 88L, 37L, 89L, 38L, 90L, 39L, 40L, 91L,     92L, 41L, 93L, 42L, 43L, 44L, 94L, 45L, 95L, 46L, 47L, 48L    ), edge.length = c(0.07985, 0.4538, 0.08162, 0.02485, 0.7281,     0.03581, 0.07629, 0.0951, 0.602, 0.04869, 0.01955, 0.04267,     0.18831, 0.13693, 0.01466, 0.59457, 0.0246, 0.00899, 0.04711,     0.00015, 0.19144, 0.03743, 0.1169, 0.04977, 0.10881, 0.17221,     0.00923, 0.16488, 0.00747, 0.12302, 0.00692, 0.00723, 0.08664,     0.05842, 0.11106, 0.10138, 0.02358, 0.03289, 0.08824, 0.09689,     0.21583, 0.03581, 0.06853, 0.14835, 0.06058, 0.08025, 0.0199,     0.13191, 0.03128, 0.03595, 0.19078, 0.00624, 0.36621, 0.03126,     0.05214, 0.32222, 0.18388, 0.07486, 0.40147, 0.56049, 0.14264,     0.08883, 0.03682, 0.00945, 0.05036, 0.00831, 0.04766, 0.05833,     0.04503, 0.02602, 0.09935, 0.0094, 0.20217, 0.12779, 0.02148,     0.19368, 0.14862, 0.07187, 0.00911, 0.07346, 0.02753, 0.14482,     0.03917, 0.06138, 0.00697, 0.0571, 0.15039, 0.09712, 0.01174,     0.09867, 0.1165, 0.10151, 0.02128, 0.0033), Nnode = 47L,         node.label = c("0.533", "0.937", "0.959", "0.735", "0.962",         "0.873", "0.950", "0.935", "0.898", "0.256", "0.982",         "0.864", "0.900", "0.574", "0.717", "0.513", "0.869",         "0.926", "0.874", "0.469", "0.986", "0.717", "0.253",         "0.777", "0.991", "0.939", "0.994", "0.879", "0.215",         "0.977", "0.858", "0.985", "0.768", "0.902", "0.999",         "0.940", "0.948", "0.927", "0.897", "0.777", "0.949",         "0.794", "0.902", "0.953", "0.883", "0.366", "0.890"),         tip.label = c("OTU_97.43147", "OTU_97.36", "OTU_97.170",         "OTU_97.63", "OTU_97.39258", "OTU_97.43909", "OTU_97.467",         "OTU_97.8114", "OTU_97.176", "OTU_97.330", "OTU_97.92",         "OTU_97.9844", "OTU_97.519", "OTU_97.623", "OTU_97.34541",         "OTU_97.38219", "OTU_97.334", "OTU_97.1371", "OTU_97.16522",         "OTU_97.178", "OTU_97.20539", "OTU_97.45016", "OTU_97.41422",         "OTU_97.759", "OTU_97.37368", "OTU_97.357", "OTU_97.38234",         "OTU_97.84", "OTU_97.27684", "OTU_97.135", "OTU_97.42042",         "OTU_97.37954", "OTU_97.204", "OTU_97.40084", "OTU_97.12613",         "OTU_97.883", "OTU_97.181", "OTU_97.136", "OTU_97.45196",         "OTU_97.44941", "OTU_97.405", "OTU_97.42864", "OTU_97.98",         "OTU_97.33295", "OTU_97.36154", "OTU_97.22389", "OTU_97.20",         "OTU_97.45246")), refseq = NULL), verbose = FALSE, assay_name = "counts",     pseudo_count = FALSE, adj_formula = NULL, rand_formula = "(1|RSID)",     lme_control = list(optimizer = "nloptwrap", restart_edge = TRUE,         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"),         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()),     contrast = c("HMP_BODY_SUBSITE", "Supragingival Plaque",     "Subgingival Plaque"), n_cl = 1, alpha = 0.05, p_adj_method = "BH",     struc_zero = FALSE, BC = FALSE)
39: do.call(what = method, args = args_list)
40: FUN(X[[i]], ...)
41: lapply(X = method_list, FUN = function(x) {    method <- as.character(x[["method"]])    if (verbose)         message("      * Running now:", method, "\n")    params <- unlist(lapply(x[-1], paste, collapse = "."))    param_names <- paste(names(x[-1]))    if (verbose)         message("        Parameters:", paste(param_names, "=",             params, sep = "", collapse = ", "), "\n")    if (is.element(el = "weights", set = names(x)))         if (x[["weights"]]) {            x["weights"] <- NULL            x <- append(x = x, values = list(weights = weights))        }        else x["weights"] <- NULL    args_list <- append(x = x[-1], values = list(object = object,         verbose = verbose), after = 0)    do.call(what = method, args = args_list)})
42: doTryCatch(return(expr), name, parentenv, handler)
43: tryCatchOne(expr, names, parentenv, handlers[[1L]])
44: tryCatchList(expr, classes, parentenv, handlers)
45: tryCatch(expr = {    out <- lapply(X = method_list, FUN = function(x) {        method <- as.character(x[["method"]])        if (verbose)             message("      * Running now:", method, "\n")        params <- unlist(lapply(x[-1], paste, collapse = "."))        param_names <- paste(names(x[-1]))        if (verbose)             message("        Parameters:", paste(param_names,                 "=", params, sep = "", collapse = ", "), "\n")        if (is.element(el = "weights", set = names(x)))             if (x[["weights"]]) {                x["weights"] <- NULL                x <- append(x = x, values = list(weights = weights))            }            else x["weights"] <- NULL        args_list <- append(x = x[-1], values = list(object = object,             verbose = verbose), after = 0)        do.call(what = method, args = args_list)    })    names(out) <- unlist(lapply(out, FUN = function(x) x[["name"]]))    return(out)}, error = function(e) {    stop(conditionMessage(e))})
46: runDA(method_list = method_list, object = pfo, weights = weights,     verbose = verbose)
47: (function (splits, i) {    if (verbose)         message("  - Comparison", i, "\n")    if (verbose)         message("    Splitting the samples...")    counts_and_metadata <- get_counts_metadata(object, assay_name = assay_name)    counts <- counts_and_metadata[[1]]    is_phyloseq <- counts_and_metadata[[3]]    if (is_phyloseq) {        po <- phyloseq::prune_samples(phyloseq::sample_names(object) %in%             splits, object)        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        pfo <- phyloseq::filter_taxa(po, function(x) sum(x >             min_counts) > min_samples, 1)    }    else {        po <- object[, splits]        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        taxa_to_keep <- apply(counts, 1, function(x) sum(x >             min_counts) > min_samples)        pfo <- po[taxa_to_keep, ]    }    if (!is.null(normalization_list)) {        if (verbose)             message("    Computing normalizations...")        pfo <- runNormalizations(normalization_list = normalization_list,             object = pfo, verbose = verbose)    }    weights_info <- unlist(lapply(X = method_list, FUN = function(x) {        x[["weights"]]    }))    if (sum(weights_info) > 0) {        if (verbose)             message("    Computing ZINB weights...")        weights <- weights_ZINB(pfo, design = ~1)    }    else weights <- NULL    if (verbose)         message("    Differential abundance:")    runDA(method_list = method_list, object = pfo, weights = weights,         verbose = verbose)})(dots[[1L]][[1L]], dots[[2L]][[1L]])
48: .mapply(.FUN, dots, .MoreArgs)
49: FUN(...)
50: withCallingHandlers({    ERROR_CALL_DEPTH <<- (function() sys.nframe() - 1L)()    FUN(...)}, error = function(e) {    annotated_condition <- handle_error(e)    stop(annotated_condition)}, warning = handle_warning)
51: doTryCatch(return(expr), name, parentenv, handler)
52: tryCatchOne(expr, names, parentenv, handlers[[1L]])
53: tryCatchList(expr, classes, parentenv, handlers)
54: 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)
55: FUN(X[[i]], ...)
56: (function (X, FUN, ...) {    FUN <- match.fun(FUN)    if (!is.vector(X) || is.object(X))         X <- as.list(X)    .Internal(lapply(X, FUN))})(X = list(Comparison1 = list(list(c("700023502", "700023503", "700023596", "700023597", "700024199", "700024200", "700024305", "700024306", "700024410", "700024411", "700024554", "700024555", "700024698", "700024699", "700024915", "700024916", "700037245", "700037246", "700038401", "700038402", "700038587", "700038588", "700038911", "700038912", "700103572", "700103573", "700106347", "700106348", "700106405", "700106406")), 1L), Comparison2 = list(    list(c("700023549", "700023550", "700024095", "700024096",     "700024305", "700024306", "700024410", "700024411", "700024554",     "700024555", "700024602", "700024603", "700024644", "700024645",     "700024698", "700024699", "700024915", "700024916", "700037040",     "700037041", "700038401", "700038402", "700038863", "700038864",     "700105931", "700105932", "700106405", "700106406", "700106710",     "700106711")), 2L)), 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    }}, .FUN = function (splits, i) {    if (verbose)         message("  - Comparison", i, "\n")    if (verbose)         message("    Splitting the samples...")    counts_and_metadata <- get_counts_metadata(object, assay_name = assay_name)    counts <- counts_and_metadata[[1]]    is_phyloseq <- counts_and_metadata[[3]]    if (is_phyloseq) {        po <- phyloseq::prune_samples(phyloseq::sample_names(object) %in%             splits, object)        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        pfo <- phyloseq::filter_taxa(po, function(x) sum(x >             min_counts) > min_samples, 1)    }    else {        po <- object[, splits]        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        taxa_to_keep <- apply(counts, 1, function(x) sum(x >             min_counts) > min_samples)        pfo <- po[taxa_to_keep, ]    }    if (!is.null(normalization_list)) {        if (verbose)             message("    Computing normalizations...")        pfo <- runNormalizations(normalization_list = normalization_list,             object = pfo, verbose = verbose)    }    weights_info <- unlist(lapply(X = method_list, FUN = function(x) {        x[["weights"]]    }))    if (sum(weights_info) > 0) {        if (verbose)             message("    Computing ZINB weights...")        weights <- weights_ZINB(pfo, design = ~1)    }    else weights <- NULL    if (verbose)         message("    Differential abundance:")    runDA(method_list = method_list, object = pfo, weights = weights,         verbose = verbose)}, .MoreArgs = NULL)
57: do.call(lapply, args)
58: BiocParallel:::.workerLapply_impl(...)
59: (function (...) BiocParallel:::.workerLapply_impl(...))(X = list(Comparison1 = list(    list(c("700023502", "700023503", "700023596", "700023597",     "700024199", "700024200", "700024305", "700024306", "700024410",     "700024411", "700024554", "700024555", "700024698", "700024699",     "700024915", "700024916", "700037245", "700037246", "700038401",     "700038402", "700038587", "700038588", "700038911", "700038912",     "700103572", "700103573", "700106347", "700106348", "700106405",     "700106406")), 1L), Comparison2 = list(list(c("700023549", "700023550", "700024095", "700024096", "700024305", "700024306", "700024410", "700024411", "700024554", "700024555", "700024602", "700024603", "700024644", "700024645", "700024698", "700024699", "700024915", "700024916", "700037040", "700037041", "700038401", "700038402", "700038863", "700038864", "700105931", "700105932", "700106405", "700106406", "700106710", "700106711")), 2L)), FUN = function (dots,     .FUN, .MoreArgs) {    .mapply(.FUN, dots, .MoreArgs)[[1L]]}, ARGS = list(.FUN = function (splits, i) {    if (verbose)         message("  - Comparison", i, "\n")    if (verbose)         message("    Splitting the samples...")    counts_and_metadata <- get_counts_metadata(object, assay_name = assay_name)    counts <- counts_and_metadata[[1]]    is_phyloseq <- counts_and_metadata[[3]]    if (is_phyloseq) {        po <- phyloseq::prune_samples(phyloseq::sample_names(object) %in%             splits, object)        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        pfo <- phyloseq::filter_taxa(po, function(x) sum(x >             min_counts) > min_samples, 1)    }    else {        po <- object[, splits]        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        taxa_to_keep <- apply(counts, 1, function(x) sum(x >             min_counts) > min_samples)        pfo <- po[taxa_to_keep, ]    }    if (!is.null(normalization_list)) {        if (verbose)             message("    Computing normalizations...")        pfo <- runNormalizations(normalization_list = normalization_list,             object = pfo, verbose = verbose)    }    weights_info <- unlist(lapply(X = method_list, FUN = function(x) {        x[["weights"]]    }))    if (sum(weights_info) > 0) {        if (verbose)             message("    Computing ZINB weights...")        weights <- weights_ZINB(pfo, design = ~1)    }    else weights <- NULL    if (verbose)         message("    Differential abundance:")    runDA(method_list = method_list, object = pfo, weights = weights,         verbose = verbose)}, .MoreArgs = NULL), OPTIONS = list(log = FALSE, threshold = "INFO",     stop.on.error = TRUE, as.error = TRUE, timeout = NA_integer_,     force.GC = FALSE, globalOptions = NULL), BPRNGSEED = c(10407L, -596023605L, -43277878L, -979600964L, 616258808L, -363330594L, 587017769L), GLOBALS = list(), PACKAGES = character(0))
60: do.call(msg$data$fun, msg$data$args)
61: doTryCatch(return(expr), name, parentenv, handler)
62: tryCatchOne(expr, names, parentenv, handlers[[1L]])
63: tryCatchList(expr, classes, parentenv, handlers)
64: tryCatch({    do.call(msg$data$fun, msg$data$args)}, error = function(e) {    list(.error_worker_comm(e, "worker evaluation failed"))})
65: .bpworker_EXEC(msg, bplog(backend$BPPARAM))
66: .recv_any(manager$backend)
67: .recv_any(manager$backend)
68: .manager_recv(manager)
69: .manager_recv(manager)
70: .collect_result(manager, reducer, progress, BPPARAM)
71: .bploop_impl(ITER = ITER, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM,     BPOPTIONS = BPOPTIONS, BPREDO = BPREDO, reducer = reducer,     progress.length = length(redo_index))
72: bploop.lapply(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS,     ...)
73: bploop(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, ...)
74: .bpinit(manager = manager, X = X, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM,     BPOPTIONS = BPOPTIONS, BPREDO = BPREDO)
75: bplapply(X = ddd, .wrapMapplyNotShared, .FUN = FUN, .MoreArgs = MoreArgs,     BPREDO = BPREDO, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS)
76: bplapply(X = ddd, .wrapMapplyNotShared, .FUN = FUN, .MoreArgs = MoreArgs,     BPREDO = BPREDO, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS)
77: BiocParallel::bpmapply(subset_list, index, FUN = function(splits,     i) {    if (verbose)         message("  - Comparison", i, "\n")    if (verbose)         message("    Splitting the samples...")    counts_and_metadata <- get_counts_metadata(object, assay_name = assay_name)    counts <- counts_and_metadata[[1]]    is_phyloseq <- counts_and_metadata[[3]]    if (is_phyloseq) {        po <- phyloseq::prune_samples(phyloseq::sample_names(object) %in%             splits, object)        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        pfo <- phyloseq::filter_taxa(po, function(x) sum(x >             min_counts) > min_samples, 1)    }    else {        po <- object[, splits]        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        taxa_to_keep <- apply(counts, 1, function(x) sum(x >             min_counts) > min_samples)        pfo <- po[taxa_to_keep, ]    }    if (!is.null(normalization_list)) {        if (verbose)             message("    Computing normalizations...")        pfo <- runNormalizations(normalization_list = normalization_list,             object = pfo, verbose = verbose)    }    weights_info <- unlist(lapply(X = method_list, FUN = function(x) {        x[["weights"]]    }))    if (sum(weights_info) > 0) {        if (verbose)             message("    Computing ZINB weights...")        weights <- weights_ZINB(pfo, design = ~1)    }    else weights <- NULL    if (verbose)         message("    Differential abundance:")    runDA(method_list = method_list, object = pfo, weights = weights,         verbose = verbose)}, SIMPLIFY = FALSE, BPPARAM = BPPARAM)
78: BiocParallel::bpmapply(subset_list, index, FUN = function(splits,     i) {    if (verbose)         message("  - Comparison", i, "\n")    if (verbose)         message("    Splitting the samples...")    counts_and_metadata <- get_counts_metadata(object, assay_name = assay_name)    counts <- counts_and_metadata[[1]]    is_phyloseq <- counts_and_metadata[[3]]    if (is_phyloseq) {        po <- phyloseq::prune_samples(phyloseq::sample_names(object) %in%             splits, object)        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        pfo <- phyloseq::filter_taxa(po, function(x) sum(x >             min_counts) > min_samples, 1)    }    else {        po <- object[, splits]        if (verbose)             message("    Keeping taxa with more than ", min_counts,                 " counts in more than ", min_samples, " samples.")        taxa_to_keep <- apply(counts, 1, function(x) sum(x >             min_counts) > min_samples)        pfo <- po[taxa_to_keep, ]    }    if (!is.null(normalization_list)) {        if (verbose)             message("    Computing normalizations...")        pfo <- runNormalizations(normalization_list = normalization_list,             object = pfo, verbose = verbose)    }    weights_info <- unlist(lapply(X = method_list, FUN = function(x) {        x[["weights"]]    }))    if (sum(weights_info) > 0) {        if (verbose)             message("    Computing ZINB weights...")        weights <- weights_ZINB(pfo, design = ~1)    }    else weights <- NULL    if (verbose)         message("    Differential abundance:")    runDA(method_list = method_list, object = pfo, weights = weights,         verbose = verbose)}, SIMPLIFY = FALSE, BPPARAM = BPPARAM)
79: FUN(X[[i]], ...)
80: lapply(X = subsets, FUN = function(subset_number) {    subset <- split_list[[subset_number]]    if (verbose)         message("- Subset", subset_number, "\n")    index <- seq_len(nrow(subset))    subset_list <- as.list(as.data.frame(t(subset)))    BiocParallel::bpmapply(subset_list, index, FUN = function(splits,         i) {        if (verbose)             message("  - Comparison", i, "\n")        if (verbose)             message("    Splitting the samples...")        counts_and_metadata <- get_counts_metadata(object, assay_name = assay_name)        counts <- counts_and_metadata[[1]]        is_phyloseq <- counts_and_metadata[[3]]        if (is_phyloseq) {            po <- phyloseq::prune_samples(phyloseq::sample_names(object) %in%                 splits, object)            if (verbose)                 message("    Keeping taxa with more than ", min_counts,                   " counts in more than ", min_samples, " samples.")            pfo <- phyloseq::filter_taxa(po, function(x) sum(x >                 min_counts) > min_samples, 1)        }        else {            po <- object[, splits]            if (verbose)                 message("    Keeping taxa with more than ", min_counts,                   " counts in more than ", min_samples, " samples.")            taxa_to_keep <- apply(counts, 1, function(x) sum(x >                 min_counts) > min_samples)            pfo <- po[taxa_to_keep, ]        }        if (!is.null(normalization_list)) {            if (verbose)                 message("    Computing normalizations...")            pfo <- runNormalizations(normalization_list = normalization_list,                 object = pfo, verbose = verbose)        }        weights_info <- unlist(lapply(X = method_list, FUN = function(x) {            x[["weights"]]        }))        if (sum(weights_info) > 0) {            if (verbose)                 message("    Computing ZINB weights...")            weights <- weights_ZINB(pfo, design = ~1)        }        else weights <- NULL        if (verbose)             message("    Differential abundance:")        runDA(method_list = method_list, object = pfo, weights = weights,             verbose = verbose)    }, SIMPLIFY = FALSE, BPPARAM = BPPARAM)})
81: runSplits(split_list = my_splits, method_list = my_methods_noWeights,     normalization_list = my_normalizations, object = ps_plaque_16S,     min_counts = 0, min_samples = 2, verbose = FALSE, BPPARAM = bpparam)
82: eval(expr, envir, enclos)
83: eval(expr, envir, enclos)
84: eval_with_user_handlers(expr, envir, enclos, user_handlers)
85: withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers))
86: withCallingHandlers(withVisible(eval_with_user_handlers(expr,     envir, enclos, user_handlers)), warning = wHandler, error = eHandler,     message = mHandler)
87: handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr,     envir, enclos, user_handlers)), warning = wHandler, error = eHandler,     message = mHandler))
88: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr,     envir, enclos, user_handlers)), warning = wHandler, error = eHandler,     message = mHandler)))
89: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,     debug = debug, last = i == length(out), use_try = stop_on_error !=         2L, keep_warning = keep_warning, keep_message = keep_message,     log_echo = log_echo, log_warning = log_warning, output_handler = output_handler,     include_timing = include_timing)
90: evaluate::evaluate(...)
91: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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))
92: in_dir(input_dir(), expr)
93: 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$render, options)))
94: eng_r(options)
95: block_exec(params)
96: call_block(x)
97: process_group.block(group)
98: process_group(group)
99: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),     error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e))
100: withCallingHandlers(expr, error = function(e) {    loc = paste0(current_lines(), label, sprintf(" (%s)", knit_concord$get("infile")))    message(one_string(handler(e, loc)))})
101: 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]))
102: process_file(text, output)
103: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
104: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),     output_dir = getwd(), ...)
105: vweave_rmarkdown(...)
106: engine$weave(file, quiet = quiet, encoding = enc)
107: doTryCatch(return(expr), name, parentenv, handler)
108: tryCatchOne(expr, names, parentenv, handlers[[1L]])
109: tryCatchList(expr, classes, parentenv, handlers)
110: tryCatch({    engine$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)))})
111: tools::buildVignettes(dir = ".", tangle = TRUE)
An irrecoverable exception occurred. R is aborting now ...