• using R version 4.6.0 (2026-04-24 ucrt)
• using platform: x86_64-w64-mingw32
• R was compiled by     gcc.exe (GCC) 14.3.0     GNU Fortran (GCC) 14.3.0
• running under: Windows Server 2022 x64 (build 20348)
• using session charset: UTF-8 * current time: 2026-06-22 18:14:43 UTC
• checking for file 'confMeta/DESCRIPTION' ... OK
• this is package 'confMeta' version '0.1.1'
• package encoding: UTF-8
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking whether package 'confMeta' can be installed ... OK See the install log for details.
• checking installed package size ... OK
• checking package directory ... OK
• checking 'build' directory ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... [4s] OK
• checking whether the package can be loaded with stated dependencies ... [3s] OK
• checking whether the package can be unloaded cleanly ... [3s] OK
• checking whether the namespace can be loaded with stated dependencies ... [3s] OK
• checking whether the namespace can be unloaded cleanly ... [3s] OK
• checking loading without being on the library search path ... [4s] OK
• checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [12s] OK
• checking Rd files ... [1s] OK
• checking Rd metadata ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking installed files from 'inst/doc' ... OK
• checking files in 'vignettes' ... OK
• checking examples ... [10s] OK
• checking for unstated dependencies in 'tests' ... OK
• checking tests ... [10s] ERROR   Running 'testthat.R' [10s] Running the tests in 'tests/testthat.R' failed. Complete output:   > # This file is part of the standard setup for testthat.   > # It is recommended that you do not modify it.   > #   > # Where should you do additional test configuration?   > # Learn more about the roles of various files in:   > # * https://r-pkgs.org/tests.html   > # * https://testthat.r-lib.org/reference/test_package.html#special-files   >   > library(testthat)   > library(confMeta)   >   > test_check("confMeta")   Saving _problems/test-confMeta.weights-19.R   [ FAIL 1 | WARN 0 | SKIP 0 | PASS 79 ]      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test-confMeta.weights.R:13:5'): weights argument is validated correctly ──   Error in `(function (yi, vi, sei, weights, ai, bi, ci, di, n1i, n2i, x1i, x2i, t1i, t2i, m1i, m2i, sd1i, sd2i, xi, mi, ri, ti, fi, pi, sdi, r2i, ni, mods, scale, measure = "GEN", data, slab, subset, add = 1/2, to = "only0", drop00 = FALSE, intercept = TRUE, method = "REML", weighted = TRUE, test = "z", level = 95, btt, att, tau2, verbose = FALSE, digits, control, ...) { mstyle <- .get.mstyle() if (!is.element(measure, c("RR", "OR", "PETO", "RD", "AS", "PHI", "ZPHI", "YUQ", "YUY", "RTET", "ZTET", "PBIT", "OR2D", "OR2DN", "OR2DL", "MPRD", "MPRR", "MPOR", "MPORC", "MPPETO", "MPORM", "IRR", "IRD", "IRSD", "MD", "SMD", "SMDH", "SMD1", "SMD1H", "ROM", "VR", "CVR", "RPB", "ZPB", "RBIS", "ZBIS", "D2OR", "D2ORN", "D2ORL", "COR", "UCOR", "ZCOR", "PCOR", "ZPCOR", "SPCOR", "ZSPCOR", "R2", "ZR2", "R2F", "ZR2F", "PR", "PLN", "PLO", "PRZ", "PAS", "PFT", "IR", "IRLN", "IRS", "IRFT", "MN", "SMN", "MNLN", "SDLN", "CVLN", "MC", "SMCC", "SMCR", "SMCRH", "SMCRP", "SMCRPH", "CLESCN", "AUCCN", "ROMC", "VRC", "CVRC", "ARAW", "AHW", "ABT", "REH", "CLES", "CLESN", "AUC", "AUCN", "HR", "HD", "GEN"))) stop(mstyle$stop("Unknown 'measure' specified.")) if (!is.element(method[1], c("FE", "EE", "CE", "HS", "HSk", "HE", "CO", "VC", "DL", "DLIT", "GENQ", "GENQM", "SJ", "SJIT", "PM", "MP", "PMM", "ML", "REML", "EB"))) stop(mstyle$stop("Unknown 'method' specified.")) if (length(add) > 1L) add <- add[1] if (length(to) > 1L) to <- to[1] na.act <- getOption("na.action") if (!is.element(na.act, c("na.omit", "na.exclude", "na.fail", "na.pass"))) stop(mstyle$stop("Unknown 'na.action' specified under options().")) if (missing(tau2)) { tau2 <- NULL } else { if (!is.null(tau2) && length(tau2) != 1L) stop(mstyle$stop("Argument 'tau2' must be a scalar.")) } if (missing(control)) control <- list() time.start <- proc.time() ddd <- list(...) .chkdots(ddd, c("vtype", "knha", "onlyo1", "addyi", "addvi", "correct", "i2def", "r2def", "skipr2", "abbrev", "dfs", "time", "outlist", "link", "optbeta", "alpha", "beta", "skiphes", "retopt", "randhet", "omega2", "pleasedonotreportI2thankyouverymuch")) if (is.null(ddd$vtype)) { vtype <- "LS" } else { vtype <- ddd$vtype } if (isFALSE(ddd$knha)) test <- "z" if (isTRUE(ddd$knha)) test <- "knha" test <- tolower(test) if (!is.element(test, c("z", "t", "knha", "hksj", "adhoc"))) stop(mstyle$stop("Unknown option specified for the 'test' argument.")) if (test == "hksj") test <- "knha" if (missing(scale)) { model <- "rma.uni" } else { model <- "rma.ls" } onlyo1 <- .chkddd(ddd$onlyo1, FALSE) addyi <- .chkddd(ddd$addyi, TRUE) addvi <- .chkddd(ddd$addvi, TRUE) correct <- .chkddd(ddd$correct, TRUE) i2def <- .chkddd(ddd$i2def, "1") r2def <- .chkddd(ddd$r2def, "1") link <- .chkddd(ddd$link, "log", match.arg(ddd$link, c("log", "identity"))) optbeta <- .chkddd(ddd$optbeta, FALSE, isTRUE(ddd$optbeta)) if (model == "rma.uni" && optbeta) { warning(mstyle$warning("Argument 'optbeta' only relevant for location-scale models and hence ignored."), call. = FALSE) optbeta <- FALSE } if (optbeta && !weighted) stop(mstyle$stop("Must use 'weighted=TRUE' when 'optbeta=TRUE'.")) alpha <- .chkddd(ddd$alpha, NA_real_) beta <- .chkddd(ddd$beta, NA_real_) omega2 <- .chkddd(ddd$omega2, NA_real_) if (model == "rma.uni" && !missing(att)) warning(mstyle$warning("Argument 'att' only relevant for location-scale models and hence ignored."), call. = FALSE) if (missing(digits)) { digits <- .set.digits(dmiss = TRUE) } else { digits <- .set.digits(digits, dmiss = FALSE) } formula.yi <- NULL formula.mods <- NULL formula.scale <- NULL if (verbose > 2) { opwarn <- options(warn = 1) on.exit(options(warn = opwarn$warn), add = TRUE) } randhet <- isTRUE(ddd$randhet) if (model == "rma.ls") { if (randhet && link == "identity") stop(mstyle$stop("Cannot use identity link when allowing for residual heteroscedasticity.")) if (randhet) { method <- "ML" optbeta <- TRUE } } else { if (randhet) warning(mstyle$warning("Argument 'randhet' only relevant for location-scale models and hence ignored."), call. = FALSE) } if (verbose) .space() if (verbose > 1) message(mstyle$message("Extracting/computing the yi/vi values ...")) if (missing(data)) data <- NULL if (is.null(data)) { data <- sys.frame(sys.parent()) } else { if (!is.data.frame(data)) data <- data.frame(data) } mf <- match.call() addval <- mf[[match("add", names(mf))]] if (is.element(measure, c("AS", "PHI", "ZPHI", "RTET", "ZTET", "IRSD", "PAS", "PFT", "IRS", "IRFT")) && is.null(addval)) add <- 0 yi <- .getx("yi", mf = mf, data = data) if (!is.null(yi) && inherits(yi, "escalc")) data <- yi weights <- .getx("weights", mf = mf, data = data, checknumeric = TRUE) slab <- .getx("slab", mf = mf, data = data) subset <- .getx("subset", mf = mf, data = data) mods <- .getx("mods", mf = mf, data = data) scale <- .getx("scale", mf = mf, data = data) ai <- bi <- ci <- di <- x1i <- x2i <- t1i <- t2i <- NA_real_ if (!is.null(yi)) { if (inherits(yi, "formula")) { formula.yi <- yi formula.mods <- formula.yi[-2] options(na.action = "na.pass") mods <- model.matrix(yi, data = data) attr(mods, "assign") <- NULL attr(mods, "contrasts") <- NULL yi <- model.response(model.frame(yi, data = data)) options(na.action = na.act) names(yi) <- NULL intercept <- FALSE } if (inherits(yi, "escalc")) { if (!is.null(attr(yi, "yi.names"))) { yi.name <- attr(yi, "yi.names")[1] } else { if (!is.element("yi", names(yi))) stop(mstyle$stop("Cannot determine the name of the 'yi' variable.")) yi.name <- "yi" } if (!is.null(attr(yi, "vi.names"))) { vi.name <- attr(yi, "vi.names")[1] } else { if (!is.element("vi", names(yi))) stop(mstyle$stop("Cannot determine the name of the 'vi' variable.")) vi.name <- "vi" } vi <- yi[[vi.name]] yi <- yi[[yi.name]] if (is.null(yi)) stop(mstyle$stop(paste0("Cannot find variable '", yi.name, "' in the object."))) if (is.null(vi)) stop(mstyle$stop(paste0("Cannot find variable '", vi.name, "' in the object."))) yi.escalc <- TRUE } else { yi.escalc <- FALSE } if (is.data.frame(yi)) { if (ncol(yi) == 1L) { yi <- yi[[1]] } else { stop(mstyle$stop("The object/variable specified for the 'yi' argument is a data frame with multiple columns.")) } } if (.is.matrix(yi)) { if (nrow(yi) == 1L || ncol(yi) == 1L) { yi <- as.vector(yi) } else { stop(mstyle$stop("The object/variable specified for the 'yi' argument is a matrix with multiple rows/columns.")) } } if (inherits(yi, "array")) stop(mstyle$stop("The object/variable specified for the 'yi' argument is an array.")) if (!is.numeric(yi)) stop(mstyle$stop("The object/variable specified for the 'yi' argument is not numeric.")) k <- length(yi) k.all <- k if (measure == "GEN" && !is.null(attr(yi, "measure"))) measure <- attr(yi, "measure") attr(yi, "measure") <- measure if (!yi.escalc) { vi <- .getx("vi", mf = mf, data = data, checknumeric = TRUE) sei <- .getx("sei", mf = mf, data = data, checknumeric = TRUE) } ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) if (is.null(vi)) { if (is.null(sei)) { stop(mstyle$stop("Must specify the 'vi' or 'sei' argument.")) } else { vi <- sei^2 } } .chkviarg(mf$vi) if (.is.matrix(vi)) { if (nrow(vi) == 1L || ncol(vi) == 1L) { vi <- as.vector(vi) } else { if (.is.square(vi) && isSymmetric(unname(vi))) { vi <- as.matrix(vi) if (any(vi[!diag(nrow(vi))] != 0)) warning(mstyle$warning("Using only the diagonal elements from the 'vi' argument as the sampling variances."), call. = FALSE) vi <- diag(vi) } else { stop(mstyle$stop("The object/variable specified for the 'vi' argument is a matrix with multiple rows/columns.")) } } } if (inherits(vi, "array")) stop(mstyle$stop("The object/variable specified for the 'vi' argument is an array.")) if ((length(vi) == 1L && vi == 0) || (length(vi) == k && all(vi == 0, na.rm = TRUE))) { vi0 <- TRUE } else { vi0 <- FALSE } vi <- .expand1(vi, k) if (length(vi) != k) stop(mstyle$stop("Length of 'yi' and 'vi' (or 'sei') are not the same.")) if (is.null(ni)) ni <- attr(yi, "ni") if (!is.null(ni) && length(ni) != k) ni <- NULL if (!is.null(ni)) attr(yi, "ni") <- ni if (is.null(slab)) { slab <- attr(yi, "slab") if (!is.null(slab) && length(slab) != k) slab <- NULL } if (!is.null(subset)) { subset <- .chksubset(subset, k) yi <- .getsubset(yi, subset) vi <- .getsubset(vi, subset) ni <- .getsubset(ni, subset) attr(yi, "measure") <- measure attr(yi, "ni") <- ni } } else { if (is.element(measure, c("RR", "OR", "PETO", "RD", "AS", "PHI", "ZPHI", "YUQ", "YUY", "RTET", "ZTET", "PBIT", "OR2D", "OR2DN", "OR2DL", "MPRD", "MPRR", "MPOR", "MPORC", "MPPETO", "MPORM"))) { ai <- .getx("ai", mf = mf, data = data, checknumeric = TRUE) bi <- .getx("bi", mf = mf, data = data, checknumeric = TRUE) ci <- .getx("ci", mf = mf, data = data, checknumeric = TRUE) di <- .getx("di", mf = mf, data = data, checknumeric = TRUE) n1i <- .getx("n1i", mf = mf, data = data, checknumeric = TRUE) n2i <- .getx("n2i", mf = mf, data = data, checknumeric = TRUE) ri <- .getx("ri", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) if (is.null(bi)) bi <- n1i - ai if (is.null(di)) di <- n2i - ci k <- length(ai) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) ai <- .getsubset(ai, subset) bi <- .getsubset(bi, subset) ci <- .getsubset(ci, subset) di <- .getsubset(di, subset) ri <- .getsubset(ri, subset) pi <- .getsubset(pi, subset) } args <- list(ai = ai, bi = bi, ci = ci, di = di, ri = ri, pi = pi, add = add, to = to, drop00 = drop00, onlyo1 = onlyo1, addyi = addyi, addvi = addvi) } if (is.element(measure, c("IRR", "IRD", "IRSD"))) { x1i <- .getx("x1i", mf = mf, data = data, checknumeric = TRUE) x2i <- .getx("x2i", mf = mf, data = data, checknumeric = TRUE) t1i <- .getx("t1i", mf = mf, data = data, checknumeric = TRUE) t2i <- .getx("t2i", mf = mf, data = data, checknumeric = TRUE) k <- length(x1i) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) x1i <- .getsubset(x1i, subset) x2i <- .getsubset(x2i, subset) t1i <- .getsubset(t1i, subset) t2i <- .getsubset(t2i, subset) } args <- list(x1i = x1i, x2i = x2i, t1i = t1i, t2i = t2i, add = add, to = to, drop00 = drop00, addyi = addyi, addvi = addvi) } if (is.element(measure, c("MD", "SMD", "SMDH", "SMD1", "SMD1H", "ROM", "RPB", "ZPB", "RBIS", "ZBIS", "D2OR", "D2ORN", "D2ORL", "VR", "CVR"))) { m1i <- .getx("m1i", mf = mf, data = data, checknumeric = TRUE) m2i <- .getx("m2i", mf = mf, data = data, checknumeric = TRUE) sd1i <- .getx("sd1i", mf = mf, data = data, checknumeric = TRUE) sd2i <- .getx("sd2i", mf = mf, data = data, checknumeric = TRUE) n1i <- .getx("n1i", mf = mf, data = data, checknumeric = TRUE) n2i <- .getx("n2i", mf = mf, data = data, checknumeric = TRUE) di <- .getx("di", mf = mf, data = data, checknumeric = TRUE) ti <- .getx("ti", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) ri <- .getx("ri", mf = mf, data = data, checknumeric = TRUE) if (is.element(measure, c("SMD", "RPB", "ZPB", "RBIS", "ZBIS", "D2OR", "D2ORN", "D2ORL"))) { if (!.equal.length(m1i, m2i, sd1i, sd2i, n1i, n2i, di, ti, pi, ri)) stop(mstyle$stop("Supplied data vectors are not all of the same length.")) ti <- replmiss(ti, .convp2t(pi, df = n1i + n2i - 2)) di <- replmiss(di, ti * sqrt(1/n1i + 1/n2i)) mi <- n1i + n2i - 2 hi <- mi/n1i + mi/n2i di <- replmiss(di, sqrt(hi) * ri/sqrt(1 - ri^2)) m1i[!is.na(di)] <- di[!is.na(di)] m2i[!is.na(di)] <- 0 sd1i[!is.na(di)] <- 1 sd2i[!is.na(di)] <- 1 } k <- length(n1i) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) m1i <- .getsubset(m1i, subset) m2i <- .getsubset(m2i, subset) sd1i <- .getsubset(sd1i, subset) sd2i <- .getsubset(sd2i, subset) n1i <- .getsubset(n1i, subset) n2i <- .getsubset(n2i, subset) } args <- list(m1i = m1i, m2i = m2i, sd1i = sd1i, sd2i = sd2i, n1i = n1i, n2i = n2i) } if (is.element(measure, c("COR", "UCOR", "ZCOR"))) { ri <- .getx("ri", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) ti <- .getx("ti", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) if (!.equal.length(ri, ni, ti, pi)) stop(mstyle$stop("Supplied data vectors are not all of the same length.")) ti <- replmiss(ti, .convp2t(pi, df = ni - 2)) ri <- replmiss(ri, ti/sqrt(ti^2 + ni - 2)) k <- length(ri) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) ri <- .getsubset(ri, subset) ni <- .getsubset(ni, subset) } args <- list(ri = ri, ni = ni) } if (is.element(measure, c("PCOR", "ZPCOR", "SPCOR", "ZSPCOR"))) { ri <- .getx("ri", mf = mf, data = data, checknumeric = TRUE) ti <- .getx("ti", mf = mf, data = data, checknumeric = TRUE) mi <- .getx("mi", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) r2i <- .getx("r2i", mf = mf, data = data, checknumeric = TRUE) if (!.equal.length(ri, ti, mi, ni, pi, r2i)) stop(mstyle$stop("Supplied data vectors are not all of the same length.")) ti <- replmiss(ti, .convp2t(pi, df = ni - mi - 1)) if (is.element(measure, c("PCOR", "ZPCOR"))) ri <- replmiss(ri, ti/sqrt(ti^2 + ni - mi - 1)) if (is.element(measure, c("SPCOR", "ZSPCOR"))) ri <- replmiss(ri, ti * sqrt(1 - r2i)/sqrt(ni - mi - 1)) k <- length(ri) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) ri <- .getsubset(ri, subset) mi <- .getsubset(mi, subset) ni <- .getsubset(ni, subset) r2i <- .getsubset(r2i, subset) } args <- list(ri = ri, mi = mi, ni = ni, r2i = r2i) } if (is.element(measure, c("R2", "ZR2", "R2F", "ZR2F"))) { r2i <- .getx("r2i", mf = mf, data = data, checknumeric = TRUE) mi <- .getx("mi", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) fi <- .getx("fi", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) if (!.equal.length(r2i, mi, ni)) stop(mstyle$stop("Supplied data vectors are not all of the same length.")) fi <- replmiss(fi, .convp2f(pi, df1 = mi, df2 = ni - mi - 1)) r2i <- replmiss(r2i, mi * fi/(mi * fi + (ni - mi - 1))) k <- length(r2i) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) r2i <- .getsubset(r2i, subset) mi <- .getsubset(mi, subset) ni <- .getsubset(ni, subset) } args <- list(r2i = r2i, mi = mi, ni = ni) } if (is.element(measure, c("PR", "PLN", "PLO", "PRZ", "PAS", "PFT"))) { xi <- .getx("xi", mf = mf, data = data, checknumeric = TRUE) mi <- .getx("mi", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) if (is.null(mi)) mi <- ni - xi k <- length(xi) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) xi <- .getsubset(xi, subset) mi <- .getsubset(mi, subset) } args <- list(xi = xi, mi = mi, add = add, to = to, addyi = addyi, addvi = addvi) } if (is.element(measure, c("IR", "IRLN", "IRS", "IRFT"))) { xi <- .getx("xi", mf = mf, data = data, checknumeric = TRUE) ti <- .getx("ti", mf = mf, data = data, checknumeric = TRUE) k <- length(xi) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) xi <- .getsubset(xi, subset) ti <- .getsubset(ti, subset) } args <- list(xi = xi, ti = ti, add = add, to = to, addyi = addyi, addvi = addvi) } if (is.element(measure, c("MN", "SMN", "MNLN", "SDLN", "CVLN"))) { mi <- .getx("mi", mf = mf, data = data, checknumeric = TRUE) sdi <- .getx("sdi", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) k <- length(ni) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) mi <- .getsubset(mi, subset) sdi <- .getsubset(sdi, subset) ni <- .getsubset(ni, subset) } args <- list(mi = mi, sdi = sdi, ni = ni) } if (is.element(measure, c("MC", "SMCC", "SMCR", "SMCRH", "SMCRP", "SMCRPH", "CLESCN", "AUCCN", "ROMC", "VRC", "CVRC"))) { m1i <- .getx("m1i", mf = mf, data = data, checknumeric = TRUE) m2i <- .getx("m2i", mf = mf, data = data, checknumeric = TRUE) sd1i <- .getx("sd1i", mf = mf, data = data, checknumeric = TRUE) sd2i <- .getx("sd2i", mf = mf, data = data, checknumeric = TRUE) ri <- .getx("ri", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) di <- .getx("di", mf = mf, data = data, checknumeric = TRUE) ti <- .getx("ti", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) ri <- .expand1(ri, list(m1i, m2i, sd1i, sd2i, ni, di, ti, pi)) if (measure == "SMCC") { if (!.equal.length(m1i, m2i, sd1i, sd2i, ri, ni, di, ti, pi)) stop(mstyle$stop("Supplied data vectors are not all of the same length.")) ti <- replmiss(ti, .convp2t(pi, df = ni - 1)) di <- replmiss(di, ti * sqrt(1/ni)) m1i[!is.na(di)] <- di[!is.na(di)] m2i[!is.na(di)] <- 0 sd1i[!is.na(di)] <- 1 sd2i[!is.na(di)] <- 1 ri[!is.na(di)] <- 0.5 } k <- length(m1i) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) m1i <- .getsubset(m1i, subset) m2i <- .getsubset(m2i, subset) sd1i <- .getsubset(sd1i, subset) sd2i <- .getsubset(sd2i, subset) ni <- .getsubset(ni, subset) ri <- .getsubset(ri, subset) } args <- list(m1i = m1i, m2i = m2i, sd1i = sd1i, sd2i = sd2i, ri = ri, ni = ni) } if (is.element(measure, c("ARAW", "AHW", "ABT"))) { ai <- .getx("ai", mf = mf, data = data, checknumeric = TRUE) mi <- .getx("mi", mf = mf, data = data, checknumeric = TRUE) ni <- .getx("ni", mf = mf, data = data, checknumeric = TRUE) k <- length(ai) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) ai <- .getsubset(ai, subset) mi <- .getsubset(mi, subset) ni <- .getsubset(ni, subset) } args <- list(ai = ai, mi = mi, ni = ni) } if (measure == "REH") { ai <- .getx("ai", mf = mf, data = data, checknumeric = TRUE) bi <- .getx("bi", mf = mf, data = data, checknumeric = TRUE) ci <- .getx("ci", mf = mf, data = data, checknumeric = TRUE) k <- length(ai) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) ai <- .getsubset(ai, subset) bi <- .getsubset(bi, subset) ci <- .getsubset(ci, subset) } args <- list(ai = ai, bi = bi, ci = ci) } if (is.element(measure, c("CLES", "AUC"))) { ai <- .getx("ai", mf = mf, data = data, checknumeric = TRUE) n1i <- .getx("n1i", mf = mf, data = data, checknumeric = TRUE) n2i <- .getx("n2i", mf = mf, data = data, checknumeric = TRUE) mi <- .getx("mi", mf = mf, data = data, checknumeric = TRUE) if (is.null(mi)) mi <- rep(0, length(ai)) mi[is.na(mi)] <- 0 k <- length(ai) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) ai <- .getsubset(ai, subset) n1i <- .getsubset(n1i, subset) n2i <- .getsubset(n2i, subset) mi <- .getsubset(mi, subset) } args <- list(ai = ai, n1i = n1i, n2i = n2i, mi = mi) } if (is.element(measure, c("CLESN", "AUCN"))) { m1i <- .getx("m1i", mf = mf, data = data, checknumeric = TRUE) m2i <- .getx("m2i", mf = mf, data = data, checknumeric = TRUE) sd1i <- .getx("sd1i", mf = mf, data = data, checknumeric = TRUE) sd2i <- .getx("sd2i", mf = mf, data = data, checknumeric = TRUE) n1i <- .getx("n1i", mf = mf, data = data, checknumeric = TRUE) n2i <- .getx("n2i", mf = mf, data = data, checknumeric = TRUE) di <- .getx("di", mf = mf, data = data, checknumeric = TRUE) ti <- .getx("ti", mf = mf, data = data, checknumeric = TRUE) pi <- .getx("pi", mf = mf, data = data, checknumeric = TRUE) ai <- .getx("ai", mf = mf, data = data, checknumeric = TRUE) if (!.equal.length(m1i, m2i, sd1i, sd2i, n1i, n2i, di, ti, pi, ai)) stop(mstyle$stop("Supplied data vectors are not all of the same length.")) if (!.all.specified(n1i, n2i)) stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n via the appropriate arguments.")) k.all <- .maxlength(m1i, m2i, sd1i, sd2i, n1i, n2i, di, ti, pi, ai) vtype <- .expand1(vtype, k.all) if (is.null(sd1i) || is.null(sd2i)) { sd1i <- .expand1(NA_real_, k.all) sd2i <- .expand1(NA_real_, k.all) } ti <- replmiss(ti, .convp2t(pi, df = n1i + n2i - 2)) di <- replmiss(di, ti * sqrt(1/n1i + 1/n2i)) if (!is.null(di)) vtype[!is.na(di)] <- "HO" sdpi <- ifelse(vtype == "HO", sqrt(((n1i - 1) * sd1i^2 + (n2i - 1) * sd2i^2)/(n1i + n2i - 2)), sqrt((sd1i^2 + sd2i^2)/2)) di <- replmiss(di, (m1i - m2i)/sdpi) ai <- replmiss(ai, pnorm(di/sqrt(2))) di <- replmiss(di, qnorm(ai) * sqrt(2)) k.all <- length(ai) sdsmiss <- is.na(sd1i) | is.na(sd2i) sd1i <- ifelse(sdsmiss, 1, sd1i) sd2i <- ifelse(sdsmiss, 1, sd2i) vtype[sdsmiss] <- "HO" k <- length(ai) k.all <- k if (!is.null(subset)) { subset <- .chksubset(subset, k) vtype <- .getsubset(vtype, subset) ai <- .getsubset(ai, subset) sd1i <- .getsubset(sd1i, subset) sd2i <- .getsubset(sd2i, subset) n1i <- .getsubset(n1i, subset) n2i <- .getsubset(n2i, subset) } args <- list(ai = ai, sd1i = sd1i, sd2i = sd2i, n1i = n1i, n2i = n2i) } args <- c(args, list(measure = measure, vtype = vtype, correct = correct)) dat <- .do.call(escalc, args) if (is.element(measure, "GEN")) stop(mstyle$stop("Specify the desired outcome measure via the 'measure' argument.")) yi <- dat$yi vi <- dat$vi ni <- attr(yi, "ni") } if (!is.null(weights)) { if (verbose > 1) message(mstyle$message("Extracting custom weights ...")) if (optbeta) stop(mstyle$stop("Cannot use custom weights when 'optbeta=TRUE'.")) weights <- .expand1(weights, k) if (length(weights) != k) stop(mstyle$stop("Length of 'yi' and 'weights' are not the same.")) if (!is.null(subset)) weights <- .getsubset(weights, subset) if (any(weights < 0, na.rm = TRUE)) stop(mstyle$stop("Negative weights are not allowed.")) if (any(is.infinite(weights))) stop(mstyle$stop("Infinite weights are not allowed.")) } if (verbose > 1) message(mstyle$message("Creating the model matrix ...")) if (inherits(mods, "formula")) { formula.mods <- mods if (.is.tilde1(formula.mods)) { mods <- matrix(1, nrow = k, ncol = 1) intercept <- FALSE } else { options(na.action = "na.pass") mods <- model.matrix(mods, data = data) attr(mods, "assign") <- NULL attr(mods, "contrasts") <- NULL options(na.action = na.act) intercept <- FALSE } } if (.is.vector(mods)) mods <- cbind(mods) if (is.data.frame(mods)) mods <- as.matrix(mods) if (is.character(mods)) stop(mstyle$stop("The model matrix contains character variables.")) if (!is.null(mods) && nrow(mods) != k) stop(mstyle$stop(paste0("Number of rows in the model matrix (", nrow(mods), ") does not match the length of the outcome vector (", k, ")."))) if (model == "rma.ls") { if (inherits(scale, "formula")) { formula.scale <- scale if (.is.tilde1(formula.scale)) { Z <- matrix(1, nrow = k, ncol = 1) colnames(Z) <- "intrcpt" } else { options(na.action = "na.pass") Z <- model.matrix(scale, data = data) colnames(Z)[grep("(Intercept)", colnames(Z), fixed = TRUE)] <- "intrcpt" attr(Z, "assign") <- NULL attr(Z, "contrasts") <- NULL options(na.action = na.act) } } else { Z <- scale if (.is.vector(Z)) Z <- cbind(Z) if (is.data.frame(Z)) Z <- as.matrix(Z) if (is.character(Z)) stop(mstyle$stop("Scale model matrix contains character variables.")) } if (nrow(Z) != k) stop(mstyle$stop(paste0("Number of rows in the model matrix specified via the 'scale' argument (", nrow(Z), ") does not match the length of the outcome vector (", k, ")."))) } else { Z <- NULL } if (!is.null(subset)) { if (verbose > 1) message(mstyle$message("Subsetting ...")) mods <- .getsubset(mods, subset) Z <- .getsubset(Z, subset) } if (verbose > 1) message(mstyle$message("Generating/extracting the study labels ...")) ids <- seq_len(k) if (is.null(slab)) { slab.null <- TRUE slab <- ids } else { if (anyNA(slab)) stop(mstyle$stop("NAs in study labels.")) if (length(slab) != k) stop(mstyle$stop(paste0("Length of the 'slab' argument (", length(slab), ") does not correspond to the size of the dataset (", k, ")."))) slab.null <- FALSE } if (!is.null(subset)) { if (verbose > 1) message(mstyle$message("Subsetting ...")) slab <- .getsubset(slab, subset) ids <- .getsubset(ids, subset) } if (anyDuplicated(slab)) slab <- .make.unique(slab) attr(yi, "slab") <- slab k <- length(yi) outdat.f <- list(ai = ai, bi = bi, ci = ci, di = di, x1i = x1i, x2i = x2i, t1i = t1i, t2i = t2i) yi.f <- yi vi.f <- vi weights.f <- weights ni.f <- ni mods.f <- mods Z.f <- Z k.f <- k has.na <- is.na(yi) | is.na(vi) | (if (is.null(mods)) FALSE else apply(is.na(mods), 1, any)) | (if (is.null(Z)) FALSE else apply(is.na(Z), 1, any)) | (if (is.null(weights)) FALSE else is.na(weights)) not.na <- !has.na if (any(has.na)) { if (verbose > 1) message(mstyle$message("Handling NAs ...")) if (na.act == "na.omit" || na.act == "na.exclude" || na.act == "na.pass") { yi <- yi[not.na] vi <- vi[not.na] weights <- weights[not.na] ni <- ni[not.na] mods <- mods[not.na, , drop = FALSE] Z <- Z[not.na, , drop = FALSE] k <- length(yi) warning(mstyle$warning(paste(sum(has.na), ifelse(sum(has.na) > 1, "studies", "study"), "with NAs omitted from model fitting.")), call. = FALSE) attr(yi, "measure") <- measure attr(yi, "ni") <- ni } if (na.act == "na.fail") stop(mstyle$stop("Missing values in data.")) } if (k < 1L) stop(mstyle$stop("Processing terminated since k = 0.")) if (any(vi <= 0)) { allvipos <- FALSE if (!vi0) warning(mstyle$warning("There are outcomes with non-positive sampling variances."), call. = FALSE) vi.neg <- vi < 0 if (any(vi.neg)) { vi[vi.neg] <- 0 warning(mstyle$warning("Negative sampling variances constrained to 0."), call. = FALSE) } } else { allvipos <- TRUE } if (k == 1L && test != "z") { warning(mstyle$warning("Setting argument test=\"z\" since k=1."), call. = FALSE) test <- "z" } if (is.null(mods) && !intercept) { warning(mstyle$warning("Must either include an intercept and/or moderators in the model.\nCoerced an intercept into the model."), call. = FALSE) intercept <- TRUE } if (!is.null(mods) && ncol(mods) == 0L) { warning(mstyle$warning("Cannot fit model with an empty model matrix. Coerced an intercept into the model."), call. = FALSE) intercept <- TRUE } if (intercept) { X <- cbind(intrcpt = rep(1, k), mods) X.f <- cbind(intrcpt = rep(1, k.f), mods.f) } else { X <- mods X.f <- mods.f } tmp <- try(lm(yi ~ 0 + X), silent = TRUE) if (inherits(tmp, "lm")) { coef.na <- is.na(coef(tmp)) } else { coef.na <- rep(FALSE, NCOL(X)) } if (any(coef.na)) { warning(mstyle$warning("Redundant predictors dropped from the model."), call. = FALSE) X <- X[, !coef.na, drop = FALSE] X.f <- X.f[, !coef.na, drop = FALSE] } is.int <- apply(X, 2, .is.intercept) if (any(is.int)) { int.incl <- TRUE int.indx <- which(is.int, arr.ind = TRUE) X <- cbind(intrcpt = 1, X[, -int.indx, drop = FALSE]) X.f <- cbind(intrcpt = 1, X.f[, -int.indx, drop = FALSE]) intercept <- TRUE } else { int.incl <- FALSE } p <- NCOL(X) colnames(X) <- colnames(X.f) <- .make.unique(colnames(X)) colnames(Z) <- colnames(Z.f) <- .make.unique(colnames(Z)) if ((p == 1L) && .is.intercept(X)) { int.only <- TRUE } else { int.only <- FALSE } if (!(int.only && k == 1L)) { if (is.element(method[1], c("FE", "EE", "CE"))) { if (p > k) stop(mstyle$stop("Number of parameters to be estimated is larger than the number of observations.")) } else { if (!is.null(tau2) && !is.na(tau2)) { if (p > k) stop(mstyle$stop("Number of parameters to be estimated is larger than the number of observations.")) } else { if ((p + 1) > k) stop(mstyle$stop("Number of parameters to be estimated is larger than the number of observations.")) } } } btt <- .set.btt(btt, p, int.incl, colnames(X)) m <- length(btt) con <- list(verbose = FALSE, evtol = 1e-07, REMLf = TRUE) if (model == "rma.uni") { con <- c(con, list(tau2.init = NULL, tau2.min = 0, tau2.max = 100, threshold = 10^-5, tol = .Machine$double.eps^0.25, ll0check = TRUE, maxiter = 100, stepadj = 1)) con$tau2.max <- max(con$tau2.max, 10 * mad(yi)^2) } if (model == "rma.ls") { con <- c(con, list(beta.init = NULL, hesspack = "numDeriv", optimizer = "nlminb", optmethod = "BFGS", parallel = list(), cl = NULL, ncpus = 1L, tau2.min = 0, tau2.max = Inf, alpha.init = NULL, alpha.min = -Inf, alpha.max = Inf, omega2.init = NULL, hessianCtrl = NULL, htransf = FALSE, hes.beta.fix = FALSE, hes.alpha.fix = FALSE, hes.omega2.fix = FALSE, omega2tol = 1e-05, scaleZ = TRUE, mfmaxit = 10^5)) } con.pos <- pmatch(names(control), names(con)) con[c(na.omit(con.pos))] <- control[!is.na(con.pos)] if (verbose) con$verbose <- verbose verbose <- con$verbose if (model == "rma.ls") { con$hesspack <- match.arg(con$hesspack, c("numDeriv", "pracma", "calculus")) if (!isTRUE(ddd$skiphes) && !requireNamespace(con$hesspack, quietly = TRUE)) stop(mstyle$stop(paste0("Please install the '", con$hesspack, "' package to compute the Hessian."))) if (con$hesspack == "numDeriv") { if (is.null(con$hessianCtrl$r)) con$hessianCtrl$r <- 8 } if (con$hesspack == "pracma") { if (is.null(con$hessianCtrl$h)) con$hessianCtrl$h <- .Machine$double.eps^(1/4) } if (con$hesspack == "calculus") { if (is.null(con$hessianCtrl$accuracy)) con$hessianCtrl$accuracy <- 4 } } if (model == "rma.uni") { if (con$tau2.min < 0 && (con$tau2.min < -min(vi))) { con$tau2.min <- -min(vi) warning(mstyle$warning(paste0("Value of 'tau2.min' constrained to -min(vi) = ", fmtx(-min(vi), digits[["est"]]), ".")), call. = FALSE) } } else { if (is.element("tau2.min", names(control))) con$tau2.min[con$tau2.min < 0] <- 0 } if (!.chkpd(crossprod(X), tol = con$evtol)) stop(mstyle$stop("Model matrix not of full rank. Cannot fit model.")) vimaxmin <- max(vi)/min(vi) if (is.finite(vimaxmin) && vimaxmin >= 1/con$evtol) warning(mstyle$warning("Ratio of largest to smallest sampling variance extremely large. May not be able to obtain stable results."), call. = FALSE) se.tau2 <- I2 <- H2 <- QE <- QEp <- NA_real_ s2w <- 1 level <- .level(level) Y <- as.matrix(yi) ymci <- scale(yi, center = TRUE, scale = FALSE) Ymc <- as.matrix(ymci) tau2.inf <- FALSE if (model == "rma.uni") { if (!is.null(tau2) && !is.na(tau2) && !is.element(method[1], c("FE", "EE", "CE"))) { tau2.fix <- TRUE tau2.arg <- tau2 tau2.inf <- identical(tau2, Inf) } else { tau2.fix <- FALSE tau2.arg <- NA_real_ } if (verbose > 1 && !tau2.fix && !is.element(method[1], c("FE", "EE", "CE"))) message(mstyle$message("Estimating the tau^2 value ...\n")) if (k == 1L) { method.sav <- method[1] method <- "k1" if (!tau2.fix) tau2 <- 0 } conv <- FALSE while (!conv && !tau2.inf) { conv <- TRUE change <- con$threshold + 1 iter <- 0 if (is.element(method[1], c("HS", "HSk"))) { if (!allvipos) stop(mstyle$stop(paste0(method[1], " estimator cannot be used when there are non-positive sampling variances in the data."))) wi <- 1/vi W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) RSS <- crossprod(Ymc, P) %*% Ymc if (method[1] == "HS") { tau2 <- ifelse(tau2.fix, tau2.arg, (RSS - k)/sum(wi)) } else { tau2 <- ifelse(tau2.fix, tau2.arg, (k/(k - p) * RSS - k)/sum(wi)) } } if (is.element(method[1], c("HE", "CO", "VC", "ML", "REML", "EB"))) { stXX <- .invcalc(X = X, W = diag(k), k = k) P <- diag(k) - X %*% tcrossprod(stXX, X) RSS <- crossprod(Ymc, P) %*% Ymc V <- .diag(vi) PV <- P %*% V trPV <- .tr(PV) tau2 <- ifelse(tau2.fix, tau2.arg, (RSS - trPV)/(k - p)) } if (method[1] == "DL") { if (!allvipos) stop(mstyle$stop("DL estimator cannot be used when there are non-positive sampling variances in the data.")) wi <- 1/vi W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) RSS <- crossprod(Ymc, P) %*% Ymc trP <- .tr(P) tau2 <- ifelse(tau2.fix, tau2.arg, (RSS - (k - p))/trP) } if (method[1] == "DLIT") { if (is.null(con$tau2.init)) { tau2 <- 0 } else { tau2 <- con$tau2.init } while (change > con$threshold) { if (verbose) cat(mstyle$verbose(paste("Iteration", formatC(iter, width = 5, flag = "-", format = "f", digits = 0), "tau^2 =", fmtx(tau2, digits[["var"]]), "\n"))) iter <- iter + 1 old2 <- tau2 wi <- 1/(vi + tau2) if (any(tau2 + vi < 0)) stop(mstyle$stop("Some marginal variances are negative.")) if (any(is.infinite(wi))) stop(mstyle$stop("Division by zero when computing the inverse variance weights.")) W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) V <- .diag(vi) trP <- .tr(P) trPV <- .tr(P %*% V) RSS <- crossprod(Ymc, P) %*% Ymc tau2 <- ifelse(tau2.fix, tau2.arg, (RSS - trPV)/trP) tau2[tau2 < con$tau2.min] <- con$tau2.min change <- abs(old2 - tau2) if (iter > con$maxiter) { conv <- FALSE break } } if (!conv) { if (length(method) == 1L) { stop(mstyle$stop("Iterative DL estimator did not converge.")) } else { if (verbose) warning(mstyle$warning("Iterative DL estimator did not converge."), call. = FALSE) } } } if (method[1] == "GENQ") { if (is.null(weights)) stop(mstyle$stop("Must specify the 'weights' argument when method='GENQ'.")) A <- .diag(weights) stXAX <- .invcalc(X = X, W = A, k = k) P <- A - A %*% X %*% stXAX %*% crossprod(X, A) V <- .diag(vi) PV <- P %*% V trP <- .tr(P) trPV <- .tr(PV) RSS <- crossprod(Ymc, P) %*% Ymc tau2 <- ifelse(tau2.fix, tau2.arg, (RSS - trPV)/trP) } if (method[1] == "GENQM") { if (is.null(weights)) stop(mstyle$stop("Must specify the 'weights' argument when method='GENQM'.")) A <- .diag(weights) stXAX <- .invcalc(X = X, W = A, k = k) P <- A - A %*% X %*% stXAX %*% crossprod(X, A) V <- .diag(vi) PV <- P %*% V trP <- .tr(P) if (!tau2.fix) { RSS <- crossprod(Ymc, P) %*% Ymc if (.GENQ.func(con$tau2.min, P = P, vi = vi, Q = RSS, level = 0, k = k, p = p, getlower = TRUE) > 0.5) { tau2 <- con$tau2.min } else { if (.GENQ.func(con$tau2.max, P = P, vi = vi, Q = RSS, level = 0, k = k, p = p, getlower = TRUE) < 0.5) { conv <- FALSE if (length(method) == 1L) { stop(mstyle$stop("Value of 'tau2.max' too low. Try increasing 'tau2.max' or switch to another 'method'.")) } else { if (verbose) warning(mstyle$warning("Value of 'tau2.max' too low. Try increasing 'tau2.max' or switch to another 'method'."), call. = FALSE) } } else { tau2 <- try(uniroot(.GENQ.func, interval = c(con$tau2.min, con$tau2.max), tol = con$tol, maxiter = con$maxiter, P = P, vi = vi, Q = RSS, level = 0.5, k = k, p = p, getlower = FALSE, verbose = verbose, digits = digits, extendInt = "no")$root, silent = TRUE) if (inherits(tau2, "try-error")) { conv <- FALSE if (length(method) == 1L) { stop(mstyle$stop("Error in iterative search for tau^2 using uniroot().")) } else { if (verbose) warning(mstyle$warning("Error in iterative search for tau^2 using uniroot()."), call. = FALSE) } } } } } else { tau2 <- tau2.arg } } if (method[1] == "SJ") { if (is.null(con$tau2.init)) { tau2.0 <- c(var(ymci) * (k - 1)/k) } else { tau2.0 <- con$tau2.init } wi <- 1/(vi + tau2.0) W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) RSS <- crossprod(Ymc, P) %*% Ymc V <- .diag(vi) PV <- P %*% V tau2 <- ifelse(tau2.fix, tau2.arg, tau2.0 * RSS/(k - p)) } if (method[1] == "SJIT") { if (is.null(con$tau2.init)) { tau2 <- c(var(ymci) * (k - 1)/k) } else { tau2 <- con$tau2.init } tau2.0 <- tau2 while (change > con$threshold) { if (verbose) cat(mstyle$verbose(paste("Iteration", formatC(iter, width = 5, flag = "-", format = "f", digits = 0), "tau^2 =", fmtx(tau2, digits[["var"]]), "\n"))) iter <- iter + 1 old2 <- tau2 wi <- 1/(vi + tau2) W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) RSS <- crossprod(Ymc, P) %*% Ymc V <- .diag(vi) PV <- P %*% V tau2 <- ifelse(tau2.fix, tau2.arg, tau2 * RSS/(k - p)) change <- abs(old2 - tau2) if (iter > con$maxiter) { conv <- FALSE break } } if (!conv) { if (length(method) == 1L) { stop(mstyle$stop("Iterative SJ estimator did not converge.")) } else { if (verbose) warning(mstyle$warning("Iterative SJ estimator did not converge."), call. = FALSE) } } } if (is.element(method[1], c("PM", "MP", "PMM"))) { if (!allvipos) stop(mstyle$stop(method[1], " estimator cannot be used when there are non-positive sampling variances in the data.")) if (method[1] == "PMM") { target <- qchisq(0.5, df = k - p) } else { target <- k - p } if (!tau2.fix) { if (.QE.func(con$tau2.min, Y = Ymc, vi = vi, X = X, k = k, objective = 0) < target) { tau2 <- con$tau2.min } else { if (.QE.func(con$tau2.max, Y = Ymc, vi = vi, X = X, k = k, objective = 0) > target) { conv <- FALSE if (length(method) == 1L) { stop(mstyle$stop("Value of 'tau2.max' too low. Try increasing 'tau2.max' or switch to another 'method'.")) } else { if (verbose) warning(mstyle$warning("Value of 'tau2.max' too low. Try increasing 'tau2.max' or switch to another 'method'."), call. = FALSE) } } else { tau2 <- try(uniroot(.QE.func, interval = c(con$tau2.min, con$tau2.max), tol = con$tol, maxiter = con$maxiter, Y = Ymc, vi = vi, X = X, k = k, objective = target, verbose = verbose, digits = digits, extendInt = "no")$root, silent = TRUE) if (inherits(tau2, "try-error")) { conv <- FALSE if (length(method) == 1L) { stop(mstyle$stop("Error in iterative search for tau^2 using uniroot().")) } else { if (verbose) warning(mstyle$warning("Error in iterative search for tau^2 using uniroot()."), call. = FALSE) } } } } } else { tau2 <- tau2.arg } } if (is.element(method[1], c("ML", "REML", "EB"))) { if (is.null(con$tau2.init)) { tau2 <- max(0, tau2, con$tau2.min) } else { tau2 <- con$tau2.init } while (change > con$threshold) { if (verbose) cat(mstyle$verbose(paste(mstyle$verbose(paste("Iteration", formatC(iter, width = 5, flag = "-", format = "f", digits = 0), "tau^2 =", fmtx(tau2, digits[["var"]]), "\n"))))) iter <- iter + 1 old2 <- tau2 wi <- 1/(vi + tau2) if (any(tau2 + vi < 0)) stop(mstyle$stop("Some marginal variances are negative.")) if (any(is.infinite(wi))) stop(mstyle$stop("Division by zero when computing the inverse variance weights.")) W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) if (method[1] == "ML") { PP <- P %*% P adj <- c(crossprod(Ymc, PP) %*% Ymc - sum(wi))/sum(wi^2) } if (method[1] == "REML") { PP <- P %*% P adj <- c(crossprod(Ymc, PP) %*% Ymc - .tr(P))/.tr(PP) } if (method[1] == "EB") { adj <- c(crossprod(Ymc, P) %*% Ymc * k/(k - p) - k)/sum(wi) } adj <- c(adj) * con$stepadj if (is.na(adj)) adj <- 0 while (tau2 + adj < con$tau2.min) adj <- adj/2 tau2 <- ifelse(tau2.fix, tau2.arg, tau2 + adj) change <- abs(old2 - tau2) if (iter > con$maxiter) { conv <- FALSE break } } if (!conv) { if (length(method) == 1L) { stop(mstyle$stop("Fisher scoring algorithm did not converge. See 'help(rma)' for possible remedies.")) } else { if (verbose) warning(mstyle$warning("Fisher scoring algorithm did not converge. See 'help(rma)' for possible remedies."), call. = FALSE) } } if (conv && is.element(method[1], c("ML", "REML")) && con$ll0check && allvipos && !tau2.fix) { wi <- 1/vi W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) beta <- stXWX %*% crossprod(X, W) %*% Ymc RSS <- sum(wi * (ymci - X %*% beta)^2) if (method[1] == "ML") ll0 <- -1/2 * (k) * log(2 * base::pi) - 1/2 * sum(log(vi)) - 1/2 * RSS if (method[1] == "REML") ll0 <- -1/2 * (k - p) * log(2 * base::pi) - 1/2 * sum(log(vi)) - 1/2 * determinant(crossprod(X, W) %*% X, logarithm = TRUE)$modulus - 1/2 * RSS wi <- 1/(vi + tau2) if (any(tau2 + vi < 0)) stop(mstyle$stop("Some marginal variances are negative.")) if (any(is.infinite(wi))) stop(mstyle$stop("Division by zero when computing the inverse variance weights.")) W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) beta <- stXWX %*% crossprod(X, W) %*% Ymc RSS <- sum(wi * (ymci - X %*% beta)^2) if (method[1] == "ML") ll <- -1/2 * (k) * log(2 * base::pi) - 1/2 * sum(log(vi + tau2)) - 1/2 * RSS if (method[1] == "REML") ll <- -1/2 * (k - p) * log(2 * base::pi) - 1/2 * sum(log(vi + tau2)) - 1/2 * determinant(crossprod(X, W) %*% X, logarithm = TRUE)$modulus - 1/2 * RSS if (ll0 - ll > con$tol && tau2 > con$threshold) { warning(mstyle$warning("Fisher scoring algorithm may have gotten stuck at a local maximum.\nSetting tau^2 = 0. Check the profile likelihood plot with profile()."), call. = FALSE) tau2 <- 0 } } if (conv) { wi <- 1/(vi + tau2) if (any(tau2 + vi < 0)) stop(mstyle$stop("Some marginal variances are negative.")) if (any(is.infinite(wi))) stop(mstyle$stop("Division by zero when computing the inverse variance weights.")) W <- .diag(wi) stXWX <- .invcalc(X = X, W = W, k = k) P <- W - W %*% X %*% stXWX %*% crossprod(X, W) } } if (conv) { tau2 <- max(con$tau2.min, c(tau2)) if (!is.na(tau2) && any(tau2 + vi < 0)) stop(mstyle$stop("Some marginal variances are negative.")) if (verbose && is.element(method[1], c("ML", "REML", "EB"))) { cat(mstyle$verbose(paste("Iteration", formatC(iter, width = 5, flag = "-", format = "f", digits = 0), "tau^2 =", fmtx(tau2, digits[["var"]]), "\n"))) cat(mstyle$verbose(paste("Fisher scoring algorithm converged after", iter, "iterations.\n"))) } if (method[1] == "HS") se.tau2 <- sqrt(1/sum(wi)^2 * (2 * (k - p) + 4 * max(tau2, 0) * .tr(P) + 2 * max(tau2, 0)^2 * sum(P * P))) if (method[1] == "HSk") se.tau2 <- k/(k - p) * sqrt(1/sum(wi)^2 * (2 * (k - p) + 4 * max(tau2, 0) * .tr(P) + 2 * max(tau2, 0)^2 * sum(P * P))) if (is.element(method[1], c("HE", "CO", "VC"))) se.tau2 <- sqrt(1/(k - p)^2 * (2 * sum(PV * t(PV)) + 4 * max(tau2, 0) * trPV + 2 * max(tau2, 0)^2 * (k - p))) if (method[1] == "DL") se.tau2 <- sqrt(1/trP^2 * (2 * (k - p) + 4 * max(tau2, 0) * trP + 2 * max(tau2, 0)^2 * sum(P * P))) if (is.element(method[1], c("GENQ", "GENQM"))) se.tau2 <- sqrt(1/trP^2 * (2 * sum(PV * t(PV)) + 4 * max(tau2, 0) * sum(PV * P) + 2 * max(tau2, 0)^2 * sum(P * P))) if (method[1] == "SJ") se.tau2 <- sqrt(tau2.0^2/(k - p)^2 * (2 * sum(PV * t(PV)) + 4 * max(tau2, 0) * sum(PV * P) + 2 * max(tau2, 0)^2 * sum(P * P))) if (method[1] == "ML") se.tau2 <- sqrt(2/sum(wi^2)) if (method[1] == "REML") se.tau2 <- sqrt(2/sum(P * P)) if (is.element(method[1], c("EB", "PM", "MP", "PMM", "DLIT", "SJIT"))) { wi <- 1/(vi + tau2) se.tau2 <- sqrt(2 * k^2/(k - p)/sum(wi)^2) } } else { method <- method[-1] } } if (k == 1L) method <- method.sav } if (model == "rma.ls") { if (!is.element(method[1], c("ML", "REML"))) stop(mstyle$stop("Location-scale models can only be fitted with ML or REML estimation.")) tau2.fix <- FALSE if (!is.null(tau2) && !is.na(tau2)) warning(mstyle$warning("Argument 'tau2' ignored for location-scale models."), call. = FALSE) optimizer <- match.arg(con$optimizer, c("optim", "nlminb", "uobyqa", "newuoa", "bobyqa", "nloptr", "nlm", "hjk", "nmk", "mads", "ucminf", "lbfgsb3c", "subplex", "BBoptim", "optimParallel", "constrOptim", "solnp", "alabama", "constrOptim.nl", "auglag", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent", "Rcgmin", "Rvmmin")) optmethod <- match.arg(con$optmethod, c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent")) if (optimizer %in% c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent")) { optmethod <- optimizer optimizer <- "optim" } parallel <- con$parallel cl <- con$cl ncpus <- con$ncpus optcontrol <- control[is.na(con.pos)] if (length(optcontrol) == 0L) optcontrol <- list() if (ncpus > 1L) optimizer <- "optimParallel" if (optimizer == "alabama") optimizer <- "constrOptim.nl" if (link == "identity") { if (optimizer == "nlminb") { optimizer <- "constrOptim" } else { if (!is.element(optimizer, c("constrOptim", "solnp", "nloptr", "constrOptim.nl", "auglag"))) { optimizer <- "constrOptim" warning(mstyle$warning(paste0("Can only use optimizers 'constrOptim', 'solnp', 'nloptr', 'constrOptim.nl', or 'auglag' when link='identity' (resetting to '", optimizer, "').")), call. = FALSE) } } } if (link == "log" && is.element(optimizer, c("constrOptim", "constrOptim.nl", "auglag"))) stop(mstyle$stop(paste0("Cannot use '", optimizer, "' optimizer when using a log link."))) reml <- ifelse(method[1] == "REML", TRUE, FALSE) tmp <- try(lm(yi ~ 0 + Z), silent = TRUE) if (inherits(tmp, "lm")) { coef.na.Z <- is.na(coef(tmp)) } else { coef.na.Z <- rep(FALSE, NCOL(Z)) } if (any(coef.na.Z)) { warning(mstyle$warning("Redundant predictors dropped from the scale model."), call. = FALSE) Z <- Z[, !coef.na.Z, drop = FALSE] Z.f <- Z.f[, !coef.na.Z, drop = FALSE] } is.int <- apply(Z, 2, .is.intercept) if (any(is.int)) { Z.int.incl <- TRUE int.indx <- which(is.int, arr.ind = TRUE) Z <- cbind(intrcpt = 1, Z[, -int.indx, drop = FALSE]) Z.f <- cbind(intrcpt = 1, Z.f[, -int.indx, drop = FALSE]) Z.intercept <- TRUE } else { Z.int.incl <- FALSE } q <- NCOL(Z) if (!.chkpd(crossprod(Z), tol = con$evtol)) stop(mstyle$stop("Model matrix for scale part of the model not of full rank. Cannot fit model.")) is.int <- apply(Z, 2, .is.intercept) if (q == 1L && is.int) { Z.int.only <- TRUE } else { Z.int.only <- FALSE } if (missing(alpha) || is.null(alpha) || all(is.na(alpha))) { alpha <- rep(NA_real_, q) } else { if (any(is.infinite(alpha))) stop(mstyle$stop("Infinite values in 'alpha' argument not allowed.")) alpha <- .expand1(alpha, q) if (length(alpha) != q) stop(mstyle$stop(paste0("Length of the 'alpha' argument (", length(alpha), ") does not match the actual number of parameters (", q, ")."))) } if (optbeta) { if (missing(beta) || is.null(beta) || all(is.na(beta))) { beta <- rep(NA_real_, p) } else { beta <- .expand1(beta, p) if (length(beta) != p) stop(mstyle$stop(paste0("Length of the 'beta' argument (", length(beta), ") does not match the actual number of parameters (", p, ")."))) } X0 <- X X0[] <- 0 } else { X0 <- NULL } if (missing(omega2) || is.null(omega2) || all(is.na(omega2))) { omega2 <- NA_real_ } else { if (length(omega2) != 1) stop(mstyle$stop("Length of the 'omega2' argument must be 1.")) } if (!Z.int.only && Z.int.incl && con$scaleZ && is.na(alpha[1]) && all(is.infinite(con$alpha.min)) && all(is.infinite(con$alpha.max)) && !optbeta) { Zsave <- Z meanZ <- colMeans(Z[, 2:q, drop = FALSE]) sdZ <- apply(Z[, 2:q, drop = FALSE], 2, sd) is.d <- apply(Z, 2, .is.dummy) mZ <- rbind(c(intrcpt = 1, -1 * ifelse(is.d[-1], 0, meanZ/sdZ)), cbind(0, diag(ifelse(is.d[-1], 1, 1/sdZ), nrow = length(is.d) - 1, ncol = length(is.d) - 1))) imZ <- try(suppressWarnings(solve(mZ)), silent = TRUE) Z[, !is.d] <- apply(Z[, !is.d, drop = FALSE], 2, scale) if (any(!is.na(alpha))) { if (inherits(imZ, "try-error")) stop(mstyle$stop("Unable to rescale starting values for the scale parameters.")) alpha <- diag(imZ) * alpha } } else { mZ <- NULL } if (k == 1L && Z.int.only) { if (link == "log") con$alpha.init <- -10000 if (link == "identity") con$alpha.init <- 1e-05 } if (verbose > 1) message(mstyle$message("Extracting/computing the initial values ...")) if (is.null(con$alpha.init)) { fit <- suppressWarnings(rma.uni(yi, vi, mods = X, intercept = FALSE, method = "HE", skipr2 = TRUE)) tmp <- rstandard(fit) if (link == "log") { tmp <- suppressWarnings(rma.uni(log(tmp$resid^2), 4/tmp$resid^2 * tmp$se^2, mods = Z, intercept = FALSE, method = "FE")) alpha.init <- coef(tmp) } if (link == "identity") { tmp <- suppressWarnings(rma.uni(tmp$resid^2, tmp$se^2, mods = Z, intercept = FALSE, method = "FE")) alpha.init <- coef(tmp) if (any(Z %*% alpha.init < 0)) alpha.init <- ifelse(is.int, fit$tau2 + 0.01, 0) if (any(Z %*% alpha.init < 0)) stop(mstyle$stop("Unable to find suitable starting values for the scale parameters.")) } } else { alpha.init <- con$alpha.init if (!is.null(mZ)) { if (inherits(imZ, "try-error")) stop(mstyle$stop("Unable to rescale the starting values for the scale parameters.")) alpha.init <- c(imZ %*% cbind(alpha.init)) } if (link == "identity" && any(Z %*% alpha.init < 0)) stop(mstyle$stop("Starting values for the scale parameters lead to one or more negative tau^2 values.")) if (optbeta) fit <- suppressWarnings(rma.uni(yi, vi, mods = X, intercept = FALSE, method = "HE", skipr2 = TRUE)) } if (length(alpha.init) != q) stop(mstyle$stop(paste0("Length of the 'alpha.init' argument (", length(alpha.init), ") does not match the actual number of parameters (", q, ")."))) if (anyNA(alpha.init)) stop(mstyle$stop("No missing values allowed in 'alpha.init'.")) if (optbeta) { if (is.null(con$beta.init)) { beta.init <- c(fit$beta) } else { beta.init <- con$beta.init if (length(beta.init) != p) stop(mstyle$stop(paste0("Length of the 'beta.init' argument (", length(beta.init), ") does not match the actual number of parameters (", p, ")."))) if (anyNA(beta.init)) stop(mstyle$stop("No missing values allowed in 'beta.init'.")) } } else { beta.init <- NULL } if (is.null(con$omega2.init)) { omega2.init <- log(0.1) } else { if (length(con$omega2.init) != 1L) stop(mstyle$stop("Argument 'omega2.init' should specify a single value.")) if (is.na(con$omega2.init)) stop(mstyle$stop("No missing value allowed in 'omega2.init'.")) if (con$omega2.init <= 0) stop(mstyle$stop("Value of 'omega2.init' must be > 0.")) omega2.init <- log(con$omega2.init) } con$alpha.min <- .expand1(con$alpha.min, q) con$alpha.max <- .expand1(con$alpha.max, q) if (length(con$alpha.min) != q) stop(mstyle$stop(paste0("Length of the 'alpha.min' argument (", length(alpha.min), ") does not match the actual number of parameters (", q, ")."))) if (length(con$alpha.max) != q) stop(mstyle$stop(paste0("Length of the 'alpha.max' argument (", length(alpha.max), ") does not match the actual number of parameters (", q, ")."))) if (any(xor(is.infinite(con$alpha.min), is.infinite(con$alpha.max)))) stop(mstyle$stop("Constraints on the scale coefficients must be placed on both the lower and upper bound.")) alpha.min <- con$alpha.min alpha.max <- con$alpha.max if (link == "identity" && (any(alpha.min != -Inf) || any(alpha.max != Inf))) stop(mstyle$stop("Cannot use constraints on scale coefficients when using an identity link.")) alpha.init <- pmax(alpha.init, alpha.min) alpha.init <- pmin(alpha.init, alpha.max) alpha.init <- mapply(.mapinvfun.alpha, alpha.init, alpha.min, alpha.max) if (verbose > 1) message(mstyle$message("Estimating the scale parameters ...\n")) tmp <- .chkopt(optimizer, optcontrol, ineq = link == "identity") optimizer <- tmp$optimizer optcontrol <- tmp$optcontrol par.arg <- tmp$par.arg ctrl.arg <- tmp$ctrl.arg if (optimizer == "optimParallel::optimParallel") { parallel$cl <- NULL if (is.null(cl)) { ncpus <- as.integer(ncpus) if (ncpus < 1L) stop(mstyle$stop("Control argument 'ncpus' must be >= 1.")) cl <- parallel::makePSOCKcluster(ncpus) on.exit(parallel::stopCluster(cl), add = TRUE) } else { if (!inherits(cl, "SOCKcluster")) stop(mstyle$stop("Specified cluster is not of class 'SOCKcluster'.")) } parallel$cl <- cl if (is.null(parallel$forward)) parallel$forward <- FALSE if (is.null(parallel$loginfo)) { if (verbose) { parallel$loginfo <- TRUE } else { parallel$loginfo <- FALSE } } } if (link == "log") { if (randhet) { init.str <- "=c(beta.init, alpha.init, omega2.init), " } else { init.str <- "=c(beta.init, alpha.init), " } optcall <- paste0(optimizer, "(", par.arg, init.str, " .ll.rma.ls, ", ifelse(optimizer == "optim", "method=optmethod, ", ""), "yi=yi, vi=vi, X=X, Z=Z, reml=reml, alpha.arg=alpha, beta.arg=beta, omega2.arg=omega2, verbose=verbose, digits=digits,\n REMLf=con$REMLf, link=link, mZ=mZ, alpha.min=alpha.min, alpha.max=alpha.max, alpha.transf=TRUE, omega2.transf=TRUE,\n tau2.min=con$tau2.min, tau2.max=con$tau2.max, optbeta=optbeta, randhet=randhet, mfmaxit=con$mfmaxit", ctrl.arg, ")\n") } if (link == "identity") { if (optimizer == "constrOptim") optcall <- paste0("constrOptim(theta=c(beta.init, alpha.init), f=.ll.rma.ls, grad=NULL, ui=cbind(X0,Z), ci=rep(0,k),\n yi=yi, vi=vi, X=X, Z=Z, reml=reml, alpha.arg=alpha, beta.arg=beta, omega2.arg=omega2, verbose=verbose, digits=digits,\n REMLf=con$REMLf, link=link, mZ=mZ, alpha.min=alpha.min, alpha.max=alpha.max, alpha.transf=TRUE, omega2.transf=TRUE,\n tau2.min=con$tau2.min, tau2.max=con$tau2.max, optbeta=optbeta, randhet=randhet, mfmaxit=con$mfmaxit", ctrl.arg, ")\n") if (optimizer == "Rsolnp::solnp") optcall <- paste0("Rsolnp::solnp(pars=c(beta.init, alpha.init), fun=.ll.rma.ls, ineqfun=.rma.ls.ineqfun.pos, ineqLB=rep(0,k), ineqUB=rep(Inf,k),\n yi=yi, vi=vi, X=X, Z=Z, reml=reml, alpha.arg=alpha, beta.arg=beta, omega2.arg=omega2, verbose=verbose, digits=digits,\n REMLf=con$REMLf, link=link, mZ=mZ, alpha.min=alpha.min, alpha.max=alpha.max, alpha.transf=TRUE, omega2.transf=TRUE,\n tau2.min=con$tau2.min, tau2.max=con$tau2.max, optbeta=optbeta, randhet=randhet, mfmaxit=con$mfmaxit", ctrl.arg, ")\n") if (optimizer == "nloptr::nloptr") optcall <- paste0("nloptr::nloptr(x0=c(beta.init, alpha.init), eval_f=.ll.rma.ls, eval_g_ineq=.rma.ls.ineqfun.neg,\n yi=yi, vi=vi, X=X, Z=Z, reml=reml, alpha.arg=alpha, beta.arg=beta, omega2.arg=omega2, verbose=verbose, digits=digits,\n REMLf=con$REMLf, link=link, mZ=mZ, alpha.min=alpha.min, alpha.max=alpha.max, alpha.transf=TRUE, omega2.transf=TRUE,\n tau2.min=con$tau2.min, tau2.max=con$tau2.max, optbeta=optbeta, randhet=randhet, mfmaxit=con$mfmaxit", ctrl.arg, ")\n") if (is.element(optimizer, c("alabama::constrOptim.nl", "alabama::auglag"))) optcall <- paste0(optimizer, "(par=c(beta.init, alpha.init), fn=.ll.rma.ls, hin=.rma.ls.ineqfun.pos,\n yi=yi, vi=vi, X=X, Z=Z, reml=reml, alpha.arg=alpha, beta.arg=beta, omega2.arg=omega2, verbose=verbose, digits=digits,\n REMLf=con$REMLf, link=link, mZ=mZ, alpha.min=alpha.min, alpha.max=alpha.max, alpha.transf=TRUE, omega2.transf=TRUE,\n tau2.min=con$tau2.min, tau2.max=con$tau2.max, optbeta=optbeta, randhet=randhet, mfmaxit=con$mfmaxit", ctrl.arg, ")\n") } iteration <- 0 try(assign("iteration", iteration, envir = .metafor), silent = TRUE) if (verbose) { opt.res <- try(eval(str2lang(optcall)), silent = !verbose) } else { opt.res <- try(suppressWarnings(eval(str2lang(optcall))), silent = !verbose) } if (isTRUE(ddd$retopt)) return(opt.res) opt.res <- .chkconv(optimizer = optimizer, opt.res = opt.res, optcontrol = optcontrol, fun = "rma", verbose = verbose, paronly = FALSE) if (optbeta) { pos.beta <- 1:p pos.alpha <- (p + 1):(p + q) if (randhet) { pos.omega2 <- p + q + 1 } else { pos.omega2 <- integer(0) } } else { pos.alpha <- 1:q pos.beta <- integer(0) pos.omega2 <- integer(0) } opt.res$par[pos.alpha] <- mapply(.mapfun.alpha, opt.res$par[pos.alpha], alpha.min, alpha.max) opt.res$par[pos.beta] <- ifelse(is.na(beta), opt.res$par[pos.beta], beta) opt.res$par[pos.alpha] <- ifelse(is.na(alpha), opt.res$par[pos.alpha], alpha) beta.arg <- beta alpha.arg <- alpha omega2.arg <- omega2 beta <- cbind(opt.res$par[pos.beta]) alpha <- cbind(opt.res$par[pos.alpha]) lnomega2 <- unname(opt.res$par[pos.omega2]) omega2 <- exp(lnomega2) H <- NA_real_ if (con$hes.beta.fix) { beta.hes <- beta } else { beta.hes <- beta.arg } if (con$hes.alpha.fix) { alpha.hes <- alpha } else { alpha.hes <- alpha.arg } if (con$hes.omega2.fix || isTRUE(omega2 < con$omega2tol)) { omega2.hes <- omega2 } else { omega2.hes <- omega2.arg } if (optbeta) { if (randhet) { vcovmat <- matrix(NA_real_, nrow = p + q + 1, ncol = p + q + 1) hest <- c(is.na(beta.hes), is.na(alpha.hes), is.na(omega2.hes)) hesspars <- c(beta, alpha, omega2) } else { vcovmat <- matrix(NA_real_, nrow = p + q, ncol = p + q) hest <- c(is.na(beta.hes), is.na(alpha.hes)) hesspars <- c(beta, alpha) } } else { vcovmat <- matrix(NA_real_, nrow = q, ncol = q) hest <- is.na(alpha.hes) hesspars <- c(alpha) } if (any(hest) && !isTRUE(ddd$skiphes)) { if (verbose > 1) message(mstyle$message("\nComputing the Hessian ...")) if (con$htransf) { if (con$hesspack == "numDeriv") H <- try(numDeriv::hessian(func = .ll.rma.ls, x = opt.res$par, method.args = con$hessianCtrl, yi = yi, vi = vi, X = X, Z = Z, reml = reml, alpha.arg = alpha.hes, beta.arg = beta.hes, omega2.arg = omega2.hes, verbose = FALSE, digits = digits, REMLf = con$REMLf, link = link, mZ = mZ, alpha.min = alpha.min, alpha.max = alpha.max, alpha.transf = FALSE, omega2.transf = TRUE, tau2.min = con$tau2.min, tau2.max = con$tau2.max, optbeta = optbeta, randhet = randhet, mfmaxit = con$mfmaxit), silent = TRUE) if (con$hesspack == "pracma") H <- try(pracma::hessian(f = .ll.rma.ls, x0 = opt.res$par, h = con$hessianCtrl$h, yi = yi, vi = vi, X = X, Z = Z, reml = reml, alpha.arg = alpha.hes, beta.arg = beta.hes, omega2.arg = omega2.hes, verbose = FALSE, digits = digits, REMLf = con$REMLf, link = link, mZ = mZ, alpha.min = alpha.min, alpha.max = alpha.max, alpha.transf = FALSE, omega2.transf = TRUE, tau2.min = con$tau2.min, tau2.max = con$tau2.max, optbeta = optbeta, randhet = randhet, mfmaxit = con$mfmaxit), silent = TRUE) if (con$hesspack == "calculus") H <- try(calculus::hessian(f = .ll.rma.ls, var = opt.res$par, accuracy = con$hessianCtrl$accuracy, stepsize = con$hessianCtrl$stepsize, params = list(yi = yi, vi = vi, X = X, Z = Z, reml = reml, alpha.arg = alpha.hes, beta.arg = beta.hes, omega2.arg = omega2.hes, verbose = FALSE, digits = digits, REMLf = con$REMLf, link = link, mZ = mZ, alpha.min = alpha.min, alpha.max = alpha.max, alpha.transf = FALSE, omega2.transf = TRUE, tau2.min = con$tau2.min, tau2.max = con$tau2.max, optbeta = optbeta, randhet = randhet, mfmaxit = con$mfmaxit)), silent = TRUE) } else { if (con$hesspack == "numDeriv") H <- try(numDeriv::hessian(func = .ll.rma.ls, x = hesspars, method.args = con$hessianCtrl, yi = yi, vi = vi, X = X, Z = Z, reml = reml, alpha.arg = alpha.hes, beta.arg = beta.hes, omega2.arg = omega2.hes, verbose = FALSE, digits = digits, REMLf = con$REMLf, link = link, mZ = mZ, alpha.min = alpha.min, alpha.max = alpha.max, alpha.transf = FALSE, omega2.transf = FALSE, tau2.min = con$tau2.min, tau2.max = con$tau2.max, optbeta = optbeta, randhet = randhet, mfmaxit = con$mfmaxit), silent = TRUE) if (con$hesspack == "pracma") H <- try(pracma::hessian(f = .ll.rma.ls, x0 = hesspars, h = con$hessianCtrl$h, yi = yi, vi = vi, X = X, Z = Z, reml = reml, alpha.arg = alpha.hes, beta.arg = beta.hes, omega2.arg = omega2.hes, verbose = FALSE, digits = digits, REMLf = con$REMLf, link = link, mZ = mZ, alpha.min = alpha.min, alpha.max = alpha.max, alpha.transf = FALSE, omega2.transf = FALSE, tau2.min = con$tau2.min, tau2.max = con$tau2.max, optbeta = optbeta, randhet = randhet, mfmaxit = con$mfmaxit), silent = TRUE) if (con$hesspack == "calculus") H <- try(calculus::hessian(f = .ll.rma.ls, var = hesspars, accuracy = con$hessianCtrl$accuracy, stepsize = con$hessianCtrl$stepsize, params = list(yi = yi, vi = vi, X = X, Z = Z, reml = reml, alpha.arg = alpha.hes, beta.arg = beta.hes, omega2.arg = omega2.hes, verbose = FALSE, digits = digits, REMLf = con$REMLf, link = link, mZ = mZ, alpha.min = alpha.min, alpha.max = alpha.max, alpha.transf = FALSE, omega2.transf = FALSE, tau2.min = con$tau2.min, tau2.max = con$tau2.max, optbeta = optbeta, randhet = randhet, mfmaxit = con$mfmaxit)), silent = TRUE) } if (inherits(H, "try-error")) { warning(mstyle$warning("Error when trying to compute the Hessian."), call. = FALSE) } else { if (optbeta) { if (randhet) { rownames(H) <- colnames(H) <- c(paste0("X", colnames(X)), paste0("Z", colnames(Z)), "omega2") } else { rownames(H) <- colnames(H) <- c(paste0("X", colnames(X)), paste0("Z", colnames(Z))) } } else { rownames(H) <- colnames(H) <- colnames(Z) } H.hest <- H[hest, hest, drop = FALSE] iH.hest <- try(suppressWarnings(chol2inv(chol(H.hest))), silent = TRUE) if (inherits(iH.hest, "try-error") || anyNA(iH.hest) || any(is.infinite(iH.hest))) { warning(mstyle$warning("Error when trying to invert the Hessian."), call. = FALSE) } else { vcovmat[hest, hest] <- iH.hest } } } vb <- vcovmat[pos.beta, pos.beta, drop = FALSE] va <- vcovmat[pos.alpha, pos.alpha, drop = FALSE] vo <- vcovmat[pos.omega2, pos.omega2] vo <- max(0, vo) crit.omega2 <- qnorm(level/2, lower.tail = FALSE) if (con$htransf) { se.omega2 <- sqrt(vo) * omega2 ci.lb.omega2 <- exp(lnomega2 - crit.omega2 * sqrt(vo)) ci.ub.omega2 <- exp(lnomega2 + crit.omega2 * sqrt(vo)) } else { se.omega2 <- sqrt(vo) ci.lb.omega2 <- omega2 - crit.omega2 * se.omega2 ci.ub.omega2 <- omega2 + crit.omega2 * se.omega2 } ci.lb.omega2 <- max(0, ci.lb.omega2) if (any(alpha <= alpha.min + 10 * .Machine$double.eps^0.25) || any(alpha >= alpha.max - 10 * .Machine$double.eps^0.25)) warning(mstyle$warning("One or more 'alpha' estimates are (almost) equal to their lower or upper bound.\nTreat results with caution (or consider adjusting 'alpha.min' and/or 'alpha.max')."), call. = FALSE) if (!is.null(mZ)) { alpha <- mZ %*% alpha va[!hest, ] <- 0 va[, !hest] <- 0 va <- mZ %*% va %*% t(mZ) va[!hest, ] <- NA_real_ va[, !hest] <- NA_real_ Z <- Zsave } att <- .set.btt(att, q, Z.int.incl, colnames(Z)) m.alpha <- length(att) if (is.element(test, c("knha", "adhoc", "t"))) { ddf.alpha <- k - q } else { ddf.alpha <- NA_integer_ } QS <- try(as.vector(t(alpha)[att] %*% chol2inv(chol(va[att, att])) %*% alpha[att]), silent = TRUE) if (inherits(QS, "try-error")) QS <- NA_real_ se.alpha <- sqrt(diag(va)) if (isTRUE(ddd$abbrev)) { tmp <- colnames(Z) tmp <- gsub("relevel(factor(", "", tmp, fixed = TRUE) tmp <- gsub("\\), ref = \"[[:alnum:]]*\")", "", tmp) tmp <- gsub("poly(", "", tmp, fixed = TRUE) tmp <- gsub(", degree = [[:digit:]], raw = TRUE)", "^", tmp) tmp <- gsub(", degree = [[:digit:]], raw = TRUE)", "^", tmp) tmp <- gsub(", degree = [[:digit:]])", "^", tmp) tmp <- gsub("rcs\\([[:alnum:]]*, [[:digit:]]\\)", "", tmp) tmp <- gsub("factor(", "", tmp, fixed = TRUE) tmp <- gsub("I(", "", tmp, fixed = TRUE) tmp <- gsub(")", "", tmp, fixed = TRUE) colnames(Z) <- tmp } rownames(alpha) <- rownames(va) <- colnames(va) <- colnames(Z) names(se.alpha) <- NULL zval.alpha <- c(alpha/se.alpha) if (is.element(test, c("knha", "adhoc", "t"))) { QS <- QS/m.alpha QSdf <- c(m.alpha, ddf.alpha) QSp <- if (QSdf[2] > 0) pf(QS, df1 = QSdf[1], df2 = QSdf[2], lower.tail = FALSE) else NA_real_ pval.alpha <- if (ddf.alpha > 0) 2 * pt(abs(zval.alpha), df = ddf.alpha, lower.tail = FALSE) else rep(NA_real_, q) crit.alpha <- if (ddf.alpha > 0) qt(level/2, df = ddf.alpha, lower.tail = FALSE) else NA_real_ } else { QSdf <- c(m.alpha, ddf.alpha) QSp <- pchisq(QS, df = QSdf[1], lower.tail = FALSE) pval.alpha <- 2 * pnorm(abs(zval.alpha), lower.tail = FALSE) crit.alpha <- qnorm(level/2, lower.tail = FALSE) } ci.lb.alpha <- c(alpha - crit.alpha * se.alpha) ci.ub.alpha <- c(alpha + crit.alpha * se.alpha) if (link == "log") tau2 <- exp(as.vector(Z %*% alpha)) if (link == "identity") tau2 <- as.vector(Z %*% alpha) } if (is.element(method[1], c("FE", "EE", "CE"))) tau2 <- 0 if (verbose > 1) message(mstyle$message("\nModel fitting ...")) wi <- 1/(vi + tau2) W <- .diag(wi) M <- .diag(vi + tau2) if (weighted) { if (is.null(weights) || is.element(test, c("knha", "adhoc"))) { if (any(is.infinite(wi))) stop(mstyle$stop("Division by zero when computing the inverse variance weights.")) if (!optbeta) { if (tau2.inf) { beta <- cbind(coef(lm(yi ~ 0 + X))) vb <- .diag(rep(Inf, p)) } else { stXWX <- .invcalc(X = X, W = W, k = k) beta <- stXWX %*% crossprod(X, W) %*% Y vb <- stXWX } } RSS.f <- sum(wi * c(yi - X %*% beta)^2) RSS.knha <- RSS.f } if (!is.null(weights)) { A <- .diag(weights) stXAX <- .invcalc(X = X, W = A, k = k) beta <- stXAX %*% crossprod(X, A) %*% Y vb <- stXAX %*% t(X) %*% A %*% M %*% A %*% X %*% stXAX RSS.f <- sum(wi * c(yi - X %*% beta)^2) } if (is.element(test, c("knha", "adhoc"))) { if (RSS.knha <= .Machine$double.eps) { s2w <- 0 } else { s2w <- RSS.knha/(k - p) } } } else { stXX <- .invcalc(X = X, W = diag(k), k = k) beta <- stXX %*% crossprod(X, Y) vb <- tcrossprod(stXX, X) %*% M %*% X %*% stXX RSS.f <- sum(wi * (yi - X %*% beta)^2) if (is.element(test, c("knha", "adhoc"))) { if (any(is.infinite(wi))) stop(mstyle$stop("Division by zero when computing the inverse variance weights.")) stXWX <- .invcalc(X = X, W = W, k = k) beta.knha <- stXWX %*% crossprod(X, W) %*% Y RSS.knha <- sum(wi * (yi - X %*% beta.knha)^2) if (RSS.knha <= .Machine$double.eps) { s2w <- 0 } else { s2w <- RSS.knha/(k - p) } } } if (verbose > 1) message(mstyle$message("Conducting the tests of the fixed effects ...")) if (is.element(method[1], c("FE", "EE", "CE")) && is.element(test, c("knha", "adhoc"))) warning(mstyle$warning(paste0("Knapp and Hartung method is not meant to be used in the context of '", method[1], "' models.")), call. = FALSE) if (test == "adhoc") s2w[s2w < 1] <- 1 vb <- s2w * vb if (tau2.inf) vb <- diag(rep(Inf, p)) if (is.element(test, c("knha", "adhoc", "t"))) { ddf <- .chkddd(ddd$dfs, k - p, ddd$dfs[[1]]) } else { ddf <- NA_integer_ } QM <- try(as.vector(t(beta)[btt] %*% chol2inv(chol(vb[btt, btt])) %*% beta[btt]), silent = TRUE) if (inherits(QM, "try-error")) QM <- NA_real_ if (isTRUE(ddd$abbrev)) { tmp <- colnames(X) tmp <- gsub("relevel(factor(", "", tmp, fixed = TRUE) tmp <- gsub("\\), ref = \"[[:alnum:]]*\")", "", tmp) tmp <- gsub("poly(", "", tmp, fixed = TRUE) tmp <- gsub(", degree = [[:digit:]], raw = TRUE)", "^", tmp) tmp <- gsub(", degree = [[:digit:]], raw = TRUE)", "^", tmp) tmp <- gsub(", degree = [[:digit:]])", "^", tmp) tmp <- gsub("rcs\\([[:alnum:]]*, [[:digit:]]\\)", "", tmp) tmp <- gsub("factor(", "", tmp, fixed = TRUE) tmp <- gsub("I(", "", tmp, fixed = TRUE) tmp <- gsub(")", "", tmp, fixed = TRUE) colnames(X) <- tmp } rownames(beta) <- rownames(vb) <- colnames(vb) <- colnames(X.f) <- colnames(X) se <- sqrt(diag(vb)) names(se) <- NULL zval <- c(beta/se) if (is.element(test, c("knha", "adhoc", "t"))) { QM <- QM/m QMdf <- c(m, ddf) QMp <- if (QMdf[2] > 0) pf(QM, df1 = QMdf[1], df2 = QMdf[2], lower.tail = FALSE) else NA_real_ pval <- if (ddf > 0) 2 * pt(abs(zval), df = ddf, lower.tail = FALSE) else rep(NA_real_, p) crit <- if (ddf > 0) qt(level/2, df = ddf, lower.tail = FALSE) else NA_real_ } else { QMdf <- c(m, ddf) QMp <- pchisq(QM, df = QMdf[1], lower.tail = FALSE) pval <- 2 * pnorm(abs(zval), lower.tail = FALSE) crit <- qnorm(level/2, lower.tail = FALSE) } ci.lb <- c(beta - crit * se) ci.ub <- c(beta + crit * se) if (verbose > 1) message(mstyle$message("Conducting the heterogeneity test ...")) if (allvipos) { if (k > p) { wi <- 1/vi W.FE <- .diag(wi) stXWX <- .invcalc(X = X, W = W.FE, k = k) P <- W.FE - W.FE %*% X %*% stXWX %*% crossprod(X, W.FE) QE <- max(0, c(crossprod(Ymc, P) %*% Ymc)) QEp <- pchisq(QE, df = k - p, lower.tail = FALSE) if (i2def == "1") vt <- (k - p)/.tr(P) if (i2def == "2") vt <- 1/mean(wi) if (is.element(method[1], c("FE", "EE", "CE"))) { I2 <- max(0, 100 * (QE - (k - p))/QE) H2 <- QE/(k - p) } else { I2 <- 100 * tau2/(vt + tau2) H2 <- tau2/vt + 1 } } else { QE <- 0 QEp <- 1 I2 <- 0 H2 <- 1 vt <- 0 } } else { if (!vi0) warning(mstyle$warning(paste0("Cannot compute ", ifelse(int.only, "Q", "QE"), "-test, I^2, or H^2 when there are non-positive sampling variances in the data.")), call. = FALSE) vt <- NA_real_ } if (verbose > 1) message(mstyle$message("Computing fit statistics and log-likelihood ...")) q.est <- ifelse(model == "rma.uni", 0, sum(is.na(alpha.arg)) + (randhet && is.na(omega2.arg))) parms <- ifelse(optbeta, sum(is.na(beta.arg)), p) + ifelse(model == "rma.uni", ifelse(is.element(method[1], c("FE", "EE", "CE")) || tau2.fix, 0, 1), q.est) if (randhet) { ll.ML <- -opt.res$value ll.REML <- NA_real_ } else { ll.ML <- -1/2 * (k) * log(2 * base::pi) - 1/2 * sum(log(vi + tau2)) - 1/2 * RSS.f ll.REML <- -1/2 * (k - p) * log(2 * base::pi) + ifelse(con$REMLf, 1/2 * determinant(crossprod(X), logarithm = TRUE)$modulus, 0) + -1/2 * sum(log(vi + tau2)) - 1/2 * determinant(crossprod(X, W) %*% X, logarithm = TRUE)$modulus - 1/2 * RSS.f } if (k > p) { if (allvipos) { dev.ML <- -2 * (ll.ML - sum(dnorm(yi, mean = yi, sd = sqrt(vi), log = TRUE))) } else { dev.ML <- -2 * ll.ML } } else { dev.ML <- 0 } AIC.ML <- -2 * ll.ML + 2 * parms BIC.ML <- -2 * ll.ML + parms * log(k) AICc.ML <- -2 * ll.ML + 2 * parms * max(k, parms + 2)/(max(k, parms + 2) - parms - 1) dev.REML <- -2 * (ll.REML - 0) AIC.REML <- -2 * ll.REML + 2 * parms BIC.REML <- -2 * ll.REML + parms * log(k - p) AICc.REML <- -2 * ll.REML + 2 * parms * max(k - p, parms + 2)/(max(k - p, parms + 2) - parms - 1) fit.stats <- matrix(c(ll.ML, dev.ML, AIC.ML, BIC.ML, AICc.ML, ll.REML, dev.REML, AIC.REML, BIC.REML, AICc.REML), ncol = 2, byrow = FALSE) dimnames(fit.stats) <- list(c("ll", "dev", "AIC", "BIC", "AICc"), c("ML", "REML")) fit.stats <- data.frame(fit.stats) is.nested.in.int.only <- .is.nested(X, cbind(rep(1, k))) if (model == "rma.uni" && !int.only && is.nested.in.int.only && !isTRUE(ddd$skipr2)) { if (verbose > 1) message(mstyle$message("Computing R^2 ...")) if (is.element(method[1], c("FE", "EE", "CE"))) { if (identical(var(yi), 0)) { R2 <- 0 } else { if (weighted) { if (is.null(weights)) { R2 <- max(0, 100 * summary(lm(yi ~ X, weights = wi))$adj.r.squared) } else { R2 <- max(0, 100 * summary(lm(yi ~ X, weights = weights))$adj.r.squared) } } else { R2 <- max(0, 100 * summary(lm(yi ~ X))$adj.r.squared) } } } else { if (r2def %in% c("1", "1v", "3", "3v", "5", "6", "7", "8")) { args <- list(yi = yi, vi = vi, weights = weights, method = method, weighted = weighted, test = test, verbose = ifelse(verbose, TRUE, FALSE), control = con, digits = digits, outlist = "minimal") if (verbose > 1) { res0 <- try(.do.call(rma.uni, args), silent = FALSE) } else { res0 <- try(suppressWarnings(.do.call(rma.uni, args)), silent = TRUE) } if (!inherits(res0, "try-error")) { tau2.RE <- res0$tau2 if (identical(tau2.RE, 0) && r2def %in% c("1", "3")) { R2 <- 0 } else { ll0 <- logLik(res0) ll1 <- ifelse(method[1] == "ML", ll.ML, ll.REML) lls <- (ifelse(method[1] == "ML", dev.ML, dev.REML) + 2 * ll1)/2 if (r2def == "1") R2 <- (tau2.RE - tau2)/tau2.RE if (r2def == "1v") R2 <- (tau2.RE - tau2)/(tau2.RE + 1/mean(1/vi)) if (r2def == "3") R2 <- var(c(X %*% beta))/tau2.RE if (r2def == "3v") R2 <- var(c(X %*% beta))/(tau2.RE + 1/mean(1/vi)) if (r2def == "5") R2 <- 1 - ll1/ll0 if (r2def == "6") R2 <- 1 - (exp(ll0)/exp(ll1))^(2/k) if (r2def == "7") R2 <- (1 - (exp(ll0)/exp(ll1))^(2/k))/(1 - exp(ll0)^(2/k)) if (r2def == "8") R2 <- (ll1 - ll0)/(lls - ll0) } } else { R2 <- NA_real_ } } else { if (r2def == "2") R2 <- var(c(X %*% beta))/(var(c(X %*% beta)) + tau2) if (r2def == "2v") R2 <- var(c(X %*% beta))/(var(c(X %*% beta)) + tau2 + 1/mean(1/vi)) if (r2def == "4") R2 <- cor(yi, c(X %*% beta))^2 if (r2def == "4w") { if (is.null(weights)) { R2 <- cov.wt(cbind(yi, c(X %*% beta)), cor = TRUE, wt = 1/(vi + tau2))$cor[1, 2]^2 } else { R2 <- cov.wt(cbind(yi, c(X %*% beta)), cor = TRUE, wt = weights)$cor[1, 2]^2 } } } R2 <- max(0, 100 * R2) } } else { R2 <- NULL } if (isTRUE(ddd$pleasedonotreportI2thankyouverymuch)) { I2 <- NA H2 <- NA } if (verbose > 1) message(mstyle$message("Preparing the output ...")) p.eff <- p k.eff <- k if (is.null(ddd$outlist) || ddd$outlist == "nodata") { res <- list(b = beta, beta = beta, se = se, zval = zval, pval = pval, ci.lb = ci.lb, ci.ub = ci.ub, vb = vb, tau2 = tau2, se.tau2 = se.tau2, tau2.fix = tau2.fix, tau2.f = tau2, I2 = I2, H2 = H2, R2 = R2, vt = vt, QE = QE, QEp = QEp, QM = QM, QMdf = QMdf, QMp = QMp, k = k, k.f = k.f, k.eff = k.eff, k.all = k.all, p = p, p.eff = p.eff, parms = parms, int.only = int.only, int.incl = int.incl, intercept = intercept, allvipos = allvipos, coef.na = coef.na, yi = yi, vi = vi, X = X, weights = weights, yi.f = yi.f, vi.f = vi.f, X.f = X.f, weights.f = weights.f, M = M, chksumyi = digest::digest(as.vector(yi)), chksumvi = digest::digest(as.vector(vi)), chksumX = digest::digest(X), outdat.f = outdat.f, ni = ni, ni.f = ni.f, ids = ids, not.na = not.na, subset = subset, slab = slab, slab.null = slab.null, measure = measure, method = method[1], model = model, weighted = weighted, test = test, dfs = ddf, ddf = ddf, s2w = s2w, btt = btt, m = m, digits = digits, level = level, control = control, verbose = verbose, add = add, to = to, drop00 = drop00, fit.stats = fit.stats, formula.yi = formula.yi, formula.mods = formula.mods, version = packageVersion("metafor"), call = mf) if (is.null(ddd$outlist)) res <- append(res, list(data = data), which(names(res) == "fit.stats")) } else { if (ddd$outlist == "minimal") { res <- list(b = beta, beta = beta, se = se, zval = zval, pval = pval, ci.lb = ci.lb, ci.ub = ci.ub, vb = vb, tau2 = tau2, se.tau2 = se.tau2, tau2.fix = tau2.fix, I2 = I2, H2 = H2, R2 = R2, QE = QE, QEp = QEp, QM = QM, QMdf = QMdf, QMp = QMp, k = k, k.f = k.f, k.eff = k.eff, p = p, p.eff = p.eff, parms = parms, int.only = int.only, int.incl = int.incl, intercept = intercept, chksumyi = digest::digest(as.vector(yi)), chksumvi = digest::digest(as.matrix(vi)), chksumX = digest::digest(X), measure = measure, method = method[1], model = model, weighted = weighted, test = test, dfs = ddf, ddf = ddf, btt = btt, m = m, digits = digits, level = level, fit.stats = fit.stats) } else { res <- eval(str2lang(paste0("list(", ddd$outlist, ")"))) } } if (model == "rma.ls") { res$alpha <- alpha res$va <- va res$se.alpha <- se.alpha res$zval.alpha <- zval.alpha res$pval.alpha <- pval.alpha res$ci.lb.alpha <- ci.lb.alpha res$ci.ub.alpha <- ci.ub.alpha res$alpha.fix <- !is.na(alpha.arg) res$optbeta <- optbeta if (optbeta) { res$vcovmat <- vcovmat res$beta.fix <- !is.na(beta.arg) } res$randhet <- randhet if (randhet) { res$omega2 <- omega2 res$omega2.fix <- !is.na(omega2.arg) res$se.omega2 <- se.omega2 res$ci.lb.omega2 <- ci.lb.omega2 res$ci.ub.omega2 <- ci.ub.omega2 } res$q <- q res$alphas <- q res$link <- link res$Z <- Z res$Z.f <- Z.f res$tau2.f <- rep(NA_real_, k.f) res$tau2.f[not.na] <- tau2 res$att <- att res$m.alpha <- m.alpha res$ddf.alpha <- ddf.alpha res$QS <- QS res$QSdf <- QSdf res$QSp <- QSp res$formula.scale <- formula.scale res$Z.int.incl <- Z.int.incl res$Z.intercept <- Z.int.incl res$Z.int.only <- Z.int.only res$coef.na.Z <- coef.na.Z res$H <- H } time.end <- proc.time() res$time <- unname(time.end - time.start)[3] if (isTRUE(ddd$time)) .print.time(res$time) if (verbose || isTRUE(ddd$time)) cat("\n") if (model == "rma.ls") { class(res) <- c("rma.ls", "rma.uni", "rma") } else { class(res) <- c("rma.uni", "rma") } return(res) })(yi = c(0.608633627839699, 0.829063699821454, 0.758777742442889, -0.632681644068139, 0.583780204394991), sei = c(1.65045067235645, 0.926849181557613, 1.39949966536894, 0.39530076550589, 1.37058246455349), method = "REML")`: Fisher scoring algorithm did not converge. See 'help(rma)' for possible remedies.   Backtrace:        ▆     1. └─confMeta::confMeta(...) at test-confMeta.weights.R:13:5     2. └─confMeta:::new_confMeta(...)     3. └─confMeta:::get_stats_others(...)     4. └─base::lapply(...)     5. └─confMeta (local) FUN(X[[i]], ...)     6. └─confMeta:::get_obj_fe(estimates = estimates, SEs = SEs, conf_level = conf_level)     7. └─meta::metagen(...)     8. └─meta:::hetcalc(...)     9. └─meta:::runNN(...)    10. ├─base::do.call(func, args)    11. └─metafor (local) `<fn>`(yi = `<dbl>`, sei = `<dbl>`, method = "REML")      [ FAIL 1 | WARN 0 | SKIP 0 | PASS 79 ]   Error:   ! Test failures.   Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [21s] OK
• checking PDF version of manual ... [20s] OK
• checking HTML version of manual ... [7s] OK
• DONE

Status: 1 ERROR