metabind.R
#' Combine and summarize meta-analysis objects
#'
#' @description
#' This function can be used to combine meta-analysis objects and is,
#' for example, useful to summarize results of various meta-analysis
#' methods or to generate a forest plot with results of several
#' subgroup analyses.
#'
#' @param ... Any number of meta-analysis objects or a single list
#' with meta-analyses.
#' @param name An optional character vector providing descriptive
#' names for the meta-analysis objects.
#' @param pooled A character string indicating whether results of a
#' fixed effect or random effects model should be considered. Either
#' \code{"fixed"} or \code{"random"}, can be abbreviated.
#' @param backtransf A logical indicating whether results should be
#' back transformed in printouts and plots. If
#' \code{backtransf=TRUE} (default), results for \code{sm="OR"} are
#' printed as odds ratios rather than log odds ratios, for example.
#' @param outclab Outcome label for all meta-analyis objects.
#'
#' @details
#' This function can be used to combine any number of meta-analysis
#' objects which is useful, for example, to summarize results of
#' various meta-analysis methods or to generate a forest plot with
#' results of several subgroup analyses (see Examples).
#'
#' Individual study results are not retained with
#' \code{metabind}. This is possible using R function
#' \code{\link{metamerge}} which, however, can only be used to combine
#' results of two meta-analyses.
#'
#' @return
#' An object of class \code{c("metabind", "meta")} with corresponding
#' \code{print}, \code{summary}, and \code{forest} functions. See
#' \code{\link{metagen}} for more information on list elements.
#'
#' @author Guido Schwarzer \email{sc@@imbi.uni-freiburg.de}
#'
#' @seealso \code{\link{metagen}}, \code{\link{forest.metabind}},
#' \code{\link{metamerge}}
#'
#' @examples
#' data(Fleiss1993cont)
#'
#' # Add some (fictitious) grouping variables:
#' #
#' Fleiss1993cont$age <- c(55, 65, 55, 65, 55)
#' Fleiss1993cont$region <- c("Europe", "Europe", "Asia", "Asia", "Europe")
#'
#' m1 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont,
#' data = Fleiss1993cont, sm = "MD")
#'
#' # Conduct two subgroup analyses
#' #
#' mu1 <- update(m1, subgroup = age, subgroup.name = "Age group")
#' mu2 <- update(m1, subgroup = region, subgroup.name = "Region")
#'
#' # Combine subgroup meta-analyses and show forest plot with subgroup
#' # results
#' #
#' mb1 <- metabind(mu1, mu2)
#' mb1
#' forest(mb1)
#'
#' # Use various estimation methods for between-study heterogeneity
#' # variance
#' #
#' m1.pm <- update(m1, method.tau = "PM")
#' m1.reml <- update(m1, method.tau = "REML")
#' m1.ml <- update(m1, method.tau = "ML")
#' m1.hs <- update(m1, method.tau = "HS")
#' m1.sj <- update(m1, method.tau = "SJ")
#' m1.he <- update(m1, method.tau = "HE")
#' m1.eb <- update(m1, method.tau = "EB")
#'
#' # Combine meta-analyses and show results
#' #
#' taus <- c("DerSimonian-Laird estimator",
#' "Paule-Mandel estimator",
#' "Restricted maximum-likelihood estimator",
#' "Maximum-likelihood estimator",
#' "Hunter-Schmidt estimator",
#' "Sidik-Jonkman estimator",
#' "Hedges estimator",
#' "Empirical Bayes estimator")
#' #
#' m1.taus <- metabind(m1, m1.pm, m1.reml, m1.ml, m1.hs, m1.sj, m1.he, m1.eb,
#' name = taus, pooled = "random")
#' m1.taus
#' forest(m1.taus, print.I2 = FALSE, print.pval.Q = FALSE)
#'
#' @export metabind
metabind <- function(..., name, pooled, backtransf, outclab) {
if (!missing(pooled))
pooled <- setchar(pooled, c("fixed", "random"))
##
if (!missing(backtransf))
chklogical(backtransf)
args <- list(...)
##
n.meta <- length(args)
n.i <- seq_len(n.meta)
##
if (length(args) == 1) {
if (inherits(args[[1]], "meta.rm5")) {
args <- args[[1]]
if (missing(name))
name <- unlist(lapply(args, "[[" , "outclab"))
}
else if (!is.list(args[[1]]))
stop("All elements of argument '...' must be of ",
"class 'meta'.",
call. = FALSE)
##
if (!inherits(args[[1]], "meta")) {
n.meta <- length(args[[1]])
n.i <- seq_len(n.meta)
##
args2 <- list()
for (i in n.i)
args2[[i]] <- args[[1]][[i]]
##
args <- args2
}
}
##
for (i in n.i) {
if (inherits(args[[i]], "metabind"))
stop("Elements of argument '...' may not be of ",
"class 'metabind'.",
call. = FALSE)
##
if (!inherits(args[[i]], "meta"))
stop("All elements of argument '...' must be of class 'meta'.",
call. = FALSE)
else
args[[i]] <- updateversion(args[[i]])
}
is.subgroup <- rep(FALSE, n.meta)
##
for (i in n.i) {
if (!is.null(args[[i]]$subgroup))
is.subgroup[i] <- TRUE
}
print.warning1 <- FALSE
print.warning2 <- FALSE
print.warning3 <- FALSE
##
## Name of meta-analysis object
##
if (missing(name)) {
name <- rep("", n.meta)
##
for (i in n.i)
name[i] <- replaceNULL(args[[i]]$subgroup.name)
##
if (all(is.na(name)))
name <- paste0("meta", n.i)
else if (anyNA(name))
name[is.na(name)] <- paste0("meta", n.i[is.na(name)])
##
if (length(unique(name)) != length(name)) {
warning1 <-
paste0("Meta-analyses are labelled 'meta1' to 'meta", n.meta,
"' as argument 'name' is missing and information ",
"from meta-analysis objects is not unique.")
print.warning1 <- TRUE
##
name <- paste0("meta", n.i)
}
}
else {
if (length(name) != length(is.subgroup))
stop("Number of meta-analyses and names provided in ",
"argument 'name' differ.",
call. = FALSE)
##
if (length(unique(name)) != length(name)) {
warning2 <-
paste0("Meta-analyses are labelled 'meta1' to 'meta", n.meta,
"' as values of argument 'name' are not all disparate.")
print.warning2 <- FALSE
##
name <- paste0("meta", n.i)
}
}
for (i in n.i) {
m.i <- args[[i]]
##
meth.i <- data.frame(sm = m.i$sm,
method = m.i$method,
level = m.i$level.ma,
level.ma = m.i$level.ma,
level.predict = m.i$level.predict,
fixed = m.i$fixed,
random = m.i$random,
hakn = m.i$hakn,
method.tau = m.i$method.tau,
tau.preset = replaceNULL(m.i$tau.preset),
TE.tau = replaceNULL(m.i$TE.tau),
tau.common = m.i$tau.common,
prediction = m.i$prediction,
method.bias = "",
null.effect = m.i$null.effect,
##
title = m.i$title,
complab = m.i$complab,
outclab = if (missing(outclab)) m.i$outclab else outclab,
label.e = m.i$label.e,
label.c = m.i$label.c,
label.left = m.i$label.left,
label.right = m.i$label.right,
##
print.subgroup.name = FALSE,
sep.subgroup = "",
warn = replaceNULL(m.i$warn, FALSE),
##
backtransf = m.i$backtransf,
pscale = m.i$pscale,
irscale = m.i$irscale,
irunit = replaceNULL(m.i$ir.unit),
##
stringsAsFactors = FALSE)
##
if (i == 1)
meth <- meth.i
else
meth <- rbind(meth, meth.i)
}
##
## Unify some settings
##
if (missing(pooled)) {
if (all(meth$fixed) & all(!meth$random))
pooled <- "fixed"
else if (all(!meth$fixed) & all(meth$random))
pooled <- "random"
else {
if (any(meth$fixed)) {
warning3 <-
paste("Note, results from random effects model extracted.",
"Use argument pooled = \"fixed\" for results of",
"fixed effect model.")
print.warning3 <- TRUE
}
pooled <- "random"
}
}
##
if (pooled == "fixed") {
meth$fixed <- TRUE
meth$random <- FALSE
}
else {
meth$fixed <- FALSE
meth$random <- TRUE
}
for (i in n.i) {
m.i <- args[[i]]
##
if (length(m.i$tau) > 1)
if (pooled == "random") {
m.i$tau <- m.i$tau[2]
m.i$tau2 <- m.i$tau2[2]
##
if (length(m.i$lower.tau) > 1) {
m.i$lower.tau <- m.i$lower.tau[2]
m.i$upper.tau <- m.i$upper.tau[2]
m.i$lower.tau2 <- m.i$lower.tau2[2]
m.i$upper.tau2 <- m.i$upper.tau2[2]
}
}
else {
m.i$tau <- m.i$tau[1]
m.i$tau2 <- m.i$tau2[1]
##
if (length(m.i$lower.tau) > 1) {
m.i$lower.tau <- m.i$lower.tau[1]
m.i$upper.tau <- m.i$upper.tau[1]
m.i$lower.tau2 <- m.i$lower.tau2[1]
m.i$upper.tau2 <- m.i$upper.tau2[1]
}
}
##
subgroup.i <- data.frame(TE.fixed.w = m.i$TE.fixed,
seTE.fixed.w = m.i$seTE.fixed,
lower.fixed.w = m.i$lower.fixed,
upper.fixed.w = m.i$upper.fixed,
statistic.fixed.w = m.i$statistic.fixed,
pval.fixed.w = m.i$pval.fixed,
w.fixed.w = 0, # sum(m.i$w.fixed),
##
TE.random.w = m.i$TE.random,
seTE.random.w = m.i$seTE.random,
lower.random.w = m.i$lower.random,
upper.random.w = m.i$upper.random,
statistic.random.w = m.i$statistic.random,
pval.random.w = m.i$pval.random,
df.hakn.w = replaceNULL(m.i$df.hakn),
w.random.w = 0, # sum(m.i$w.random),
##
n.harmonic.mean.w =
1 / mean(1 / replaceNULL(m.i$n)),
t.harmonic.mean.w =
1 / mean(1 / replaceNULL(m.i$time)),
##
n.e.w = sum(replaceNULL(m.i$n.e)),
n.c.w = sum(replaceNULL(m.i$n.c)),
##
k.w = m.i$k,
k.all.w = length(m.i$TE),
##
Q.w = m.i$Q,
df.Q.w = m.i$df.Q,
pval.Q.w = m.i$pval.Q,
##
tau2.w = m.i$tau2,
se.tau2.w = m.i$se.tau2,
lower.tau2.w = m.i$lower.tau2,
upper.tau2.w = m.i$upper.tau2,
tau.w = m.i$tau,
lower.tau.w = m.i$lower.tau,
upper.tau.w = m.i$upper.tau,
H.w = m.i$H,
lower.H.w = m.i$lower.H,
upper.H.w = m.i$upper.H,
I2.w = m.i$I2,
lower.I2.w = m.i$lower.I2,
upper.I2.w = m.i$upper.I2,
Rb.w = m.i$Rb,
lower.Rb.w = m.i$lower.Rb,
upper.Rb.w = m.i$upper.Rb,
##
stringsAsFactors = FALSE)
##
if (is.subgroup[i]) {
##
Q.b.fixed.i <- m.i$Q.b.fixed
Q.b.random.i <- m.i$Q.b.random
df.Q.b.i <- m.i$df.Q.b
pval.Q.b.fixed.i <- m.i$pval.Q.b.fixed
pval.Q.b.random.i <- m.i$pval.Q.b.random
##
n.bylevs.i <- length(m.i$k.w) - 1
##
if (n.bylevs.i > 0) {
Q.b.fixed.i <- c(Q.b.fixed.i, rep(NA, n.bylevs.i))
Q.b.random.i <- c(Q.b.random.i, rep(NA, n.bylevs.i))
df.Q.b.i <- c(df.Q.b.i, rep(NA, n.bylevs.i))
pval.Q.b.fixed.i <- c(pval.Q.b.fixed.i, rep(NA, n.bylevs.i))
pval.Q.b.random.i <- c(pval.Q.b.random.i, rep(NA, n.bylevs.i))
}
##
data.i <- data.frame(name = name[i],
bylevs = m.i$bylevs,
##
n.e = replaceNULL(m.i$n.e.w),
n.c = replaceNULL(m.i$n.c.w),
df.hakn = replaceNULL(m.i$df.hakn.w),
##
n.harmonic.mean = m.i$n.harmonic.mean.w,
t.harmonic.mean = m.i$t.harmonic.mean.w,
##
k = m.i$k.w,
k.all = m.i$k.all.w,
Q = m.i$Q.w,
df.Q = m.i$k.w - 1,
pval.Q = pvalQ(m.i$Q.w, m.i$k.w - 1),
##
tau2 = m.i$tau.w^2,
lower.tau2 = m.i$lower.tau2.w,
upper.tau2 = m.i$upper.tau2.w,
tau = m.i$tau.w,
lower.tau = m.i$lower.tau.w,
upper.tau = m.i$upper.tau.w,
H = m.i$H.w,
lower.H = m.i$lower.H.w,
upper.H = m.i$upper.H.w,
I2 = m.i$I2.w,
lower.I2 = m.i$lower.I2.w,
upper.I2 = m.i$upper.I2.w,
Rb = m.i$Rb.w,
lower.Rb = m.i$lower.Rb.w,
upper.Rb = m.i$upper.Rb.w,
##
Q.b.fixed = Q.b.fixed.i,
Q.b.random = Q.b.random.i,
df.Q.b = df.Q.b.i,
pval.Q.b.fixed = pval.Q.b.fixed.i,
pval.Q.b.random = pval.Q.b.random.i,
##
stringsAsFactors = FALSE)
}
else
data.i <- data.frame(name = name[i],
bylevs = "overall",
##
n.e = sum(replaceNULL(m.i$n.e)),
n.c = sum(replaceNULL(m.i$n.c)),
df.hakn = replaceNULL(m.i$df.hakn),
##
n.harmonic.mean =
1 / mean(1 / replaceNULL(m.i$n)),
t.harmonic.mean =
1 / mean(1 / replaceNULL(m.i$time)),
##
k = m.i$k,
k.all = length(m.i$TE),
Q = m.i$Q,
df.Q = m.i$df.Q,
pval.Q = pvalQ(m.i$Q, m.i$df.Q),
##
tau = m.i$tau,
lower.tau = m.i$lower.tau,
upper.tau = m.i$upper.tau,
tau2 = m.i$tau^2,
lower.tau2 = m.i$lower.tau2,
upper.tau2 = m.i$upper.tau2,
H = m.i$H,
lower.H = m.i$lower.H,
upper.H = m.i$upper.H,
I2 = m.i$I2,
lower.I2 = m.i$lower.I2,
upper.I2 = m.i$upper.I2,
Rb = m.i$Rb,
lower.Rb = m.i$lower.Rb,
upper.Rb = m.i$upper.Rb,
##
Q.b.fixed = NA,
Q.b.random = NA,
df.Q.b = NA,
pval.Q.b.fixed = NA,
pval.Q.b.random = NA,
##
stringsAsFactors = FALSE)
##
overall.i <- data.frame(name = name[i],
##
TE.fixed = m.i$TE.fixed,
seTE.fixed = m.i$seTE.fixed,
lower.fixed = m.i$lower.fixed,
upper.fixed = m.i$upper.fixed,
statistic.fixed = m.i$statistic.fixed,
pval.fixed = m.i$pval.fixed,
##
TE.random = m.i$TE.random,
seTE.random = m.i$seTE.random,
lower.random = m.i$lower.random,
upper.random = m.i$upper.random,
statistic.random = m.i$statistic.random,
pval.random = m.i$pval.random,
df.hakn = replaceNULL(m.i$df.hakn),
##
n.harmonic.mean.ma =
1 / mean(1 / replaceNULL(m.i$n)),
t.harmonic.mean.ma =
1 / mean(1 / replaceNULL(m.i$time)),
##
seTE.predict = m.i$seTE.predict,
lower.predict = m.i$lower.predict,
upper.predict = m.i$upper.predict,
##
k = m.i$k,
k.all = length(m.i$TE),
##
Q = m.i$Q,
df.Q = m.i$df.Q,
tau2 = m.i$tau2,
lower.tau2 = m.i$lower.tau2,
upper.tau2 = m.i$upper.tau2,
se.tau2 = replaceNULL(m.i$se.tau2),
tau = m.i$tau,
lower.tau = m.i$lower.tau,
upper.tau = m.i$upper.tau,
##
H = m.i$H,
lower.H = m.i$lower.H,
upper.H = m.i$upper.H,
I2 = m.i$I2,
lower.I2 = m.i$lower.I2,
upper.I2 = m.i$upper.I2,
Rb = m.i$Rb,
lower.Rb = m.i$lower.Rb,
upper.Rb = m.i$upper.Rb,
##
Q.w.fixed = NA,
Q.w.random = NA,
##
Q.b.fixed = NA,
pval.Q.b.fixed = NA,
Q.b.random = NA,
df.Q.b = NA,
pval.Q.b.random = NA,
##
stringsAsFactors = FALSE)
##
if (i == 1) {
data <- data.i
overall <- overall.i
subgroup <- subgroup.i
}
else {
data <- rbind(data, data.i)
overall <- rbind(overall, overall.i)
subgroup <- rbind(subgroup, subgroup.i)
}
}
## Unify more settings
##
if (missing(backtransf)) {
if (any(meth$backtransf))
meth$backtransf <- TRUE
}
else
meth$backtransf <- backtransf
##
if (any(meth$warn))
meth$warn <- TRUE
##
if (any(meth$prediction))
meth$prediction <- TRUE
##
## Only consider argument 'tau.common' from subgroup meta-analyses
##
if (any(is.subgroup) & any(!is.subgroup)) {
tau.common.uniq <- unique(meth$tau.common[is.subgroup])
if (length(tau.common.uniq) == 1)
meth$tau.common[!is.subgroup] <- tau.common.uniq
}
##
show.studies <- TRUE
overall.hetstat <- TRUE
##
if (length(unique(meth$method)) != 1) {
meth$method <- ""
show.studies <- FALSE
overall.hetstat <- FALSE
}
##
if (length(unique(meth$hakn)) != 1) {
meth$hakn <- FALSE
}
##
if (length(unique(meth$method.tau)) != 1) {
meth$method.tau <- ""
show.studies <- FALSE
overall.hetstat <- FALSE
}
##
if (length(unique(meth$method.bias)) != 1)
meth$method.bias <- ""
##
if (length(unique(meth$title)) != 1)
meth$title <- ""
##
if (length(unique(meth$complab)) != 1)
meth$complab <- ""
##
if (length(unique(meth$outclab)) != 1)
meth$outclab <- ""
##
if (length(unique(meth$label.e)) != 1)
meth$label.e <- ""
##
if (length(unique(meth$label.c)) != 1)
meth$label.c <- ""
##
if (length(unique(meth$label.left)) != 1)
meth$label.left <- ""
##
if (length(unique(meth$label.right)) != 1)
meth$label.right <- ""
##
if (length(unique(meth$pscale)) != 1)
meth$pscale <- min(meth$pscale)
##
if (length(unique(meth$irscale)) != 1)
meth$irscale <- min(meth$irscale)
##
if (length(unique(meth$irunit)) != 1)
meth$irunit <- NA
## Check whether settings are unique
##
meth2 <- meth
meth2$level <- NULL
n.meth <- apply(meth2, 2,
function(x)
length(unique(x)))
##
if (any(n.meth != 1))
stop("Setting for the following argument",
if (sum(n.meth != 1) > 1) "s",
" must be the same for all meta-analyses",
": ",
paste0(paste0("'", names(meth2)[n.meth != 1], "'"),
collapse = " - "))
for (i in n.i) {
m.i <- args[[i]]
##
study.i <- data.frame(studlab = replaceNULL(m.i$bylevs, "overall"),
stringsAsFactors = FALSE)
##
if (is.subgroup[i]) {
study.i$n.e <- replaceNULL(m.i$n.e.w)
study.i$n.c <- replaceNULL(m.i$n.c.w)
##
if (pooled == "fixed") {
study.i$TE <- m.i$TE.fixed.w
study.i$seTE <- m.i$seTE.fixed.w
study.i$lower <- m.i$lower.fixed.w
study.i$upper <- m.i$upper.fixed.w
study.i$statistic <- m.i$statistic.fixed.w
study.i$pval <- m.i$pval.fixed.w
study.i$w.fixed <- m.i$w.fixed.w
study.i$w.random <- 0
}
else {
study.i$TE <- m.i$TE.random.w
study.i$seTE <- m.i$seTE.random.w
study.i$lower <- m.i$lower.random.w
study.i$upper <- m.i$upper.random.w
study.i$statistic <- m.i$statistic.random.w
study.i$pval <- m.i$pval.random.w
study.i$w.fixed <- 0
study.i$w.random <- m.i$w.random.w
}
}
else {
study.i$n.e <- sum(replaceNULL(m.i$n.e.w))
study.i$n.c <- sum(replaceNULL(m.i$n.c.w))
##
if (pooled == "fixed") {
study.i$TE <- m.i$TE.fixed
study.i$seTE <- m.i$seTE.fixed
study.i$lower <- m.i$lower.fixed
study.i$upper <- m.i$upper.fixed
study.i$statistic <- m.i$statistic.fixed
study.i$pval <- m.i$pval.fixed
study.i$w.fixed <- 1
study.i$w.random <- 0
}
else {
study.i$TE <- m.i$TE.random
study.i$seTE <- m.i$seTE.random
study.i$lower <- m.i$lower.random
study.i$upper <- m.i$upper.random
study.i$statistic <- m.i$statistic.random
study.i$pval <- m.i$pval.random
study.i$w.fixed <- 0
study.i$w.random <- 1
}
}
##
study.i$subgroup <- name[i]
##
if (i == 1)
study <- study.i
else
study <- rbind(study, study.i)
}
if (length(unique(study$subgroup)) == 1) {
res <- c(as.list(study), as.list(meth[1, ]), as.list(overall))
res$subgroup <- NULL
}
else
res <- c(as.list(study), as.list(meth[1, ]),
as.list(overall), as.list(subgroup))
##
##
res$data <- data
res$n.harmonic.mean <- data$n.harmonic.mean
res$t.harmonic.mean <- data$t.harmonic.mean
##
res$call <- match.call()
res$version <- packageDescription("meta")$Version
makeunique <- function(x, val = NA) {
if (length(unique(x)) == 1)
res <- unique(x)
else
res <- val
##
res
}
##
res$TE.fixed <- makeunique(res$TE.fixed)
res$seTE.fixed <- makeunique(res$seTE.fixed)
res$lower.fixed <- makeunique(res$lower.fixed)
res$upper.fixed <- makeunique(res$upper.fixed)
res$statistic.fixed <- makeunique(res$statistic.fixed)
res$pval.fixed <- makeunique(res$pval.fixed)
##
res$TE.random <- makeunique(res$TE.random)
res$seTE.random <- makeunique(res$seTE.random)
res$lower.random <- makeunique(res$lower.random)
res$upper.random <- makeunique(res$upper.random)
res$statistic.random <- makeunique(res$statistic.random)
res$pval.random <- makeunique(res$pval.random)
res$df.hakn <- makeunique(res$df.hakn)
##
res$seTE.predict <- makeunique(res$seTE.predict)
res$lower.predict <- makeunique(res$lower.predict)
res$upper.predict <- makeunique(res$upper.predict)
##
res$k <- makeunique(res$k)
res$k.all <- makeunique(res$k.all)
##
res$Q <- makeunique(res$Q)
res$df.Q <- makeunique(res$df.Q, 0)
res$pval.Q <- makeunique(makeunique(res$pval.Q, pvalQ(res$Q, res$df.Q)))
res$tau2 <- makeunique(res$tau2)
res$se.tau2 <- makeunique(res$se.tau2)
res$tau <- makeunique(res$tau)
res$lower.tau <- res$upper.tau <- NA
res$lower.tau2 <- res$upper.tau2 <- NA
res$method.tau.ci <- ""
##
res$H <- makeunique(res$H)
res$lower.H <- makeunique(res$lower.H)
res$upper.H <- makeunique(res$upper.H)
##
res$I2 <- makeunique(res$I2)
res$lower.I2 <- makeunique(res$lower.I2)
res$upper.I2 <- makeunique(res$upper.I2)
##
res$n.harmonic.mean.ma <- makeunique(res$n.harmonic.mean.ma)
res$t.harmonic.mean.ma <- makeunique(res$t.harmonic.mean.ma)
##
res$Rb <- makeunique(res$Rb)
res$lower.Rb <- makeunique(res$lower.Rb)
res$upper.Rb <- makeunique(res$upper.Rb)
##
res$Q.w.fixed <- makeunique(res$Q.w.fixed)
res$Q.w.random <- makeunique(res$Q.w.random)
res$df.Q.w <- makeunique(res$df.Q.w, 0)
##
res$Q.b.fixed <- makeunique(res$Q.b.fixed)
res$Q.b.random <- makeunique(res$Q.b.random)
##
res$df.Q.b <- makeunique(res$df.Q.b, 0)
res$pval.Q.b.fixed <-
makeunique(makeunique(res$pval.Q.b.fixed,
pvalQ(res$Q.b.fixed, res$df.Q.b)))
res$pval.Q.b.random <-
makeunique(makeunique(res$pval.Q.b.random,
pvalQ(res$Q.b.random, res$df.Q.b)))
##
res$show.studies <- show.studies
res$overall.hetstat <- overall.hetstat
res$is.subgroup <- is.subgroup
if (!is.null(res$subgroup)) {
res$subgroup.name <- "meta-analysis"
res$bylevs <- unique(res$subgroup)
res$w.fixed <- rep(0, length(res$w.fixed))
res$w.fixed.w <- rep(0, length(res$w.fixed.w))
res$w.random <- rep(0, length(res$w.random))
res$w.random.w <- rep(0, length(res$w.random.w))
res$lower.predict.w <- rep(NA, length(res$w.random.w))
res$upper.predict.w <- rep(NA, length(res$w.random.w))
}
if (is.na(res$tau.preset))
res$tau.preset <- NULL
class(res) <- c("metabind", "meta")
##
##
##
if (print.warning1)
warning(warning1)
if (print.warning2)
warning(warning2)
if (print.warning3)
warning(warning3)
res
}
