R/forest.netsplit.R
In guido-s/netmeta: Network Meta-Analysis using Frequentist Methods
Defines functions
plot.netsplit
forest.netsplit
Documented in
forest.netsplit
plot.netsplit
#' Forest plot for direct and indirect evidence
#'
#' @description
#' Forest plot to show direct and indirect evidence in network
#' meta-analysis. Furthermore, estimates from network meta-analysis
#' as well as prediction intervals can be printed.
#'
#' @aliases forest.netsplit plot.netsplit
#'
#' @param x An object of class \code{netsplit}.
#' @param pooled A character string indicating whether results for the
#' common (\code{"common"}) or random effects model (\code{"random"})
#' should be plotted. Can be abbreviated.
#' @param show A character string indicating which comparisons should
#' be printed (see Details).
#' @param overall A logical indicating whether network meta-analysis
#' estimates should be printed.
#' @param direct A logical indicating whether direct estimates should
#' be printed.
#' @param indirect A logical indicating whether indirect estimates
#' should be printed.
#' @param prediction A logical indicating whether prediction intervals
#' should be printed.
#' @param only.reference A logical indicating whether only comparisons
#' with the reference group should be printed.
#' @param sortvar An optional vector used to sort comparisons (must be
#' of same length as the total number of comparisons).
#' @param subset An optional logical vector specifying a subset of
#' comparisons to print (must be of same length as the total number of
#' comparisons) .
#' @param subgroup A character string indicating which layout should
#' be used in forest plot: subgroups by comparisons
#' (\code{"comparison"}) or subgroups by estimates
#' (\code{"estimate"}). Can be abbreviated.
#' @param text.overall A character string used in the plot to label
#' the network estimates.
#' @param text.direct A character string used in the plot to label the
#' direct estimates.
#' @param text.indirect A character string used in the plot to label
#' the indirect estimates.
#' @param text.predict A character string used in the plot to label
#' the prediction interval.
#' @param type.overall A character string specifying how to plot
#' treatment effects and confidence intervals for the overall
#' network evidence.
#' @param type.direct A character string specifying how to plot
#' treatment effects and confidence intervals for the direct
#' evidence.
#' @param type.indirect A character string specifying how to plot
#' treatment effects and confidence intervals for the indirect
#' evidence.
#' @param col.square The colour for squares.
#' @param col.square.lines The colour for the outer lines of squares.
#' @param col.inside The colour for results and confidence limits if
#' confidence limits are completely within squares squares.
#' @param col.diamond The colour of diamonds.
#' @param col.diamond.lines The colour of the outer lines of diamonds.
#' @param col.predict Background colour of prediction intervals.
#' @param col.predict.lines Colour of outer lines of prediction
#' intervals.
#' @param equal.size A logical indicating whether all squares should
#' be of equal size. Otherwise, the square size is proportional to
#' the precision of estimates.
#' @param leftcols A character vector specifying columns to be plotted
#' on the left side of the forest plot (see Details).
#' @param leftlabs A character vector specifying labels for columns on
#' left side of the forest plot.
#' @param rightcols A character vector specifying columns to be
#' plotted on the right side of the forest plot (see Details).
#' @param rightlabs A character vector specifying labels for columns
#' on right side of the forest plot.
#' @param digits Minimal number of significant digits for treatment
#' effects and confidence intervals, see \code{print.default}.
#' @param digits.prop Minimal number of significant digits for the
#' direct evidence proportion.
#' @param backtransf A logical indicating whether results should be
#' back transformed in forest plots. If \code{backtransf = TRUE},
#' results for \code{sm = "OR"} are presented as odds ratios rather
#' than log odds ratios, for example.
#' @param lab.NA A character string to label missing values.
#' @param smlab A label printed at top of figure. By default, text
#' indicating either common or random effects model is printed.
#' @param \dots Additional arguments for \code{\link{forest.meta}}
#' function.
#'
#' @details
#' A forest plot, also called confidence interval plot, is drawn in
#' the active graphics window.
#'
#' The arguments \code{leftcols} and \code{rightcols} can be used to
#' specify columns which are plotted on the left and right side of the
#' forest plot, respectively. If argument \code{rightcols} is
#' \code{FALSE}, no columns will be plotted on the right side.
#'
#' If direct estimates are included in the forest plot (\code{direct =
#' TRUE}, default), the following columns will be printed on the left
#' side of the forest plot: the comparisons (column \code{"studlab"}
#' in \code{\link{forest.meta}}), number of pairwise comparisons
#' (\code{"k"}), direct evidence proportion (\code{"k"}), and
#' I\eqn{^2} from pairwise comparison (\code{"I2"}).
#'
#' If direct estimates are not included in the forest plot
#' (\code{direct = FALSE}), only the comparisons (\code{"studlab"})
#' are printed on the left side of the forest plot.
#'
#' For more information see help page of \code{\link{forest.meta}}
#' function.
#'
#' Argument \code{show} determines which comparisons are printed:
#' \tabular{ll}{
#' \dQuote{all} \tab All comparisons \cr
#' \dQuote{both} \tab Only comparisons contributing both direct and
#' indirect evidence \cr
#' \dQuote{with.direct} \tab Comparisons providing direct evidence \cr
#' \dQuote{direct.only} \tab Comparisons providing only direct
#' evidence \cr
#' \dQuote{indirect.only} \tab Comparisons providing only indirect
#' evidence
#' }
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{forest.meta}}
#'
#' @keywords hplot
#'
#' @examples
#' data(Senn2013)
#'
#' # Only consider first five studies (to reduce runtime of example)
#' #
#' studies <- unique(Senn2013$studlab)
#' Senn2013.5 <- subset(Senn2013, studlab %in% studies[1:5])
#'
#' net1 <- netmeta(TE, seTE, treat1.long, treat2.long,
#' studlab, data = Senn2013.5, common = FALSE, reference = "plac")
#' #
#' ns1 <- netsplit(net1)
#'
#' # Forest plot showing comparisons contributing both direct and
#' # indirect evidence
#' #
#' forest(ns1, fontsize = 6, spacing = 0.5, addrow.subgroups = FALSE)
#'
#' \dontrun{
#' # Forest plot showing comparisons contributing direct evidence
#' #
#' forest(ns1, fontsize = 6, spacing = 0.5, addrow.subgroups = FALSE,
#' show = "with.direct")
#'
#'
#' # Forest plot only showing network estimates compared to reference
#' # group and prediction intervals
#' #
#' forest(ns1, fontsize = 8, spacing = 0.75, show = "all",
#' only.reference = TRUE, prediction = TRUE,
#' direct = FALSE, indirect = FALSE)
#' }
#'
#' @method forest netsplit
#' @export
forest.netsplit <- function(x,
pooled = ifelse(x$x$random, "random", "common"),
show = "both",
##
subgroup = "comparison",
##
overall = TRUE,
direct = TRUE,
indirect = TRUE,
prediction = x$prediction,
##
only.reference = FALSE,
##
sortvar = NULL,
subset = NULL,
##
text.overall = "Network estimate",
text.direct = "Direct estimate",
text.indirect = "Indirect estimate",
text.predict = "Prediction interval",
##
type.overall,
type.direct,
type.indirect,
##
col.square = "gray",
col.square.lines = col.square,
col.inside = "white",
col.diamond = "gray",
col.diamond.lines = "black",
col.predict = "red",
col.predict.lines = "black",
##
equal.size = TRUE,
##
leftcols,
leftlabs,
rightcols = c("effect", "ci"),
rightlabs = NULL,
##
digits = gs("digits.forest"),
digits.prop = max(gs("digits.pval") - 2, 2),
##
backtransf = x$backtransf,
lab.NA = "",
smlab,
...) {
##
##
## (1) Check and set arguments
##
##
chkclass(x, "netsplit")
x <- updateversion(x)
##
pooled <- setchar(pooled, c("common", "random", "fixed"))
pooled[pooled == "fixed"] <- "common"
##
subgroup <- setchar(subgroup, c("comparison", "estimate"))
##
chklogical(overall)
chklogical(direct)
chklogical(indirect)
chklogical(prediction)
##
missing.only.reference <- missing(only.reference)
if (!missing.only.reference)
chklogical(only.reference)
#
# Catch sortvar and subset from data:
#
mc <- match.call()
sfsp <- sys.frame(sys.parent())
#
error <- try(sortvar.x <- catch("sortvar", mc, x, sfsp), silent = TRUE)
if (!any(class(error) == "try-error"))
sortvar <- sortvar.x
##
if (!is.null(sortvar)) {
if (length(dim(sortvar)) == 2) {
if (dim(sortvar)[1] != length(x$comparison))
stop("Argument 'sortvar' must be of length ",
length(x$comparison), ".",
call. = FALSE)
##
## Set proportions to 0 or 1
##
if (is.numeric(sortvar)) {
sortvar[is.zero(abs(sortvar), n = 1000)] <- 0
sortvar[is.zero(1 - abs(sortvar), n = 1000)] <-
1 * sign(sortvar)[is.zero(1 - abs(sortvar), n = 1000)]
}
sortvar <- order(do.call(order, as.list(as.data.frame(sortvar))))
}
else
chklength(sortvar, length(x$comparison),
text = paste0("Argument 'sortvar' must be of length ",
length(x$comparison), "."))
##
if (!is.numeric(sortvar))
sortvar <- setchar(sortvar, x$comparison)
}
#
error <- try(subset.x <- catch("subset", mc, x, sfsp), silent = TRUE)
if (!any(class(error) == "try-error"))
subset <- subset.x
##
if (!is.null(subset)) {
chklength(subset, length(x$comparison),
text = paste0("Argument 'subset' must be of length ",
length(x$comparison), "."))
if (!is.logical(subset))
stop("Argument 'subset' must be a logical vector.")
}
##
chkchar(text.overall)
chkchar(text.direct)
chkchar(text.indirect)
chkchar(text.predict)
##
missing.type.overall <- missing(type.overall)
if (missing.type.overall)
type.overall <- "diamond"
else
type.overall <- setchar(type.overall, c("diamond", "square"))
##
if (missing(type.direct))
type.direct <- "square"
else
type.direct <- setchar(type.direct, c("diamond", "square"))
if (missing(type.indirect))
type.indirect <- "square"
else
type.indirect <- setchar(type.indirect, c("diamond", "square"))
##
chkchar(col.square)
chkchar(col.square.lines)
chkchar(col.inside)
chkchar(col.diamond)
chkchar(col.diamond.lines)
chkchar(col.predict)
chkchar(col.predict.lines)
##
chklogical(equal.size)
##
chknumeric(digits, min = 0, length = 1)
chknumeric(digits.prop, min = 0, length = 1)
chklogical(backtransf)
##
chkchar(lab.NA)
##
if (pooled == "common") {
if (!(missing(prediction)) & prediction)
warning("Prediction intervals not shown for estimates ",
"from common effects model.")
prediction <- FALSE
}
##
if (!any(c(overall, direct, indirect)))
stop("At least, one of the following estimates ",
"must be included in forest plot:\n",
"- network estimates (argument 'overall')\n",
"- direct estimates (argument 'direct')\n",
"- indirect estimates (argument 'indirect')")
##
missing.leftcols <- missing(leftcols)
if (missing.leftcols)
if (direct)
leftcols <- c("studlab", "k", "prop", "I2")
else
leftcols <- "studlab"
##
missing.leftlabs <- missing(leftlabs)
if (missing.leftlabs) {
leftlabs <- rep(NA, length(leftcols))
leftlabs[leftcols == "studlab"] <- "Comparison"
leftlabs[leftcols == "k"] <- "Number of\nStudies"
leftlabs[leftcols == "n"] <- "Number of\nParticipants"
leftlabs[leftcols == "prop"] <- "Direct\nEvidence"
leftlabs[leftcols == "I2"] <- "I2"
leftlabs[leftcols == "tau2"] <- "tau2"
leftlabs[leftcols == "tau"] <- "tau"
leftlabs[leftcols == "Q"] <- "Q"
}
##
n.subgroup <- direct + indirect + overall + prediction
missing.smlab <- missing(smlab)
##
if (n.subgroup == 1 & overall & missing.type.overall)
type.overall <- "square"
##
if (missing(text.predict))
if (!(length(x$level.predict) == 0) &&
x$level.ma != x$level.predict)
text.predict <- paste0(text.predict, " (",
round(x$level.predict * 100), "%-PI)")
##
if (overall & n.subgroup > 1) {
if (text.overall == text.predict)
stop("Text must be different for arguments 'text.overall' and ",
"'text.predict'.")
if (text.overall == text.direct)
stop("Text must be different for arguments 'text.overall' and ",
"'text.direct'.")
if (text.overall == text.indirect)
stop("Text must be different for arguments 'text.overall' and ",
"'text.indirect'.")
}
##
if (prediction & n.subgroup > 1) {
if (text.predict == text.overall)
stop("Text must be different for arguments 'text.predict' and ",
"'text.overall'.")
if (text.predict == text.direct)
stop("Text must be different for arguments 'text.predict' and ",
"'text.direct'.")
if (text.predict == text.indirect)
stop("Text must be different for arguments 'text.predict' and ",
"'text.indirect'.")
}
##
## Check for deprecated arguments in '...'
##
args <- list(...)
## Check whether first argument is a list. In this case only use
## this list as input.
if (length(args) > 0 && is.list(args[[1]]))
args <- args[[1]]
##
additional.arguments <- names(args)
##
if (length(additional.arguments) > 0) {
if (!is.na(charmatch("showa", additional.arguments)))
if (!missing(show))
warning("Deprecated argument 'showall' ignored as ",
"argument 'show' is also provided.")
else {
warning("Deprecated argument 'showall' has been replaced by ",
"argument 'show'.")
show <- args[[charmatch("showa", additional.arguments)]]
if (show)
show <- "all"
else
show <- "both"
}
}
##
show <- setchar(show, c("all", "both", "with.direct",
"direct.only", "indirect.only",
"reference.only"))
##
if (show == "reference.only") {
warning("Argument 'show = \"reference.only\" replaced with ",
"'only.reference = TRUE'.",
call. = FALSE)
show <- "both"
if (missing.only.reference)
only.reference <- TRUE
}
##
##
## (2) Extract results for common and random effects model
##
##
if (pooled == "common") {
dat.direct <- x$direct.common
##
dat.indirect <- x$indirect.common
dat.indirect$Q <- dat.indirect$tau2 <-
dat.indirect$tau <- dat.indirect$I2 <- NA
##
dat.overall <- x$common
dat.overall$Q <- dat.overall$tau2 <-
dat.overall$tau <- dat.overall$I2 <- NA
##
dat.direct$prop <- formatPT(x$prop.common, digits = digits.prop)
dat.indirect$prop <- NA
dat.overall$prop <- NA
##
if (missing.smlab)
smlab <- "Common Effects Model"
}
else {
dat.direct <- x$direct.random
##
dat.indirect <- x$indirect.random
dat.indirect$Q <- dat.indirect$tau2 <-
dat.indirect$tau <- dat.indirect$I2 <- NA
##
dat.overall <- x$random
dat.overall$Q <- dat.overall$tau2 <-
dat.overall$tau <- dat.overall$I2 <- NA
##
dat.direct$prop <- formatPT(x$prop.random, digits = digits.prop)
dat.indirect$prop <- NA
dat.overall$prop <- NA
##
if (missing.smlab)
smlab <- "Random Effects Model"
}
##
if (missing.smlab & n.subgroup == 1)
smlab <- paste0(if (direct)
paste0(text.direct, "\n"),
if (indirect)
paste0(text.indirect, "\n"),
if (overall)
paste0(text.overall, "\n"),
"(",
tolower(smlab),
")")
##
dat.predict <- x$predict
##
if ( (subgroup == "estimate") | (prediction & !overall) )
dat.predict$TE <- dat.overall$TE
else
dat.predict$TE <- NA
##
dat.predict$seTE <- dat.predict$statistic <- dat.predict$p <-
dat.predict$Q <- NA
##
dat.predict <- dat.predict[, c("comparison", "TE", "seTE",
"lower", "upper", "statistic", "p", "Q")]
dat.predict$tau2 <- NA
dat.predict$tau <- NA
dat.predict$I2 <- NA
dat.predict$prop <- NA
##
dat.direct$comps <- dat.indirect$comps <-
dat.overall$comps <- dat.predict$comps <- x$comparison
##
dat.direct$k <- x$k
dat.indirect$k <- dat.overall$k <- dat.predict$k <- NA
##
dat.indirect$n <- dat.overall$n <- dat.predict$n <- NA
##
dat.direct$evidence <- text.direct
dat.indirect$evidence <- text.indirect
dat.overall$evidence <- text.overall
dat.predict$evidence <- text.predict
##
dat.direct$type.study <- type.direct
dat.indirect$type.study <- type.indirect
dat.overall$type.study <- type.overall
dat.predict$type.study <- "predict"
##
dat.direct$col.estimate <- if (type.direct == "square")
col.square
else
col.diamond
dat.indirect$col.estimate <- if (type.indirect == "square")
col.square
else
col.diamond
dat.overall$col.estimate <- if (type.overall == "square")
col.square
else
col.diamond
##
dat.direct$col.lines <- if (type.direct == "square")
col.square.lines
else
col.diamond.lines
dat.indirect$col.lines <- if (type.indirect == "square")
col.square.lines
else
col.diamond
dat.overall$col.lines <- if (type.overall == "square")
col.square.lines
else
col.diamond.lines
##
dat.predict$col.estimate <- col.predict
dat.predict$col.lines <- col.predict.lines
##
## col.square.lines = col.square,
## col.inside = "white",
## col.diamond.lines = "black",
## col.predict.lines = "black",
##
##
## (3) Select treatment comparisons to show in forest plot
##
##
if (show == "all")
sel <- rep_len(TRUE, length(x$direct.common$TE))
else if (show == "with.direct")
sel <- (!is.na(x$direct.common$TE) & !is.na(x$direct.random$TE))
else if (show == "both")
sel <- (!is.na(x$direct.common$TE) & !is.na(x$indirect.common$TE) &
!is.na(x$direct.random$TE) & !is.na(x$indirect.random$TE))
else if (show == "direct.only")
sel <- (!is.na(x$direct.common$TE) & is.na(x$indirect.common$TE) &
!is.na(x$direct.random$TE) & is.na(x$indirect.random$TE))
else if (show == "indirect.only")
sel <- (is.na(x$direct.common$TE) & !is.na(x$indirect.common$TE) &
is.na(x$direct.random$TE) & !is.na(x$indirect.random$TE))
##
if (only.reference) {
if (x$reference.group == "") {
warning("First treatment used as reference as argument ",
"'reference.group' was unspecified in netsplit().",
call. = FALSE)
x$reference.group <-
compsplit(x$comparison, x$sep.trts)[[1]][1]
}
##
sel.ref <-
apply(!is.na(sapply(compsplit(x$comparison, x$sep.trts),
match, x$reference.group)), 2, sum) >= 1
##
sel <- sel & sel.ref
}
##
dat.direct <- dat.direct[sel, ]
dat.indirect <- dat.indirect[sel, ]
dat.overall <- dat.overall[sel, ]
dat.predict <- dat.predict[sel, ]
#
if (!is.null(sortvar))
sortvar <- sortvar[sel]
#
if (!is.null(subset))
subset <- subset[sel]
#
if (!is.null(sortvar)) {
o <- order(sortvar)
##
dat.direct <- dat.direct[o, ]
dat.indirect <- dat.indirect[o, ]
dat.overall <- dat.overall[o, ]
dat.predict <- dat.predict[o, ]
#
if (!is.null(subset))
subset <- subset[o]
}
#
if (!is.null(subset)) {
dat.direct <- dat.direct[subset, ]
dat.indirect <- subset(dat.indirect, subset)
dat.overall <- subset(dat.overall, subset)
dat.predict <- subset(dat.predict, subset)
}
##
if (direct & all(is.na(dat.direct$I2)) & missing.leftcols) {
leftlabs <- leftlabs[leftcols != "I2"]
leftcols <- leftcols[leftcols != "I2"]
}
##
##
## (4) Forest plot
##
##
if (subgroup == "comparison") {
dat <- rbind(
if (direct) dat.direct,
if (indirect) dat.indirect,
if (overall) dat.overall,
if (prediction) dat.predict
)
##
if (nrow(dat) == 0) {
warning("No comparison(s) selected. Consider using argument ",
"'show = \"all\"'.")
return(invisible(NULL))
}
##
if (n.subgroup > 1)
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$evidence, data = dat,
sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = "",
subgroup = dat$comps,
print.subgroup.name = FALSE))
else
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$comps, data = dat, sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = ""))
##
if (overall) {
m$w.common[m$studlab == text.overall] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$studlab == text.overall] <- max(m$w.random, na.rm = TRUE)
}
##
if (prediction) {
m$lower[m$studlab == text.predict] <- dat.predict$lower
m$upper[m$studlab == text.predict] <- dat.predict$upper
##
m$w.common[m$studlab == text.predict] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$studlab == text.predict] <- max(m$w.random, na.rm = TRUE)
}
##
forest(m,
digits = digits,
common = FALSE, random = FALSE,
hetstat = FALSE, test.subgroup = FALSE,
leftcols = leftcols,
leftlabs = leftlabs,
rightcols = rightcols,
rightlabs = rightlabs,
lab.NA = lab.NA,
smlab = smlab,
backtransf = backtransf,
type.study = dat$type.study,
col.square = dat$col.estimate,
col.square.lines = dat$col.lines,
weight.study = if (equal.size) "same" else "common",
...)
}
else {
dat <- rbind(
if (direct) dat.direct,
if (indirect) dat.indirect,
if (overall) dat.overall,
if (prediction) dat.predict
)
##
if (nrow(dat) == 0) {
warning("No comparison(s) selected. Consider using argument ",
"'show = \"all\"'.")
return(invisible(NULL))
}
##
if (n.subgroup > 1)
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$comps, data = dat,
sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = "",
subgroup = dat$evidence,
print.subgroup.name = FALSE))
else
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$comps, data = dat, sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = ""))
##
if (overall) {
m$w.common[m$subgroup == text.overall] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$subgroup == text.overall] <- max(m$w.random, na.rm = TRUE)
}
##
if (prediction) {
m$lower[m$subgroup == text.predict] <- dat.predict$lower
m$upper[m$subgroup == text.predict] <- dat.predict$upper
##
m$w.common[m$subgroup == text.predict] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$subgroup == text.predict] <- max(m$w.random, na.rm = TRUE)
}
##
forest(m,
digits = digits,
##
overall = FALSE, common = FALSE, random = FALSE,
hetstat = FALSE, test.subgroup = FALSE,
##
subgroup.hetstat = FALSE,
prediction.subgroup = FALSE,
##
leftcols = leftcols,
leftlabs = leftlabs,
rightcols = rightcols,
rightlabs = rightlabs,
##
lab.NA = lab.NA,
smlab = smlab,
backtransf = backtransf,
##
type.study = dat$type.study,
col.square = dat$col.estimate,
col.square.lines = dat$col.lines,
##
weight.study = if (equal.size) "same" else "common",
...)
}
res <- list(dat = dat,
leftcols = leftcols,
leftlabs = leftlabs,
rightcols = rightcols,
rightlabs = rightlabs,
lab.NA = lab.NA,
backtransf = backtransf,
smlab = smlab,
weight.study = if (equal.size) "same" else "common",
##
args = list(
pooled = pooled,
show = show,
subgroup = subgroup,
overall = overall,
direct = direct,
indirect = indirect,
prediction = prediction,
only.reference = only.reference,
sortvar = sortvar,
subset = subset,
text.overall = text.overall,
text.direct = text.direct,
text.indirect = text.indirect,
text.predict = text.predict,
type.overall = type.overall,
type.direct = type.direct,
type.indirect = type.indirect,
col.square = col.square,
col.square.lines = col.square.lines,
col.inside = col.inside,
col.diamond = col.diamond,
col.diamond.lines = col.diamond.lines,
col.predict = col.predict,
col.predict.lines = col.predict.lines,
equal.size = equal.size,
digits = digits,
digits.prop = digits.prop)
)
invisible(res)
}
#' @rdname forest.netsplit
#' @method plot netsplit
#' @export
#'
plot.netsplit <- function(x, ...)
forest(x, ...)
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Defines functions plot.netsplit forest.netsplit
Documented in forest.netsplit plot.netsplit
#' Forest plot for direct and indirect evidence
#'
#' @description
#' Forest plot to show direct and indirect evidence in network
#' meta-analysis. Furthermore, estimates from network meta-analysis
#' as well as prediction intervals can be printed.
#'
#' @aliases forest.netsplit plot.netsplit
#'
#' @param x An object of class \code{netsplit}.
#' @param pooled A character string indicating whether results for the
#' common (\code{"common"}) or random effects model (\code{"random"})
#' should be plotted. Can be abbreviated.
#' @param show A character string indicating which comparisons should
#' be printed (see Details).
#' @param overall A logical indicating whether network meta-analysis
#' estimates should be printed.
#' @param direct A logical indicating whether direct estimates should
#' be printed.
#' @param indirect A logical indicating whether indirect estimates
#' should be printed.
#' @param prediction A logical indicating whether prediction intervals
#' should be printed.
#' @param only.reference A logical indicating whether only comparisons
#' with the reference group should be printed.
#' @param sortvar An optional vector used to sort comparisons (must be
#' of same length as the total number of comparisons).
#' @param subset An optional logical vector specifying a subset of
#' comparisons to print (must be of same length as the total number of
#' comparisons) .
#' @param subgroup A character string indicating which layout should
#' be used in forest plot: subgroups by comparisons
#' (\code{"comparison"}) or subgroups by estimates
#' (\code{"estimate"}). Can be abbreviated.
#' @param text.overall A character string used in the plot to label
#' the network estimates.
#' @param text.direct A character string used in the plot to label the
#' direct estimates.
#' @param text.indirect A character string used in the plot to label
#' the indirect estimates.
#' @param text.predict A character string used in the plot to label
#' the prediction interval.
#' @param type.overall A character string specifying how to plot
#' treatment effects and confidence intervals for the overall
#' network evidence.
#' @param type.direct A character string specifying how to plot
#' treatment effects and confidence intervals for the direct
#' evidence.
#' @param type.indirect A character string specifying how to plot
#' treatment effects and confidence intervals for the indirect
#' evidence.
#' @param col.square The colour for squares.
#' @param col.square.lines The colour for the outer lines of squares.
#' @param col.inside The colour for results and confidence limits if
#' confidence limits are completely within squares squares.
#' @param col.diamond The colour of diamonds.
#' @param col.diamond.lines The colour of the outer lines of diamonds.
#' @param col.predict Background colour of prediction intervals.
#' @param col.predict.lines Colour of outer lines of prediction
#' intervals.
#' @param equal.size A logical indicating whether all squares should
#' be of equal size. Otherwise, the square size is proportional to
#' the precision of estimates.
#' @param leftcols A character vector specifying columns to be plotted
#' on the left side of the forest plot (see Details).
#' @param leftlabs A character vector specifying labels for columns on
#' left side of the forest plot.
#' @param rightcols A character vector specifying columns to be
#' plotted on the right side of the forest plot (see Details).
#' @param rightlabs A character vector specifying labels for columns
#' on right side of the forest plot.
#' @param digits Minimal number of significant digits for treatment
#' effects and confidence intervals, see \code{print.default}.
#' @param digits.prop Minimal number of significant digits for the
#' direct evidence proportion.
#' @param backtransf A logical indicating whether results should be
#' back transformed in forest plots. If \code{backtransf = TRUE},
#' results for \code{sm = "OR"} are presented as odds ratios rather
#' than log odds ratios, for example.
#' @param lab.NA A character string to label missing values.
#' @param smlab A label printed at top of figure. By default, text
#' indicating either common or random effects model is printed.
#' @param \dots Additional arguments for \code{\link{forest.meta}}
#' function.
#'
#' @details
#' A forest plot, also called confidence interval plot, is drawn in
#' the active graphics window.
#'
#' The arguments \code{leftcols} and \code{rightcols} can be used to
#' specify columns which are plotted on the left and right side of the
#' forest plot, respectively. If argument \code{rightcols} is
#' \code{FALSE}, no columns will be plotted on the right side.
#'
#' If direct estimates are included in the forest plot (\code{direct =
#' TRUE}, default), the following columns will be printed on the left
#' side of the forest plot: the comparisons (column \code{"studlab"}
#' in \code{\link{forest.meta}}), number of pairwise comparisons
#' (\code{"k"}), direct evidence proportion (\code{"k"}), and
#' I\eqn{^2} from pairwise comparison (\code{"I2"}).
#'
#' If direct estimates are not included in the forest plot
#' (\code{direct = FALSE}), only the comparisons (\code{"studlab"})
#' are printed on the left side of the forest plot.
#'
#' For more information see help page of \code{\link{forest.meta}}
#' function.
#'
#' Argument \code{show} determines which comparisons are printed:
#' \tabular{ll}{
#' \dQuote{all} \tab All comparisons \cr
#' \dQuote{both} \tab Only comparisons contributing both direct and
#' indirect evidence \cr
#' \dQuote{with.direct} \tab Comparisons providing direct evidence \cr
#' \dQuote{direct.only} \tab Comparisons providing only direct
#' evidence \cr
#' \dQuote{indirect.only} \tab Comparisons providing only indirect
#' evidence
#' }
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{forest.meta}}
#'
#' @keywords hplot
#'
#' @examples
#' data(Senn2013)
#'
#' # Only consider first five studies (to reduce runtime of example)
#' #
#' studies <- unique(Senn2013$studlab)
#' Senn2013.5 <- subset(Senn2013, studlab %in% studies[1:5])
#'
#' net1 <- netmeta(TE, seTE, treat1.long, treat2.long,
#' studlab, data = Senn2013.5, common = FALSE, reference = "plac")
#' #
#' ns1 <- netsplit(net1)
#'
#' # Forest plot showing comparisons contributing both direct and
#' # indirect evidence
#' #
#' forest(ns1, fontsize = 6, spacing = 0.5, addrow.subgroups = FALSE)
#'
#' \dontrun{
#' # Forest plot showing comparisons contributing direct evidence
#' #
#' forest(ns1, fontsize = 6, spacing = 0.5, addrow.subgroups = FALSE,
#' show = "with.direct")
#'
#'
#' # Forest plot only showing network estimates compared to reference
#' # group and prediction intervals
#' #
#' forest(ns1, fontsize = 8, spacing = 0.75, show = "all",
#' only.reference = TRUE, prediction = TRUE,
#' direct = FALSE, indirect = FALSE)
#' }
#'
#' @method forest netsplit
#' @export
forest.netsplit <- function(x,
pooled = ifelse(x$x$random, "random", "common"),
show = "both",
##
subgroup = "comparison",
##
overall = TRUE,
direct = TRUE,
indirect = TRUE,
prediction = x$prediction,
##
only.reference = FALSE,
##
sortvar = NULL,
subset = NULL,
##
text.overall = "Network estimate",
text.direct = "Direct estimate",
text.indirect = "Indirect estimate",
text.predict = "Prediction interval",
##
type.overall,
type.direct,
type.indirect,
##
col.square = "gray",
col.square.lines = col.square,
col.inside = "white",
col.diamond = "gray",
col.diamond.lines = "black",
col.predict = "red",
col.predict.lines = "black",
##
equal.size = TRUE,
##
leftcols,
leftlabs,
rightcols = c("effect", "ci"),
rightlabs = NULL,
##
digits = gs("digits.forest"),
digits.prop = max(gs("digits.pval") - 2, 2),
##
backtransf = x$backtransf,
lab.NA = "",
smlab,
...) {
##
##
## (1) Check and set arguments
##
##
chkclass(x, "netsplit")
x <- updateversion(x)
##
pooled <- setchar(pooled, c("common", "random", "fixed"))
pooled[pooled == "fixed"] <- "common"
##
subgroup <- setchar(subgroup, c("comparison", "estimate"))
##
chklogical(overall)
chklogical(direct)
chklogical(indirect)
chklogical(prediction)
##
missing.only.reference <- missing(only.reference)
if (!missing.only.reference)
chklogical(only.reference)
#
# Catch sortvar and subset from data:
#
mc <- match.call()
sfsp <- sys.frame(sys.parent())
#
error <- try(sortvar.x <- catch("sortvar", mc, x, sfsp), silent = TRUE)
if (!any(class(error) == "try-error"))
sortvar <- sortvar.x
##
if (!is.null(sortvar)) {
if (length(dim(sortvar)) == 2) {
if (dim(sortvar)[1] != length(x$comparison))
stop("Argument 'sortvar' must be of length ",
length(x$comparison), ".",
call. = FALSE)
##
## Set proportions to 0 or 1
##
if (is.numeric(sortvar)) {
sortvar[is.zero(abs(sortvar), n = 1000)] <- 0
sortvar[is.zero(1 - abs(sortvar), n = 1000)] <-
1 * sign(sortvar)[is.zero(1 - abs(sortvar), n = 1000)]
}
sortvar <- order(do.call(order, as.list(as.data.frame(sortvar))))
}
else
chklength(sortvar, length(x$comparison),
text = paste0("Argument 'sortvar' must be of length ",
length(x$comparison), "."))
##
if (!is.numeric(sortvar))
sortvar <- setchar(sortvar, x$comparison)
}
#
error <- try(subset.x <- catch("subset", mc, x, sfsp), silent = TRUE)
if (!any(class(error) == "try-error"))
subset <- subset.x
##
if (!is.null(subset)) {
chklength(subset, length(x$comparison),
text = paste0("Argument 'subset' must be of length ",
length(x$comparison), "."))
if (!is.logical(subset))
stop("Argument 'subset' must be a logical vector.")
}
##
chkchar(text.overall)
chkchar(text.direct)
chkchar(text.indirect)
chkchar(text.predict)
##
missing.type.overall <- missing(type.overall)
if (missing.type.overall)
type.overall <- "diamond"
else
type.overall <- setchar(type.overall, c("diamond", "square"))
##
if (missing(type.direct))
type.direct <- "square"
else
type.direct <- setchar(type.direct, c("diamond", "square"))
if (missing(type.indirect))
type.indirect <- "square"
else
type.indirect <- setchar(type.indirect, c("diamond", "square"))
##
chkchar(col.square)
chkchar(col.square.lines)
chkchar(col.inside)
chkchar(col.diamond)
chkchar(col.diamond.lines)
chkchar(col.predict)
chkchar(col.predict.lines)
##
chklogical(equal.size)
##
chknumeric(digits, min = 0, length = 1)
chknumeric(digits.prop, min = 0, length = 1)
chklogical(backtransf)
##
chkchar(lab.NA)
##
if (pooled == "common") {
if (!(missing(prediction)) & prediction)
warning("Prediction intervals not shown for estimates ",
"from common effects model.")
prediction <- FALSE
}
##
if (!any(c(overall, direct, indirect)))
stop("At least, one of the following estimates ",
"must be included in forest plot:\n",
"- network estimates (argument 'overall')\n",
"- direct estimates (argument 'direct')\n",
"- indirect estimates (argument 'indirect')")
##
missing.leftcols <- missing(leftcols)
if (missing.leftcols)
if (direct)
leftcols <- c("studlab", "k", "prop", "I2")
else
leftcols <- "studlab"
##
missing.leftlabs <- missing(leftlabs)
if (missing.leftlabs) {
leftlabs <- rep(NA, length(leftcols))
leftlabs[leftcols == "studlab"] <- "Comparison"
leftlabs[leftcols == "k"] <- "Number of\nStudies"
leftlabs[leftcols == "n"] <- "Number of\nParticipants"
leftlabs[leftcols == "prop"] <- "Direct\nEvidence"
leftlabs[leftcols == "I2"] <- "I2"
leftlabs[leftcols == "tau2"] <- "tau2"
leftlabs[leftcols == "tau"] <- "tau"
leftlabs[leftcols == "Q"] <- "Q"
}
##
n.subgroup <- direct + indirect + overall + prediction
missing.smlab <- missing(smlab)
##
if (n.subgroup == 1 & overall & missing.type.overall)
type.overall <- "square"
##
if (missing(text.predict))
if (!(length(x$level.predict) == 0) &&
x$level.ma != x$level.predict)
text.predict <- paste0(text.predict, " (",
round(x$level.predict * 100), "%-PI)")
##
if (overall & n.subgroup > 1) {
if (text.overall == text.predict)
stop("Text must be different for arguments 'text.overall' and ",
"'text.predict'.")
if (text.overall == text.direct)
stop("Text must be different for arguments 'text.overall' and ",
"'text.direct'.")
if (text.overall == text.indirect)
stop("Text must be different for arguments 'text.overall' and ",
"'text.indirect'.")
}
##
if (prediction & n.subgroup > 1) {
if (text.predict == text.overall)
stop("Text must be different for arguments 'text.predict' and ",
"'text.overall'.")
if (text.predict == text.direct)
stop("Text must be different for arguments 'text.predict' and ",
"'text.direct'.")
if (text.predict == text.indirect)
stop("Text must be different for arguments 'text.predict' and ",
"'text.indirect'.")
}
##
## Check for deprecated arguments in '...'
##
args <- list(...)
## Check whether first argument is a list. In this case only use
## this list as input.
if (length(args) > 0 && is.list(args[[1]]))
args <- args[[1]]
##
additional.arguments <- names(args)
##
if (length(additional.arguments) > 0) {
if (!is.na(charmatch("showa", additional.arguments)))
if (!missing(show))
warning("Deprecated argument 'showall' ignored as ",
"argument 'show' is also provided.")
else {
warning("Deprecated argument 'showall' has been replaced by ",
"argument 'show'.")
show <- args[[charmatch("showa", additional.arguments)]]
if (show)
show <- "all"
else
show <- "both"
}
}
##
show <- setchar(show, c("all", "both", "with.direct",
"direct.only", "indirect.only",
"reference.only"))
##
if (show == "reference.only") {
warning("Argument 'show = \"reference.only\" replaced with ",
"'only.reference = TRUE'.",
call. = FALSE)
show <- "both"
if (missing.only.reference)
only.reference <- TRUE
}
##
##
## (2) Extract results for common and random effects model
##
##
if (pooled == "common") {
dat.direct <- x$direct.common
##
dat.indirect <- x$indirect.common
dat.indirect$Q <- dat.indirect$tau2 <-
dat.indirect$tau <- dat.indirect$I2 <- NA
##
dat.overall <- x$common
dat.overall$Q <- dat.overall$tau2 <-
dat.overall$tau <- dat.overall$I2 <- NA
##
dat.direct$prop <- formatPT(x$prop.common, digits = digits.prop)
dat.indirect$prop <- NA
dat.overall$prop <- NA
##
if (missing.smlab)
smlab <- "Common Effects Model"
}
else {
dat.direct <- x$direct.random
##
dat.indirect <- x$indirect.random
dat.indirect$Q <- dat.indirect$tau2 <-
dat.indirect$tau <- dat.indirect$I2 <- NA
##
dat.overall <- x$random
dat.overall$Q <- dat.overall$tau2 <-
dat.overall$tau <- dat.overall$I2 <- NA
##
dat.direct$prop <- formatPT(x$prop.random, digits = digits.prop)
dat.indirect$prop <- NA
dat.overall$prop <- NA
##
if (missing.smlab)
smlab <- "Random Effects Model"
}
##
if (missing.smlab & n.subgroup == 1)
smlab <- paste0(if (direct)
paste0(text.direct, "\n"),
if (indirect)
paste0(text.indirect, "\n"),
if (overall)
paste0(text.overall, "\n"),
"(",
tolower(smlab),
")")
##
dat.predict <- x$predict
##
if ( (subgroup == "estimate") | (prediction & !overall) )
dat.predict$TE <- dat.overall$TE
else
dat.predict$TE <- NA
##
dat.predict$seTE <- dat.predict$statistic <- dat.predict$p <-
dat.predict$Q <- NA
##
dat.predict <- dat.predict[, c("comparison", "TE", "seTE",
"lower", "upper", "statistic", "p", "Q")]
dat.predict$tau2 <- NA
dat.predict$tau <- NA
dat.predict$I2 <- NA
dat.predict$prop <- NA
##
dat.direct$comps <- dat.indirect$comps <-
dat.overall$comps <- dat.predict$comps <- x$comparison
##
dat.direct$k <- x$k
dat.indirect$k <- dat.overall$k <- dat.predict$k <- NA
##
dat.indirect$n <- dat.overall$n <- dat.predict$n <- NA
##
dat.direct$evidence <- text.direct
dat.indirect$evidence <- text.indirect
dat.overall$evidence <- text.overall
dat.predict$evidence <- text.predict
##
dat.direct$type.study <- type.direct
dat.indirect$type.study <- type.indirect
dat.overall$type.study <- type.overall
dat.predict$type.study <- "predict"
##
dat.direct$col.estimate <- if (type.direct == "square")
col.square
else
col.diamond
dat.indirect$col.estimate <- if (type.indirect == "square")
col.square
else
col.diamond
dat.overall$col.estimate <- if (type.overall == "square")
col.square
else
col.diamond
##
dat.direct$col.lines <- if (type.direct == "square")
col.square.lines
else
col.diamond.lines
dat.indirect$col.lines <- if (type.indirect == "square")
col.square.lines
else
col.diamond
dat.overall$col.lines <- if (type.overall == "square")
col.square.lines
else
col.diamond.lines
##
dat.predict$col.estimate <- col.predict
dat.predict$col.lines <- col.predict.lines
##
## col.square.lines = col.square,
## col.inside = "white",
## col.diamond.lines = "black",
## col.predict.lines = "black",
##
##
## (3) Select treatment comparisons to show in forest plot
##
##
if (show == "all")
sel <- rep_len(TRUE, length(x$direct.common$TE))
else if (show == "with.direct")
sel <- (!is.na(x$direct.common$TE) & !is.na(x$direct.random$TE))
else if (show == "both")
sel <- (!is.na(x$direct.common$TE) & !is.na(x$indirect.common$TE) &
!is.na(x$direct.random$TE) & !is.na(x$indirect.random$TE))
else if (show == "direct.only")
sel <- (!is.na(x$direct.common$TE) & is.na(x$indirect.common$TE) &
!is.na(x$direct.random$TE) & is.na(x$indirect.random$TE))
else if (show == "indirect.only")
sel <- (is.na(x$direct.common$TE) & !is.na(x$indirect.common$TE) &
is.na(x$direct.random$TE) & !is.na(x$indirect.random$TE))
##
if (only.reference) {
if (x$reference.group == "") {
warning("First treatment used as reference as argument ",
"'reference.group' was unspecified in netsplit().",
call. = FALSE)
x$reference.group <-
compsplit(x$comparison, x$sep.trts)[[1]][1]
}
##
sel.ref <-
apply(!is.na(sapply(compsplit(x$comparison, x$sep.trts),
match, x$reference.group)), 2, sum) >= 1
##
sel <- sel & sel.ref
}
##
dat.direct <- dat.direct[sel, ]
dat.indirect <- dat.indirect[sel, ]
dat.overall <- dat.overall[sel, ]
dat.predict <- dat.predict[sel, ]
#
if (!is.null(sortvar))
sortvar <- sortvar[sel]
#
if (!is.null(subset))
subset <- subset[sel]
#
if (!is.null(sortvar)) {
o <- order(sortvar)
##
dat.direct <- dat.direct[o, ]
dat.indirect <- dat.indirect[o, ]
dat.overall <- dat.overall[o, ]
dat.predict <- dat.predict[o, ]
#
if (!is.null(subset))
subset <- subset[o]
}
#
if (!is.null(subset)) {
dat.direct <- dat.direct[subset, ]
dat.indirect <- subset(dat.indirect, subset)
dat.overall <- subset(dat.overall, subset)
dat.predict <- subset(dat.predict, subset)
}
##
if (direct & all(is.na(dat.direct$I2)) & missing.leftcols) {
leftlabs <- leftlabs[leftcols != "I2"]
leftcols <- leftcols[leftcols != "I2"]
}
##
##
## (4) Forest plot
##
##
if (subgroup == "comparison") {
dat <- rbind(
if (direct) dat.direct,
if (indirect) dat.indirect,
if (overall) dat.overall,
if (prediction) dat.predict
)
##
if (nrow(dat) == 0) {
warning("No comparison(s) selected. Consider using argument ",
"'show = \"all\"'.")
return(invisible(NULL))
}
##
if (n.subgroup > 1)
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$evidence, data = dat,
sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = "",
subgroup = dat$comps,
print.subgroup.name = FALSE))
else
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$comps, data = dat, sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = ""))
##
if (overall) {
m$w.common[m$studlab == text.overall] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$studlab == text.overall] <- max(m$w.random, na.rm = TRUE)
}
##
if (prediction) {
m$lower[m$studlab == text.predict] <- dat.predict$lower
m$upper[m$studlab == text.predict] <- dat.predict$upper
##
m$w.common[m$studlab == text.predict] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$studlab == text.predict] <- max(m$w.random, na.rm = TRUE)
}
##
forest(m,
digits = digits,
common = FALSE, random = FALSE,
hetstat = FALSE, test.subgroup = FALSE,
leftcols = leftcols,
leftlabs = leftlabs,
rightcols = rightcols,
rightlabs = rightlabs,
lab.NA = lab.NA,
smlab = smlab,
backtransf = backtransf,
type.study = dat$type.study,
col.square = dat$col.estimate,
col.square.lines = dat$col.lines,
weight.study = if (equal.size) "same" else "common",
...)
}
else {
dat <- rbind(
if (direct) dat.direct,
if (indirect) dat.indirect,
if (overall) dat.overall,
if (prediction) dat.predict
)
##
if (nrow(dat) == 0) {
warning("No comparison(s) selected. Consider using argument ",
"'show = \"all\"'.")
return(invisible(NULL))
}
##
if (n.subgroup > 1)
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$comps, data = dat,
sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = "",
subgroup = dat$evidence,
print.subgroup.name = FALSE))
else
m <-
suppressWarnings(metagen(dat$TE, dat$seTE,
studlab = dat$comps, data = dat, sm = x$sm,
common = FALSE, random = FALSE,
method.tau = "DL", method.tau.ci = ""))
##
if (overall) {
m$w.common[m$subgroup == text.overall] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$subgroup == text.overall] <- max(m$w.random, na.rm = TRUE)
}
##
if (prediction) {
m$lower[m$subgroup == text.predict] <- dat.predict$lower
m$upper[m$subgroup == text.predict] <- dat.predict$upper
##
m$w.common[m$subgroup == text.predict] <- max(m$w.common, na.rm = TRUE)
m$w.random[m$subgroup == text.predict] <- max(m$w.random, na.rm = TRUE)
}
##
forest(m,
digits = digits,
##
overall = FALSE, common = FALSE, random = FALSE,
hetstat = FALSE, test.subgroup = FALSE,
##
subgroup.hetstat = FALSE,
prediction.subgroup = FALSE,
##
leftcols = leftcols,
leftlabs = leftlabs,
rightcols = rightcols,
rightlabs = rightlabs,
##
lab.NA = lab.NA,
smlab = smlab,
backtransf = backtransf,
##
type.study = dat$type.study,
col.square = dat$col.estimate,
col.square.lines = dat$col.lines,
##
weight.study = if (equal.size) "same" else "common",
...)
}
res <- list(dat = dat,
leftcols = leftcols,
leftlabs = leftlabs,
rightcols = rightcols,
rightlabs = rightlabs,
lab.NA = lab.NA,
backtransf = backtransf,
smlab = smlab,
weight.study = if (equal.size) "same" else "common",
##
args = list(
pooled = pooled,
show = show,
subgroup = subgroup,
overall = overall,
direct = direct,
indirect = indirect,
prediction = prediction,
only.reference = only.reference,
sortvar = sortvar,
subset = subset,
text.overall = text.overall,
text.direct = text.direct,
text.indirect = text.indirect,
text.predict = text.predict,
type.overall = type.overall,
type.direct = type.direct,
type.indirect = type.indirect,
col.square = col.square,
col.square.lines = col.square.lines,
col.inside = col.inside,
col.diamond = col.diamond,
col.diamond.lines = col.diamond.lines,
col.predict = col.predict,
col.predict.lines = col.predict.lines,
equal.size = equal.size,
digits = digits,
digits.prop = digits.prop)
)
invisible(res)
}
#' @rdname forest.netsplit
#' @method plot netsplit
#' @export
#'
plot.netsplit <- function(x, ...)
forest(x, ...)
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