From 3c1be560553de31d6b5e0ba2c78d87ca11315ece Mon Sep 17 00:00:00 2001
From: Christian Roever <christian.roever@med.uni-goettingen.de>
Date: Fri, 14 Apr 2023 12:43:04 +0200
Subject: [PATCH] extended 'traceplot()' functionality
---
DESCRIPTION | 2 +-
NAMESPACE | 10 +-
R/bayesmeta.R | 131 +++++++++++++++++-------
R/bmr.R | 203 +++++++++++++++++++++++++++++++------
man/bayesmeta-package.Rd | 2 +-
man/traceplot.bayesmeta.Rd | 59 +++++++++--
6 files changed, 324 insertions(+), 83 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 71c1aed..dd4b7eb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: bayesmeta
Type: Package
Title: Bayesian Random-Effects Meta-Analysis and Meta-Regression
Version: 3.3
-Date: 2023-03-16
+Date: 2023-04-14
Authors@R: c(person(given="Christian", family="Roever", role=c("aut","cre"),
email="christian.roever@med.uni-goettingen.de",
comment=c(ORCID="0000-0002-6911-698X")),
diff --git a/NAMESPACE b/NAMESPACE
index a51e4ca..80f3794 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -50,11 +50,11 @@ importFrom("graphics",
"lines", "matlines", "mtext", "par", "plot", "points", "polygon",
"text")
importFrom("stats",
- "dcauchy", "dlnorm", "dlogis", "dnorm", "dt", "integrate", "median",
- "optim", "optimize", "pcauchy", "plnorm", "plogis", "pnorm",
- "pt", "qcauchy", "qchisq", "qlnorm", "qlogis", "qnorm", "qt",
- "rcauchy", "rexp", "rlogis", "rmultinom", "rnorm", "rt", "runif",
- "uniroot")
+ "dcauchy", "dlnorm", "dlogis", "dnorm", "dt", "integrate", "lm.fit",
+ "median", "optim", "optimize", "pcauchy", "plnorm", "plogis",
+ "pnorm", "pt", "qcauchy", "qchisq", "qlnorm", "qlogis", "qnorm",
+ "qt", "rcauchy", "rexp", "rlogis", "rmultinom", "rnorm", "rt",
+ "runif", "uniroot")
importFrom("forestplot",
"forestplot", "fpDrawPointCI", "fpDrawSummaryCI","fpDrawBarCI",
"fpColors")
diff --git a/R/bayesmeta.R b/R/bayesmeta.R
index dfbf1f7..f5c8a58 100644
--- a/R/bayesmeta.R
+++ b/R/bayesmeta.R
@@ -1,6 +1,6 @@
#
# bayesmeta, an R package for Bayesian random-effects meta-analysis.
-# Copyright (C) 2021 Christian Roever
+# Copyright (C) 2023 Christian Roever
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
@@ -3629,14 +3629,30 @@ traceplot <- function(x, ...)
traceplot.bayesmeta <- function(x, mulim, taulim, ci=FALSE,
ylab="effect",
- rightmargin=8, col=rainbow(x$k), ...)
+ prior=FALSE,
+ infinity=FALSE,
+ rightmargin=8, col=rainbow(x$k),
+ meanlabel="overall mean", meancol="black",
+ ...)
{
stopifnot(missing(mulim) || (length(mulim) == 2),
missing(taulim) || (length(taulim) <= 2),
- rightmargin >= 0, length(col) == x$k)
+ is.character(ylab), length(ylab)==1,
+ is.logical(prior), length(prior)==1,
+ is.logical(infinity), length(infinity)==1,
+ rightmargin >= 0, ((length(col)==x$k) | (length(col)==1)),
+ is.character(meanlabel), length(meanlabel)==1,
+ length(meancol)==1)
q975 <- qnorm(0.975)
gridcol <- "grey85"
- # convert "taulim" and "mulim" arguments
+ if (length(col)==1) col <- rep(col, x$k)
+ if (infinity & any(is.finite(x$mu.prior))) {
+ warning("mu prior ignored for `tau=Inf' computations!")
+ }
+ if (prior & (!x$tau.prior.proper)) {
+ warning("Note that plots of improper priors may not be sensibly scaled.")
+ }
+ # convert "taulim" and "mulim" input arguments
# to eventual "taurange" and "murange" vectors:
if (!missing(taulim) && all(is.finite(taulim))) {
if ((length(taulim)==2) && (taulim[1]>=0) && (taulim[2]>taulim[1]))
@@ -3648,34 +3664,55 @@ traceplot.bayesmeta <- function(x, mulim, taulim, ci=FALSE,
} else {
taurange <- c(0, x$qposterior(tau=0.995)*1.1)
}
-
- if (!missing(mulim) && (all(is.finite(mulim)) && (mulim[1] < mulim[2]))) {
- murange <- mulim
+ if (infinity) {
+ xlim <- taurange + c(0, 0.15) * diff(taurange)
+ infx <- xlim[2] + 0.04*diff(xlim) # the "infinity" x-coordinate
} else {
- cm <- x$cond.moment(tau=taurange[2], indiv=TRUE)
- if (ci){
- murange <- range(c(cm[,"mean",]-q975*cm[,"sd",], cm[,"mean",]+q975*cm[,"sd",]))
- } else {
- murange <- range(cm[,"mean",])
- }
- murange <- murange + c(-1,1)*diff(murange)*0.05
+ xlim <- taurange
+ infx <- NA_real_
}
+
+ if (missing(mulim)) mulim <- NULL
vertlines <- pretty(taurange)
+ # ensure no tickmarks beyond plotted tau range:
+ if (max(vertlines) > (taurange[2] + 0.04*diff(taurange)))
+ vertlines <- vertlines[-length(vertlines)]
mutrace <- function(x)
- {
- # range of tau values:
- tau <- seq(max(c(0,taurange[1]-0.1*diff(taurange))),
- taurange[2]+0.1*diff(taurange), le=200)
- cm.overall <- x$cond.moment(tau=tau)
+ {
+ # vector of tau values:
+ tau <- seq(max(c(0,taurange[1]-0.04*diff(taurange))),
+ taurange[2]+0.04*diff(taurange), le=200)
+ # conditional moments for individual studies (theta_i):
cm.indiv <- x$cond.moment(tau=tau, indiv=TRUE)
- plot(taurange, murange,
+ # conditional moments for overall mean (mu):
+ cm.overall <- x$cond.moment(tau=tau)
+ # determine axis range for "effect" (y-) axis
+ if (!is.null(mulim) && (all(is.finite(mulim)) && (mulim[1] < mulim[2]))) {
+ # user-defined:
+ murange <- mulim
+ } else {
+ # based on data:
+ if (ci){
+ murange <- range(c(range(cm.indiv[,"mean",]-q975*cm.indiv[,"sd",]),
+ range(cm.indiv[,"mean",]+q975*cm.indiv[,"sd",]),
+ range(cm.overall[,"mean"]-q975*cm.overall[,"sd"]),
+ range(cm.overall[,"mean"]+q975*cm.overall[,"sd"])))
+ } else {
+ murange <- range(c(range(cm.indiv[,"mean",]),
+ range(cm.overall[,"mean"])))
+ }
+ # ensure that estimates are also included:
+ if (infinity) murange <- range(murange, x$y)
+ }
+
+ plot(taurange, murange, xlim=xlim,
type="n", axes=FALSE, xlab="", ylab=ylab, main="", ...)
abline(v=vertlines, col=gridcol)
abline(h=pretty(murange), col=gridcol)
abline(v=0, col=grey(0.40))
- # grey shading:
+ # grey CI shading:
if (ci) {
for (i in 1:x$k) {
polygon(c(tau, rev(tau)),
@@ -3695,18 +3732,33 @@ traceplot.bayesmeta <- function(x, mulim, taulim, ci=FALSE,
matlines(tau, cm.indiv[,"mean",]+q975*cm.indiv[,"sd",], col=col, lty=3)
}
# overall mean:
- lines(tau, cm.overall[,"mean"], col="black", lty=2, lwd=1.5)
+ lines(tau, cm.overall[,"mean"], col=meancol, lty=2, lwd=1.5)
if (ci) {
- lines(tau, cm.overall[,"mean"]-q975*cm.overall[,"sd"], col="black", lty=3, lwd=1.5)
- lines(tau, cm.overall[,"mean"]+q975*cm.overall[,"sd"], col="black", lty=3, lwd=1.5)
+ lines(tau, cm.overall[,"mean"]-q975*cm.overall[,"sd"], col=meancol, lty=3, lwd=1.5)
+ lines(tau, cm.overall[,"mean"]+q975*cm.overall[,"sd"], col=meancol, lty=3, lwd=1.5)
+ }
+ if (infinity) {
+ labpos.indiv <- x$y
+ labpos.overall <- mean(x$y)
+ for (i in 1:x$k)
+ lines(c(max(tau), infx),
+ c(cm.indiv[length(tau),"mean",i], labpos.indiv[i]),
+ col=col[i], lty="13", lwd=1.5)
+ lines(c(max(tau), infx),
+ c(cm.overall[length(tau),"mean"], labpos.overall),
+ col=meancol, lty="13", lwd=2.0)
+ } else {
+ labpos.indiv <- cm.indiv[length(tau),"mean",]
+ labpos.overall <- cm.overall[length(tau),"mean"]
}
axis(2)
for (i in 1:x$k)
- axis(side=4, at=cm.indiv[length(tau),"mean",i],
+ axis(side=4, at=labpos.indiv[i],
labels=x$labels[i], tick=FALSE,
col.axis=col[i], las=1)
- axis(side=4, at=cm.overall[length(tau),"mean"],
- labels="overall mean", tick=FALSE, las=1)
+ axis(side=4, at=labpos.overall,
+ labels=meanlabel, tick=FALSE,
+ col.axis=meancol, las=1)
invisible()
}
@@ -3714,13 +3766,13 @@ traceplot.bayesmeta <- function(x, mulim, taulim, ci=FALSE,
# NB: function is (essentially) identical to the one within "plot.bayesmeta()"
{
# range of tau values:
- tau <- seq(max(c(0,taurange[1]-0.1*diff(taurange))),
- taurange[2]+0.1*diff(taurange), le=200)
+ tau <- seq(max(c(0,taurange[1]-0.04*diff(taurange))),
+ taurange[2]+0.04*diff(taurange), le=200)
# corresponding posterior density:
dens <- x$dposterior(tau=tau)
# empty plot:
maxdens <- max(dens[is.finite(dens)],na.rm=TRUE)
- plot(c(taurange[1],taurange[2]), c(0,maxdens),
+ plot(c(taurange[1],taurange[2]), c(0,maxdens), xlim=xlim,
type="n", axes=FALSE, xlab="", ylab="", main="")
abline(v=vertlines, col=gridcol)
# "fix" diverging density:
@@ -3737,16 +3789,27 @@ traceplot.bayesmeta <- function(x, mulim, taulim, ci=FALSE,
lines(rep(x$summary["median","tau"],2), c(0,x$dposterior(tau=x$summary["median","tau"])), col=grey(0.6))
# actual density line:
lines(tau, dens, col="black")
- # x-axis, y-axis:
- abline(h=0, v=0, col=grey(0.40))
+ # y-axis:
+ abline(v=0, col=grey(0.40))
+ # x-axis:
+ lines(taurange + c(-1,1) * 0.04*diff(taurange), c(0,0), col=grey(0.40))
+ # plot prior density (if requested):
+ if (prior) {
+ lines(tau, x$dprior(tau=tau), col="black", lty="dashed")
+ }
# add axes, labels, bounding box, ...
mtext(side=1, line=par("mgp")[1], expression("heterogeneity "*tau))
#mtext(side=2, line=par("mgp")[2], expression("marginal posterior density"))
- axis(1)#; box()
+ if (infinity) {
+ axis(1, at=c(vertlines, infx),
+ labels=c(as.numeric(vertlines), expression(infinity)))
+ } else {
+ axis(1, at=vertlines)
+ }
invisible()
}
- # make sure to re-set graphical parameters later:
+ # make sure to properly re-set graphical parameters later:
prevpar <- par(no.readonly=TRUE)
on.exit(par(prevpar))
# generate actual plot:
diff --git a/R/bmr.R b/R/bmr.R
index 515f403..f4e9507 100644
--- a/R/bmr.R
+++ b/R/bmr.R
@@ -1,6 +1,6 @@
#
# bayesmeta, an R package for Bayesian random-effects meta-analysis.
-# Copyright (C) 2021 Christian Roever
+# Copyright (C) 2023 Christian Roever
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
@@ -2222,13 +2222,55 @@ forestplot.bmr <- function(x,
traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
ylab="effect",
- rightmargin=8, col=rainbow(x$k), ...)
+ prior=FALSE,
+ infinity=FALSE,
+ rightmargin=8, col=rainbow(x$k),
+ X, Xlabels, Xcols="black",
+ ...)
{
stopifnot(missing(mulim) || (length(mulim) == 2),
missing(taulim) || (length(taulim) <= 2),
- rightmargin >= 0, length(col) == x$k)
+ is.character(ylab), length(ylab)==1,
+ is.logical(prior), length(prior)==1,
+ is.logical(infinity), length(infinity)==1,
+ rightmargin >= 0, ((length(col)==x$k) | (length(col)==1)))
q975 <- qnorm(0.975)
gridcol <- "grey85"
+ if (length(col)==1) col <- rep(col, x$k)
+ if (infinity & any(x$beta.prior.proper)) {
+ warning("beta prior ignored for `tau=Inf' computations!")
+ }
+ if (prior & (!x$tau.prior.proper)) {
+ warning("Note that plots of improper priors may not be sensibly scaled.")
+ }
+ # default treatment of "X" argument:
+ if (missing(X) || (is.null(X) || all(is.na(X)))) {
+ X <- NULL
+ lincoNumber <- 0
+ } else {
+ stopifnot(is.numeric(X),
+ (is.matrix(X) && (ncol(X) == x$d))
+ || (is.vector(X) && (length(X) == x$d)))
+ if (is.vector(X)) {
+ X <- matrix(X, nrow=1, ncol=x$d)
+ }
+ if (is.null(colnames(X))) colnames(X) <- x$variables
+ lincoNumber <- nrow(X)
+ }
+ # determine "X" labels and colours:
+ if (lincoNumber > 0) {
+ if (!missing(Xlabels)) {
+ stopifnot(is.character(Xlabels), length(Xlabels) == nrow(X))
+ rownames(X) <- Xlabels
+ } else {
+ Xlabels <- rownames(X) # take from X row names...
+ if (is.null(Xlabels)) { # ...or make up some
+ Xlabels <- sprintf("X.%02d", 1:nrow(X))
+ }
+ }
+ stopifnot(is.vector(Xcols), ((length(Xcols)==1) | (length(Xcols)==lincoNumber)))
+ if (length(Xcols)==1) Xcols <- rep(Xcols, lincoNumber)
+ }
# convert "taulim" and "mulim" arguments
# to eventual "taurange" and "murange" vectors:
if (!missing(taulim) && all(is.finite(taulim))) {
@@ -2241,38 +2283,73 @@ traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
} else {
taurange <- c(0, x$qposterior(tau=0.995)*1.1)
}
- vertlines <- pretty(taurange)
- if (!missing(mulim) && (all(is.finite(mulim)) && (mulim[1] < mulim[2]))) {
- murange <- mulim
+
+ if (infinity) {
+ xlim <- taurange + c(0, 0.15) * diff(taurange)
+ infx <- xlim[2] + 0.04*diff(xlim) # the "infinity" x-coordinate
} else {
- cm <- matrix(NA_real_, nrow=x$k, ncol=2,
- dimnames=list("study"=x$labels, "moments"=c("mean", "sd")))
- for (i in 1:x$k)
- cm[i,] <- x$shrink.moments(tau=taurange[2], which=i)
- if (ci){
- murange <- range(c(cm[,"mean"]-q975*cm[,"sd"], cm[,"mean"]+q975*cm[,"sd"]))
- } else {
- murange <- range(cm[,"mean"])
- }
- murange <- murange + c(-1,1)*diff(murange)*0.05
+ xlim <- taurange
+ infx <- NA_real_
}
+ vertlines <- pretty(taurange)
+ # ensure no tickmarks beyond plotted tau range:
+ if (max(vertlines) > (taurange[2] + 0.04*diff(taurange)))
+ vertlines <- vertlines[-length(vertlines)]
+
+ if (missing(mulim)) mulim <- NULL
+
mutrace <- function(x)
{
- # range of tau values:
- tau <- seq(max(c(0,taurange[1]-0.1*diff(taurange))),
- taurange[2]+0.1*diff(taurange), le=200)
- cm.indiv <- array(NA_real_, dim=c(length(tau), 2, x$k),
- dimnames=list(NULL, c("mean", "sd"), x$labels))
+ # vector of tau values:
+ tau <- seq(max(c(0,taurange[1]-0.04*diff(taurange))),
+ taurange[2]+0.04*diff(taurange), le=200)
+ # moments for individual studies:
+ cm.indiv <- array(NA_real_, dim=c(length(tau), 2, x$k),
+ dimnames=list(NULL, c("mean", "sd"), x$labels))
for (i in 1:x$k) {
cm.indiv[,,i] <- x$shrink.moment(tau=tau, which=i)
}
- plot(taurange, murange,
+ # moments for linear combinations (if applicable):
+ if (lincoNumber > 0) {
+ cm.linco <- array(NA_real_, dim=c(length(tau), 2, lincoNumber),
+ dimnames=list(NULL, c("mean", "sd"), Xlabels))
+ for (i in 1:lincoNumber) {
+ cm.linco[,,i] <- x$pred.moments(tau=tau, x=X[i,])
+ }
+ }
+
+ # determine axis range for "effect" (y-) axis
+ if (!is.null(mulim) && (all(is.finite(mulim)) && (mulim[1] < mulim[2]))) {
+ # user-defined:
+ murange <- mulim
+ } else {
+ # based on data:
+ if (ci){
+ murange <- range(c(range(cm.indiv[,"mean",]-q975*cm.indiv[,"sd",]),
+ range(cm.indiv[,"mean",]+q975*cm.indiv[,"sd",])))
+ if (lincoNumber > 0) {
+ murange <- range(c(murange,
+ range(cm.linco[,"mean",]-q975*cm.linco[,"sd",]),
+ range(cm.linco[,"mean",]+q975*cm.linco[,"sd",])))
+ }
+ } else {
+ murange <- range(cm.indiv[,"mean",])
+ if (lincoNumber > 0) {
+ murange <- range(c(murange,
+ range(cm.linco[,"mean",])))
+ }
+ }
+ # ensure that estimates are included:
+ if (infinity) murange <- range(murange, x$y)
+ }
+
+ plot(taurange, murange, xlim=xlim,
type="n", axes=FALSE, xlab="", ylab=ylab, main="", ...)
abline(v=vertlines, col=gridcol)
abline(h=pretty(murange), col=gridcol)
abline(v=0, col=grey(0.40))
- # grey shading:
+ # grey CI shading:
if (ci) {
for (i in 1:x$k) {
polygon(c(tau, rev(tau)),
@@ -2280,6 +2357,14 @@ traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
rev(cm.indiv[,"mean",i] + q975*cm.indiv[,"sd",i])),
col=grey(0.75, alpha=0.25), border=NA)
}
+ if (lincoNumber > 0) {
+ for (i in 1:lincoNumber) {
+ polygon(c(tau, rev(tau)),
+ c(cm.linco[,"mean",i] - q975*cm.linco[,"sd",i],
+ rev(cm.linco[,"mean",i] + q975*cm.linco[,"sd",i])),
+ col=grey(0.75, alpha=0.25), border=NA)
+ }
+ }
}
# individual estimates:
matlines(tau, cm.indiv[,"mean",], col=col, lty=1)
@@ -2287,11 +2372,54 @@ traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
matlines(tau, cm.indiv[,"mean",]-q975*cm.indiv[,"sd",], col=col, lty=3)
matlines(tau, cm.indiv[,"mean",]+q975*cm.indiv[,"sd",], col=col, lty=3)
}
+ # linear combinations:
+ if (lincoNumber > 0) {
+ matlines(tau, cm.linco[,"mean",], col=Xcols, lty=2, lwd=1.5)
+ if (ci) {
+ matlines(tau, cm.linco[,"mean",]-q975*cm.linco[,"sd",],
+ col=Xcols, lty=3, lwd=1.5)
+ matlines(tau, cm.linco[,"mean",]+q975*cm.linco[,"sd",],
+ col=Xcols, lty=3, lwd=1.5)
+ }
+ }
+
+ if (infinity) {
+ # individual studies:
+ labpos.indiv <- x$y
+ for (i in 1:x$k) {
+ lines(c(max(tau), infx),
+ c(cm.indiv[length(tau),"mean",i], labpos.indiv[i]),
+ col=col[i], lty="13", lwd=1.5)
+ }
+ # linear combinations:
+ if (lincoNumber > 0) {
+ # compute (unweighted) least-squares fit:
+ lscoef <- stats::lm.fit(x=x$X, y=x$y)$coefficients
+ labpos.linco <- X %*% lscoef
+ for (i in 1:lincoNumber) {
+ lines(c(max(tau), infx),
+ c(cm.linco[length(tau),"mean",i], labpos.linco[i]),
+ col=Xcols[i], lty="13", lwd=2)
+ }
+ }
+ } else {
+ labpos.indiv <- cm.indiv[length(tau),"mean",]
+ if (lincoNumber > 0) {
+ labpos.linco <- cm.linco[length(tau),"mean",]
+ }
+ }
axis(2)
for (i in 1:x$k)
- axis(side=4, at=cm.indiv[length(tau),"mean",i],
+ axis(side=4, at=labpos.indiv[i],
labels=x$labels[i], tick=FALSE,
col.axis=col[i], las=1)
+ if (lincoNumber > 0) {
+ for (i in 1:lincoNumber) {
+ axis(side=4, at=labpos.linco[i],
+ labels=Xlabels[i], tick=FALSE,
+ col.axis=Xcols[i], las=1)
+ }
+ }
invisible()
}
@@ -2299,13 +2427,13 @@ traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
# NB: function is (essentially) identical to the one within "plot.bayesmeta()"
{
# range of tau values:
- tau <- seq(max(c(0,taurange[1]-0.1*diff(taurange))),
- taurange[2]+0.1*diff(taurange), le=200)
+ tau <- seq(max(c(0,taurange[1]-0.04*diff(taurange))),
+ taurange[2]+0.04*diff(taurange), le=200)
# corresponding posterior density:
dens <- x$dposterior(tau=tau)
# empty plot:
maxdens <- max(dens[is.finite(dens)],na.rm=TRUE)
- plot(c(taurange[1],taurange[2]), c(0,maxdens),
+ plot(c(taurange[1],taurange[2]), c(0,maxdens), xlim=xlim,
type="n", axes=FALSE, xlab="", ylab="", main="")
abline(v=vertlines, col=gridcol)
# "fix" diverging density:
@@ -2322,16 +2450,27 @@ traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
lines(rep(x$summary["median","tau"],2), c(0,x$dposterior(tau=x$summary["median","tau"])), col=grey(0.6))
# actual density line:
lines(tau, dens, col="black")
- # x-axis, y-axis:
- abline(h=0, v=0, col=grey(0.40))
+ # y-axis:
+ abline(v=0, col=grey(0.40))
+ # x-axis:
+ lines(taurange + c(-1,1) * 0.04*diff(taurange), c(0,0), col=grey(0.40))
+ # plot prior density (if requested):
+ if (prior) {
+ lines(tau, x$dprior(tau=tau), col="black", lty="dashed")
+ }
# add axes, labels, bounding box, ...
mtext(side=1, line=par("mgp")[1], expression("heterogeneity "*tau))
#mtext(side=2, line=par("mgp")[2], expression("marginal posterior density"))
- axis(1)#; box()
+ if (infinity) {
+ axis(1, at=c(vertlines, infx),
+ labels=c(as.numeric(vertlines), expression(infinity)))
+ } else {
+ axis(1, at=vertlines)
+ }
invisible()
}
- # make sure to re-set graphical parameters later:
+ # make sure to properly re-set graphical parameters later:
prevpar <- par(no.readonly=TRUE)
on.exit(par(prevpar))
# generate actual plot:
@@ -2342,5 +2481,5 @@ traceplot.bmr <- function(x, mulim, taulim, ci=FALSE,
taumarginal(x)
graphics::layout(1)
par(mar=c(5,4,4,2)+0.1)
- invisible()
+ invisible(X)
}
diff --git a/man/bayesmeta-package.Rd b/man/bayesmeta-package.Rd
index 975dd70..d9d3fe1 100644
--- a/man/bayesmeta-package.Rd
+++ b/man/bayesmeta-package.Rd
@@ -16,7 +16,7 @@
Package: \tab bayesmeta\cr
Type: \tab Package\cr
Version: \tab 3.3\cr
- Date: \tab 2023-03-16\cr
+ Date: \tab 2023-04-14\cr
License: \tab GPL (>=2)
}
The main functionality is provided by the \code{\link{bayesmeta}()}
diff --git a/man/traceplot.bayesmeta.Rd b/man/traceplot.bayesmeta.Rd
index 0bbb6fc..41d5a9f 100644
--- a/man/traceplot.bayesmeta.Rd
+++ b/man/traceplot.bayesmeta.Rd
@@ -4,22 +4,27 @@
\alias{traceplot.bayesmeta}
\alias{traceplot.bmr}
\title{
- Illustrate conditional means of overall effect as well as study-specific
- estimates as a function of heterogeneity.
+ Illustrate conditional means of study-specific estimates as well as
+ overall mean (or other linear combinations) as a function of
+ heterogeneity.
}
\description{
- Generates a trace plot of overall mean effect and study-specific
- (shrinkage) estimates as a function of the heterogeneity (\eqn{\tau}). The
- heterogeneity's posterior distribution is also indicated.
+ Generates a trace plot of study-specific (shrinkage) estimates as a
+ function of the heterogeneity (\eqn{\tau}), along with conditional
+ estimates of the overall mean or other linear combinations of
+ regression parameters.
+ The heterogeneity's posterior distribution is also shown at the bottom.
}
\usage{
traceplot(x, ...)
\method{traceplot}{bayesmeta}(x, mulim, taulim, ci=FALSE,
- ylab="effect",
- rightmargin=8, col=rainbow(x$k), ...)
+ ylab="effect", prior=FALSE, infinity=FALSE,
+ rightmargin=8, col=rainbow(x$k),
+ meanlabel="overall mean", meancol="black", ...)
\method{traceplot}{bmr}(x, mulim, taulim, ci=FALSE,
- ylab="effect",
- rightmargin=8, col=rainbow(x$k), ...)
+ ylab="effect", prior=FALSE, infinity=FALSE,
+ rightmargin=8, col=rainbow(x$k),
+ X, Xlabels, Xcols="black", ...)
}
\arguments{
\item{x}{
@@ -35,7 +40,16 @@
confidence intervals.
}
\item{ylab}{
- a y-axis label.
+ a label for the effect (mu) axis.
+ }
+ \item{prior}{
+ a logical flag indicating whether to show the (heterogeneity) prior
+ density along with its posterior.
+ }
+ \item{infinity}{
+ a logical flag indicating whether add an \dQuote{infinity} tickmark
+ to the heterogeneity (tau) axis and show the corresponding limiting
+ values.
}
\item{rightmargin}{
an additional margin to be added to the right side of the plot, in
@@ -45,6 +59,25 @@
\item{col}{
colors to be used for plotting the (\eqn{k}) estimates.
}
+ \item{meanlabel}{
+ a label for the overall mean estimate
+ (\code{traceplot.bayesmeta()}).
+ }
+ \item{meancol}{
+ colour specification for the overall mean estimate
+ (\code{traceplot.bayesmeta()}).
+ }
+ \item{X}{
+ matrix (or vector) of coefficients defining linear combinations of
+ regression parameters to be shown (\code{traceplot.bmr()}).
+ }
+ \item{Xlabels}{
+ labels for the linear combinations (\code{traceplot.bmr()}).
+ }
+ \item{Xcols}{
+ colour specification for the linear combinations
+ (\code{traceplot.bmr()}).
+ }
\item{...}{
other arguments passed on to the
\code{\link[graphics]{plot}()} function.
@@ -87,6 +120,12 @@
Estimation in parallel randomized experiments.
\emph{Journal of Educational Statistics}, \bold{6}(4):377-401, 1981.
\doi{10.3102/10769986006004377}.
+
+ DuMouchel, W. H. (1994).
+ Hierarchical Bayes linear models for meta-analysis.
+ Technical Report 27, National Institute of Statistical Sciences (NISS);
+ Research Triangle Park, NC, USA.
+ \url{https://www.niss.org/research/technical-reports/hierarchical-bayes-linear-models-meta-analysis-1994}
}
\seealso{
\code{\link{bayesmeta}}, \code{\link{bmr}}.
--
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