"rma"
.radial(x, ...)
galbraith(x, ...)
"radial"(x, center=FALSE, xlim, zlim, xlab, zlab, atz, aty, steps=7, level=x$level, digits=2, back="lightgray", transf, targs, pch=19, arc.res=100, cex, ...)
"rma"
.FALSE
).aty
is used.NA
to suppress shading of the region.transf=exp
). If unspecified, no transformation is used.transf
.points
for other options.For a random-effects model, the function uses 1/\sqrt(vᵢ + \tau²) for the horizontal axis, where vᵢ is the sampling variance of the observed effect size or outcome and \tau² is the amount of heterogeneity as estimated based on the model. For the z-axis, \sqrt(vᵢ + \tau²) is used to standardize the individual observed effect sizes or outcomes.
If the model contains moderators, the function returns an error.
Galbraith, R. F. (1988). A note on graphical presentation of estimated odds ratios from several clinical trials. Statistics in Medicine, 7, 889--894.
Galbraith, R. F (1994). Some applications of radial plots. Journal of the American Statistical Association, 89, 1232--1242.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. http://www.jstatsoft.org/v36/i03/.
rma.uni
, rma.mh
, rma.peto
, rma.glmm
, rma.mv
### load BCG vaccine data
data(dat.bcg)
### meta-analysis of the log relative risks using a fixed-effects model
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, method="FE")
radial(res)
### line from (0,0) with slope equal to the log relative risk from the 4th study
abline(a=0, b=c(-1.44155119), lty="dotted")
### meta-analysis of the log relative risks using a random-effects model
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
radial(res)
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