"rma".labbe(x, ...)
"labbe"(x, xlim, ylim, xlab, ylab, add=x$add, to=x$to, transf, targs, pch=21, psize, bg="gray", grid=FALSE, ...)"rma". See ‘Details’.escalc function for more details.escalc function for more details.transf=exp; see also transf). If unspecified, no transformation is used.transf.points for other options."gray"). Can also be a vector of values. Set to NA to make the plotting symbols transparent.x must be a model without moderators (i.e., either a fixed- or a random-effects model) fitted with either the rma.uni, rma.mh, rma.peto, or rma.glmm function. Moreover, the model must be fitted with measure set equal to "RD" (for risk differences), "RR" (for relative risks), "OR" (for odds ratios), "AS" (for arcsine square-root transformed risk differences), "IRR" (for incidence rate ratios), "IRD" (for incidence rate differences), or "IRSD" (for square-root transformed incidence rate differences). The function calculates the arm-level outcomes for the two experimental groups (e.g., treatment and control groups) and plots them against each other. In particular, the function plots the raw proportions of the two groups again each other when analyzing risk differences, the log of the proportions when analyzing (log) relative risks, the log odds when analyzing (log) odds ratios, the arcsine square-root transformed proportions when analyzing arcsine square-root transformed risk differences, the raw incidence rates when analyzing incidence rate differences, the log of the incidence rates when analyzing (log) incidence rate ratios, and the square-root transformed incidence rates when analyzing square-root transformed incidence rate differences. The transf argument can be used to transform these values (for example, transf=exp to transform the log of the proportions back to raw proportions; see also transf).
As described under the documentation for the escalc function, zero cells can lead to problems when calculating particular outcomes. Adding a small constant to the cells of the $2x2$ tables is a common solution to this problem. By default, the functions adopts the same method for handling zero cells as was done when fitting the model.
The size of the points is drawn proportional to the precision (inverse standard error) of the outcomes. The solid line corresponds to identical outcomes in the two groups (i.e., the absence of a difference between the two groups). The dashed line indicates the estimated effect based on the fitted model.
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
### 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)
### default plot
labbe(res)
### funnel plot with risk values on the x- and y-axis
labbe(res, transf=exp)
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