"rma.uni"
.trimfill(x, ...)
"trimfill"(x, side, estimator="L0", maxiter=100, verbose=FALSE, ...)
"rma.uni"
."left"
or "right"
, indicating on which side of the funnel plot the missing studies should be imputed. If left undefined, the side is chosen within the function depending on the results of Egger's regression test (see regtest
for details on this test)."L0"
, "R0"
, or "Q0"
, indicating the estimator to use for estimating the number of missing studies (default is "L0"
).100
).FALSE
).c("rma.uni.trimfill","rma.uni","rma")
. The object is a list containing the same components as objects created by rma.uni
, except that the data are augmented by the trim and fill method. The following components are also added:
"left"
or "right"
, indicating on which side of the funnel plot the missing studies (if any) were imputed.estimator="R0"
; NA
otherwise).FALSE
) and the augmented (TRUE
) data.print.rma.uni
function. Calling funnel.rma
on the object provides a funnel plot of the observed and augmented data.
Duval, S. J., & Tweedie, R. L. (2000b). A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95, 89--98.
Duval, S. J. (2005). The trim and fill method. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.) Publication bias in meta-analysis: Prevention, assessment, and adjustments (pp. 127--144). Chichester, England: Wiley.
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/.
funnel.rma
### 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")
res.tf <- trimfill(res)
res.tf
funnel(res.tf)
### estimator "R0" also provides test
res.tf <- trimfill(res, estimator="R0")
res.tf
### 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)
res.tf <- trimfill(res)
res.tf
funnel(res.tf)
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