Dear R community,
I'm using the ,metafor'-package by Wolfgang Viechtbauer (Version: 0.5-5) to
calculate random-effects meta-analyses using Correlations and Sample Sizes
as the raw data.
(By the way: Really a nice piece of work, Wolfgang! Thanks heaps.)
I specified the "rma.uni' function so that it looks like this:
MAergebnis<-rma.uni(ri=PosOutc, ni=N,
mods=NULL,
data=dm, intercept=TRUE, slab=c(dm$Article, dm$StudyNo),
subset=NULL, measure="ZCOR", add=1/2, to="only0",
vtype="LS",
method="DL", weighted=TRUE, level=95, digits=4, btt=NULL,
tau2=NULL, knha=FALSE, control=list())
With PosOutc being the column containing the raw correlations and N being
the column with the sample sizes.
I assume that this will imply that
a) although the 'tau2' value is set to NULL, still a random-effects analysis
is calculated
b) the raw correlation coefficients are transformed to fisher-z-values
c) the according variances of the fisher-z-values are calculated from the
sample sizes
d) these calculated z-values and sample sizes are then used to calculate
summary effect and confidence interval limits
e) the resulting object contains z-values and not raw correlations
Now, the problem is:
How do I get the calculated results back into the form of raw correlation
coefficients? (In order to report them in an article.)
I see two cases, where this might be relevant:
A) When reporting numeric results:
I tried the following:
print.rma.uni(MAergebnis, transf="transf.ztor")
but then noticed that the following will bring up the same result:
print.rma.uni(MAergebnis)
(When using the "z2r" and "r2z" functions out of the "psychometric"-package
on the separate numbers, the values change, so it can't simply be a matter
of rounding.)
B) When drawing a forest plot:
forest(MAergebnis, ., transf="transf.ztor", .)
should to the trick, right?
Does anyone have any experience or greater insight knowledge regarding this
little problem?
Best regards
Sebastian
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