||@t@ @end|ng |rom dewey@myzen@co@uk
Fri Mar 10 14:24:50 CET 2023
First of all please do not hijack an old thread but start a new one as
your e-mail (and my reply) appear to be about marginal means in the archive.
Have you checked the standard deviations? I assume some are zero which
explains both your messages. It is likely the package then drops them.
On 10/03/2023 11:46, mark walton via R-sig-meta-analysis wrote:
> Dear All
> I wonder if some one can help.
> I am using metamean to determine with of my habitats have the most microplastics, I have collected 400+ papers with means, SD and sample number.Using the command:-metamean(MP_global, mean=meanMP_per_m2, na.rm=TRUE, sd=MP_SD, na.rm=TRUE,n=Sample_no, na.rm=TRUE ,byvar=Ecosystem, na.rm=TRUE,comb.random=TRUE,comb.fixed=FALSE,verbose=TRUE, control=list(maxiter=1000))
> I get some repeated warnings: Warning: Studies with non-positive standard deviation get no weight in meta-analysis.Warning: Ratio of largest to smallest sampling variance extremely large. May not be able to obtain stable results.
> But the model converges and I get means and confidence intervals, and significant differences between subgroups
> However when I estimate my own mean values for each habitat weighted by sample number (below) the values are very different for some, and I am worried that the metamean results are not a true reflection of the data.
> ddply(MP_global, .(Ecosystem), function(x) data.frame(wret=weighted.mean(x$meanMP_per_m2, x$Sample_no)))
> Can anyone shed some light? Is it because metamean is ignoring some values?
> Thanks very muchMark
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