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...r package (version 1.6-0) in R (2.15.1, 64-bit Windows 7) and noticed that I was getting slightly different values when I manually calculated the standardized mean difference versus what escalc was giving me. Here''s a very simple example: escalc(measure="SMD", m1i=5,m2i=10,n1i=5,n2i=5,sd1i=1,sd2i=2,vtype="LS") The result is: yi vi 1 -2.854599 0.8074367 However, if I calculate this manually using the pooled standard deviation formula given in Cooper et al (2009)*, I get an SMD of -2.85625 and a variance of 0.870205. The formula given in Cooper e...

...rs are considerably different. Unfortunately, I could not find where I made it wrong. I would be grateful if someone would have a look at my calculations. Here are the meta-analysis commands: ### USING METAFOR library(metafor) ( dat<-escalc(m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, measure="SMD", data=metaData, append=T) ) # COMPUTE EFFECT SIZE ( res<-rma.uni(yi,vi,data=dat,method="HE", level=95) ) ### RANDOM EFFECT ( res<-rma.uni(yi,vi,data=dat,method="FE", level=95) ) ### FIXED EFFECT ### USING META ( res<-metacont(metaData[,3]...

Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between

Hello list. I'm trying to rebuild the 2.6.9.67.0.20.EL kernel, but it fails even without modifications. How did I try it? Created a (non-root) build environment (not a mock ) Installed the kernel.scr.rpm and did a rpmbuild -ba --target=`uname -m` kernel-2.6.spec 2> prep-err.log | tee prep-out.log The build failed at the end: Processing files: kernel-xenU-devel-2.6.9-67.0.20.EL Checking