search for: sd2i

...ry simple global sieve script : require "fileinto"; if header :contains "X-DSPAM-Result" "Spam" { fileinto "SPAM"; stop; } with dovecot 1.0 all run fine, but moving to 1.1 I experience this in maillog : Oct 13 10:31:52 h8h1 deliver(ml at sd2i.com): msgid=<8a24c15885037554724ff966f5458592 at newsletter.RedSquare.com.au >: save failed to SPAM: Unknown namespace Oct 13 10:31:52 h8h1 deliver(ml at sd2i.com): sieve runtime error: Fileinto: Generic Error Oct 13 10:31:52 h8h1 deliver(ml at sd2i.com): sieve_execute_bytecode(/usr/ loc...

Dear all, I am very grateful that Wolfgang Viechtbauer implemented the standardised mean change for dependent groups. I was playing around a bit today, and I am not sure if I understand the "SMCR" procedure correctly. The documentation states that sd1i and sd2i are needed, but it seems to me that SMCR is ignoring sd2i (so Variances are not pooled). Instead, it uses sd1i (pre-test sd), as suggested by Becker 1988. Is that correct? Thank you all very much for your time and help, Markus [[alternative HTML version deleted]]

...rsion 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 et al for the p...

...t, the numbers 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(m...

a user complain about suddent disconnect error from time to time (he's using pop3 with Apple Mail) Looking at dovecot logs I discover that, each time the error shows up for him, I have this : dovecot: pop3[95137]: Info: user=<dan at userdomain.com> Disconnected top=0/0, retr=0/0, del=0/4, size=127101 but all other "regular" pop3 session looks like this one : dovecot:

?Dear All I have some data expressed in geometric means and 95% confidence intervals. Can I code them in metafor as: rma(m1i=geometric mean 1, m2i=geometric mean 2, sd1i=geometric mean 1 CI /3.92, sd2i=geometric mean 2 CI/3.92.......etc, measure="MD") All of the studies use geometric means. Thanks! Edward ---------------------------- [[alternative HTML version deleted]]

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

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",

I'm not a developper so I can't do it by myself, but I wonder if it could be possible to use tagSpam and tagNospam DSPAM feature to ehance user's dspam lerning experience. I even don't know if this must be done by DSPAM itself or by dovecot, but let me explain : The feature would be done by a signature replacement into tagSpam and tagNospam text, so the user would simply