HI
r-help-boun...@r-project.org napsal dne 28.01.2010 04:35:29:
> > Hi All,
> >
> > I'm conducting a meta-analysis and have taken a data.frame with
multiple
> > rows per
> > study (for each effect size) and performed a weighted average of
effect
> > size for
> > each study. This results in a reduced # of rows. I am particularly
> > interested in
> > simply reducing the additional variables in the data.frame to the
first row
> > of the
> > corresponding id variable. For example:
> >
> > id<-c(1,2,2,3,3,3)
> > es<-c(.3,.1,.3,.1,.2,.3)
> > mod1<-c(2,4,4,1,1,1)
> > mod2<-c("wai","other","calpas","wai","itas","other")
> > data<-as.data.frame(cbind(id,es,mod1,mod2))
Do not use cbind. Its output is a matrix and in this case character
matrix. Resulting data frame will consist from factors as you can check by
str(data)
data<-data.frame(id=id,es=es,mod1=mod1,mod2=mod2)
> >
> > data
> >
> > id es mod1 mod2
> > 1 1 0.3 2 wai
> > 2 2 0.1 4 other
> > 3 2 0.2 4 calpas
> > 4 3 0.1 1 itas
> > 5 3 0.2 1 wai
> > 6 3 0.3 1 wai
> >
> > # I would like to reduce the entire data.frame like this:
E.g. aggregate
aggregate(data[, -(3:4)], data[,3:4], mean)
mod1 mod2 id es
1 4 calpas 2 0.3
2 1 itas 3 0.2
3 1 other 3 0.3
4 4 other 2 0.1
5 1 wai 3 0.1
6 2 wai 1 0.3
doBy or tapply or ddply from plyr library or ....
Regards
Petr
> >
> > id es mod1 mod2
> >
> > 1 .30 2 wai
> > 2 .15 4 other
> > 3 .20 1 itas
> >
> > # If possible, I would also like the option of this (collapsing on id
and
> > mod2):
> >
> > id es mod1 mod2
> > 1 .30 2 wai
> > 2 0.1 4 other
> > 2 0.2 4 calpas
> > 3 0.1 1 itas
> > 3 0.25 1 wai
> >
> > Any help is much appreciated!
> >
> > AC Del Re
> >
>
> [[alternative HTML version deleted]]
>
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