Hi,
Assuming that your dataset is similar to the one below:
set.seed(25)
dat1<-
data.frame(Algae.Mass=sample(40:50,10,replace=TRUE),Seagrass.Mass=sample(30:70,10,replace=TRUE),Terrestrial.Mass=sample(80:100,10,replace=TRUE),Other.Mass=sample(40:60,10,replace=TRUE),Site.X.Treatment=rep(c("ALA1A","ALA1U"),each=5),stringsAsFactors=FALSE)
library(reshape2)
dat2<-melt(dat1,id.var="Site.X.Treatment")
sapply(split(dat2,dat2$variable),function(x)
t.test(x[x$Site.X.Treatment=="ALA1A",3],x[x$Site.X.Treatment=="ALA1U",3],paired=TRUE)$p.value)
# Algae.Mass Seagrass.Mass Terrestrial.Mass Other.Mass
# 1.0000000 0.4624989 0.4388211 0.7521036
#or
library(plyr)
ddply(dat2,.(variable),function(x)
summarize(x,Pvalue=t.test(value~Site.X.Treatment,data=x,na.rm=TRUE,paired=TRUE)$p.value))
# variable Pvalue
#1 Algae.Mass 1.0000000
#2 Seagrass.Mass 0.4624989
#3 Terrestrial.Mass 0.4388211
#4 Other.Mass 0.7521036
A.K.
>Hey,
>
>I have a fairly large data set with multiple pairs of Sites.
Each site has two levels (the pairs) "A" and "U". For each pair I want
to do a paired t test of >4 different metrics that exist as columns in
my data set.
>
>Here is the long version
>
>t.test(Algae.Mass[Site.X.Treatment=="ALA1A"],Algae.Mass[Site.X.Treatment=="ALA1U"],
> paired=T)
>t.test(Seagrass.Mass[Site.X.Treatment=="ALA1A"],Seagrass.Mass[Site.X.Treatment=="ALA1U"],
> paired=T)
>t.test(Terrestrial.Mass[Site.X.Treatment=="ALA1A"],Terrestrial.Mass[Site.X.Treatment=="ALA1U"],
> paired=T)
>t.test(Other.Mass[Site.X.Treatment=="ALA1A"],Other.Mass[Site.X.Treatment=="ALA1U"],
> paired=T)
>
>How can I do this in one line of code? I have tried lapply,
tapply etc but keep running into issues. It would also be great to not
have to keep defining >"Site.X.Treatment". I do have Site.X.Treatment
broken down by just Site and Treatment in separate columns in the data
set. Any Ideas??
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