I am not sure that I fully understand what all you want to do (and I don't
understand why you need the correlation and if a correlation based on 3 pairs is
even meaningful), but here is a first stab at what you are trying to do:
tmp <- "Species Control_CR Damage_DR
A 10 2
A 9 3
A 7 4
A 9 2
A 8 3
B 5 4
B 6 3
B 4 2
B 5 4
B 6 3
C 8 1
C 6 2
C 4 4
C 7 1
C 6 2"
plants <- read.table( textConnection(tmp), header=TRUE )
plants2 <- rbind( data.frame( Species=plants$Species, growth=plants[[2]],
group=1 ),
data.frame(Species=plants$Species, growth=plants[[3]], group=2 ) )
tmpfun <- function() {
group2 <- ave( plants2$group, plants2$Species, FUN=sample )
cr <- tapply( plants2[ group2==1, 'growth'], plants2[group2==1,
'Species'],
FUN=mean)
dr <- tapply( plants2[ group2==2, 'growth'], plants2[group2==2,
'Species'],
FUN=mean)
ir <- cr - dr
cor( ir, cr )
}
out <- replicate( 1000, tmpfun() )
hist(out)
Is this what you wanted? Or at least helpful for going in the right direction?
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Anne Kempel
> Sent: Thursday, July 23, 2009 9:02 AM
> To: r-help at r-project.org
> Subject: [R] help with randomisation test...
>
> Dear R-people,
> I hope asking this is not too cheeky, but I do have a R Problem. I hope
> that some of you like to play around with R and can help me.
>
> Its like this. I have several plant species (A,B,C) and 10 replicates
> per species. 5 plants per species are damaged, 5 not. I let a
> caterpillar feed on each plant and measured the growth of the
> caterpillars on control plants (CR) and on damaged plants (DR). The
> difference, DR-CR is an indicator for induced resistance IR.
>
> The data could look like this...just as an example.
>
> Species Control_CR Damage_DR
> A 10 2
> A 9 3
> A 7 4
> A 9 2
> A 8 3
> B 5 4
> B 6 3
> B 4 2
> B 5 4
> B 6 3
> C 8 1
> C 6 2
> C 4 4
> C 7 1
> C 6 2
>
>
> Now, i want to see if there is a trade-off (or a negative correlation)
> between CR and IR, but because IR is calculated out of CR there is a
> danger of spurious correlation. What I have to do to get rid of it is
> the following:
>
> 1)Draw with replacement sets of 5 plants per treatment for each species
>
> 2)Calculate of those sets the means (in the control and the damage
> treatment) and then abstract the mean of the damage minus the mean of
> the control to get a mean for IR.
>
> 3)Randomize this IR (now called IRrandom)among all the species.
> 4)Go back to the data, calculate: Each of the 5 CR values minus this
> IRrandom to get 5 new DR, then draw again with replacements sets of 5
> plants of each treatment for each species and calculate new means for
> the CR and the new DR. Now you have a mean CR and a new mean DR - you
> can calculate DR-CR to get a new IR (IRnew).
>
> 5)Correlate this CR with the new IR. And store the result.
>
> 6)Repeat step 1-5 many times. And make a histogram of the resulting
> corelation coefficients.
>
>
> You see, this is quite complicated. Does any of you have any idea how
> to
> start? I am very very grateful for any suggestions!
> The helper will get a parcel with swiss chocolate ;-) !!
>
> Best wishes from Bern,
> Anne
>
>
> --
> Anne Kempel
> Institute of Plant Sciences
> University of Bern
> Altenbergrain 21
> CH-3103 Bern
> kempel at ips.unibe.ch
>
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> and provide commented, minimal, self-contained, reproducible code.