Thank you, it worked!
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Thank you a lot A.K.!
One more question:
I'd like to compute the Spearman's rank correlation coefficients for V1
(from dat1) and V1 (from dat2) and so on... Do you know how to do that?
I managed to write the Pearson's correlation product moment coefficient with
your sapply-approach, but I have no
],dat2[,i],method="spearman"))
#[1] -1.000 0.500 -0.8660254
#or
diag(cor(dat1,dat2,method="spearman"))
# V1 V2 V3
#-1.000 0.500 -0.8660254
A.K.
- Original Message -
From: laro
To: r-help@r-project.org
Cc:
Sent: Friday, August 3
Thank you for your answer. But further calculations will be much more
difficult, like
(1-b)^2 * Var(V1) for all matching columns
where b is the slope from a regression V1 (from datset 1) on V1 (dataset 2)
and Var(V1) the variance from V1(from dataset2).
So what I'm looking for is somethi
Hi,
Try:
res<-sapply(seq_len(ncol(dat1)),function(i)
setNames(((1-coef(lm(dat1[,i]~dat2[,i]))[2])^2)*var(dat2[,i]),NULL))
res
#[1] 21.0 16.11842 18.69231
A.K.
Thank you for your answer. But further calculations will be much more
difficult, like
(1-b)^2 * Var(V1) for all matching
Hi,
Try:
dat1<- read.table(text="
V1 V2 V3
2 6 8
4 3 4
1 9 8
",sep="",header=TRUE)
dat2<- read.table(text="
V1 V2 V3
6 8 4
2 0 7
8 1 3
",sep="",header=TRUE)
res1<- as.matrix(dat1-dat2)
res1
# V1 V2 V3
#[1,] -4 -2 4
#[2,] 2 3 -3
#[3,] -7 8 5
res2<-t(t(dat1)-colMeans(dat2))
res2
#
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