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 thereI've got two datasets of the following form (just an example, the
real dataset got a lot more columns)dataset1V1 V2 V32 6 84
3 41 9 8and
dataset 2V1 V2 V36 8 42 0 78 1 3First,
I'd like to calculate the
followin
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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