Thanks a lot to everybody who responded! My solution now looks similar
to Ruis and Davids suggestions.
Jannis
On 28.06.2013 11:00, Rui Barradas wrote:
Hello,
You can solve your problem using only base R, with no need for an
external package. The two instrucitons below are two ways of doing the
same.
sapply(split(testframe, indices), function(x) cor(x[, 1], x[, 2]))
as.vector(by(testframe, indices, function(x) cor(x[, 1], x[, 2])))
Hope this helps,
Rui Barradas
Em 28-06-2013 09:31, Jannis escreveu:
Yes, I had a look at that function. From the documentation, however, it
did not get clear to me how to split the dataframe into subsets of rows
based on an index argument. Like:
testframe <- data.frame(a=rnorm(100), b = rnorm(100))
indices <- rep(c(1,2), each = 50)
results <- ddply(.data = testframe, INDICES= indices, .fun = function(x)
corr(x[,1], x[,2]))
Where the last command would yield the correlations between column 1 and
2 of the first 50 and of the last 50 values.
Any ideas?
Jannis
On 27.06.2013 21:43, Greg Snow wrote:
Look at the plyr package, probably the ddply function in that
package. You
can write your own function to do whatever you want on the pieces of
the
split apart object. Correlation between a specified pair of columns
would
be simple.
On Thu, Jun 27, 2013 at 11:26 AM, Jannis <bt_jan...@yahoo.de> wrote:
Dear List members,
i am seeking a multivariate version of aggregate. I want to
compute, fro
example the correlation between subsets of two vectors. In aggregate,
i can
only supply one vector with indices for subsets. Is there ready
function
for this or do i need to program my own?
Cheers
Jannis
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