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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