Hi Marc,
thanks, that works great! With yet another apply(), I can also make it
accept psych::winsor() and trimming:
some.sort.of.apply <- function ( foo, FUN, ... ) {
matrix(apply(sapply(foo, c),1,FUN,...), dim(foo[[1]]))
}
some.sort.of.apply ( foo, FUN=winsor,trim=0.3 )
Thank you all for your help!
Stephan
Marc Schwartz schrieb:
on 12/30/2008 08:33 AM Stephan Kolassa wrote:
Dear useRs,
I have a list, each entry of which is a matrix of constant dimensions.
Is there a good way (i.e., not using a for loop) to apply a mean to each
matrix entry *across list entries*?
Example:
foo <- list(rbind(c(1,2,3),c(4,5,6)),rbind(c(7,8,9),c(10,11,12)))
some.sort.of.apply(foo,FUN=mean)
I'm looking for a componentwise mean across the two entries of foo,
i.e., the following output:
[,1] [,2] [,3]
[1,] 4 5 6
[2,] 7 8 9
[NB. My "real" application involves trimming and psych::winsor(), so
anything that generalizes to this would be extra good.]
I've been looking at apply and {s,l,m,t}apply, by, with and aggregate
and searched the list archives... any ideas?
Thanks a lot,
Stephan
How about something like this:
matrix(rowMeans(sapply(foo, c)), dim(foo[[1]]))
[,1] [,2] [,3]
[1,] 4 5 6
[2,] 7 8 9
Essentially, first create matching elementwise rows:
sapply(foo, c)
[,1] [,2]
[1,] 1 7
[2,] 4 10
[3,] 2 8
[4,] 5 11
[5,] 3 9
[6,] 6 12
Then, get the row means:
rowMeans(sapply(foo, c))
[1] 4 7 5 8 6 9
Then finally restructure the result to a matrix, using the dimensions of
foo[[1]].
This of course makes the assumption that each matrix is of the same size
and does not otherwise check for that.
HTH,
Marc Schwartz
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