Hi Phil,
thanks, that already helps: Reduce() gets me the means I need!
Unfortunately, Reduce() apparently won't help me with trimming or
winsorizing the means, judging from its "successively" philosophy... Any
other ideas out there?
Best,
Stephan
Phil Spector schrieb:
Stephan -
If you try to apply mean directly to a matrix, it will just return a
scalar. It's easy to get row means and column
means, but to preserve each element of the matrix, I think
it's better to do the computations directly:
Reduce('+',foo) / length(foo)
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Tue, 30 Dec 2008, 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
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