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multivariate way to aaply on different arrays

2 messages · Jannis

Jannis
# 
Dear R community,


is there any efficient way to use aaply on different datacubes? I have 3 
dimesniolan datacubes/arrays with dimensions lon x lat x time. Now I 
would like to do caclulations on each individual time series (e.g. all 
vectors along the third dimension) using a time series (or more) in the 
same location on another (identically shaped) array.

What would be the most efficient way to do this? Is there any 
multivariate version of aaply? Or other functions? Or some memory 
efficient and fast way (the datacubes are huge!) to do this by hand?


Thanks a lot

Jannis
4 days later
Jannis
# 
Hi R gurus,


just in case anybody else has a similar problem ... I have programmed a 
function that solves this problem by creating a higher dimensional array 
out of the individual variables. I have no idea though whether this is 
very efficient. Feel welcome to comment in case you think that there are 
more efficient ways to solve this.

Best
Jannis


maaply <- structure(function(
##title<< multivariate version of aaply
##description<< apply a function to slices, vectors in different 
(multivariate)
##              multidimensional datacubes.
    ...          ##<< arrays: input arrays
    , margins    ##<< margins/dimensions along which to split the arrays 
(see help of apply).
    , fun        ##<< function to apply to each cutout from the arrays. 
  (see help of apply)
                 ##   Has to have only ... as an input.
    , parallel = TRUE ##<< logical: whether to parallelize the 
calculation (see help of aaply)
    )
##seealso<<
##\code{\link{apply}}, \code{\link{aaply}}
{
   require(abind)
   require(plyr)
   data <- abind(..., along =.5)
   fun.internal <- function(data) {
     results <- do.call(fun, alply(data, 1, function(x)return(x)))
     return(results)
   }
   return(aaply(.data = data, .margins = c(margins + 1), .fun = 
fun.internal, .parallel = parallel))
}, ex = function(){
   #example datacubes
   data1 <- aperm((array(rep(1:100, each = 10), dim = c(10,10,10))), 
c(2,3,1))
   data2 <- array(rep(2, 10^3), c(10,10,10))
   data3 <- data1

   #example function
   fun = function(...) {
     dummy <- list(...)
     return(sum(dummy[[1]]) / mean(dummy[[2]]) + mean(dummy[[3]]))
   }

   #call to function
   results <- maaply(data1,data2,data3, margins = c(1,2), fun = fun)
})
On 18.10.2012 17:13, Jannis wrote: