Dear Matt,
Here are two ways:
# Data
p <- array(c(1:5, rep(NA, times = 3)), dim = c(5, 5, 3))
# First
res<-apply(p,3,function(X)
c(scols=apply(X,2,sd,na.rm=TRUE),srows=apply(X,2,sd,na.rm=TRUE))
)
res
# Second
res2<-apply(p,3,function(X)
list(scols=apply(X,2,sd,na.rm=TRUE),srows=apply(X,1,sd,na.rm=TRUE))
)
lapply(res2,function(x) do.call(rbind,x))
HTH,
Jorge
On Wed, Feb 25, 2009 at 10:53 AM, Matt Oliver <moli...@udel.edu> wrote:
> Dear help, suppose I have this array and want to compute sd aross rows and
> columns.
>
> p <- array(c(1:5, rep(NA, times = 3)), dim = c(5, 5, 3))
>
> apply(p, 1:2, sd) fails because sd requires at least 2 numbers to compute
> sd
>
> apply(p, 1:2, sd, na.rm = TRUE) fails for the same reason
>
> I crafted my own function that does what I want
>
> sd_fun <- function(i){
> if(sum(!is.na(i))==0){
> temp.sd <- NA
> }else{
> temp.sd <- sd(i, na.rm = TRUE)
> }
> return(temp.sd)
> }
>
>
> apply(p, 1:2, sd_fun)
>
> This does what I want, but when I scale up to large arrays like
>
> pp <- array(c(1:5, rep(NA, times = 3)), dim = c(1000, 1000, 60))
>
> the apply function takes a long time to run.
>
> Is there a faster, more efficient way to do this?
>
> Thanks in advance
>
> Matt
>
>
> --
> Matthew J. Oliver
> Assistant Professor
> College of Marine and Earth Studies
> University of Delaware
> 700 Pilottown Rd.
> Lewes, DE, 19958
> 302-645-4079
> http://www.ocean.udel.edu/people/profile.aspx?moliver
>
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>
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