Hi everyone,
I'm trying to use apply (with a call to zoo's rollapply within) on the
columns of a 1.5Kx165K matrix, and I'd like to make use of the other cores
on my machine to speed it up. (And hopefully also leave more memory free: I
find that after I create a big object like this, I have to save my
workspace and then close and reopen R to be able to recover memory tied up
by R, but maybe that's a separate issue -- if so, please let me know!)
It seems the package 'multicore' has a parallel version of 'lapply', which
I suppose I could combine with a 'do.call' (I think) to gather the elements
of the output list into a matrix, but I was wondering whether there might
be another route.
And, in case the particular way I constructed the call to 'apply' might be
the source of the problem, here is a deconstructed version of what I did to
each column, for easier parsing:
----------------------------- begin call to 'apply'
------------------------
Step 1: Identify several disjoint subsequences of fixed length, say length
three, of a column.
column.values <- 1:16
desired.subseqs <- c( NA, NA, NA, 1, 1, 1, NA, 1, 1, 1, NA, NA, 1,1,1, NA
) # this vector is used for every column.
desired.values <- desired.subseq * column.values
Step 2: Find the average value of each subsequence.
desired.means <- rollapply( desired.values, 3, mean, fill=NA, align =
"right", na.rm = FALSE) # put mean in highest index of subsequence and
retain original vector length
desired.means
[1] NA NA NA NA NA 5 NA NA NA 9 NA NA NA NA NA 14 NA
Step 3: Shift values forward by one index value, retaining original
vector length.
desired.means <- zoo( desired.means ) # in order to be able to use lag.zoo
desired.means <- lag( desired.means, k = -1, na.pad = TRUE)
desired.means
[1] NA NA NA NA NA NA 5 NA NA NA 9 NA NA NA NA 14
Step 4: Use last-observation-carried-forward, retaining original vector
length.
desired.means <- na.locf( desired.means, na.rm = FALSE )
desired.means
[1] NA NA NA NA NA NA 5 5 5 5 9 9 9 9 9 14
Step 5: Use next-observation-carried-backward to assign values to initial
sequence of NAs.
desired.means <- na.locf( desired.means, fromLast = TRUE)
desired.means
[1] 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 14
Step 6: Convert back to vector (from zoo object), and subtract from column.
desired.column <- vector.values - coredata(desired.means)
desired.column
[1] -4 -3 -2 -1 0 1 2 3 4 5 2 3 4 5 6 2
----------------------------- end call to 'apply' ------------------------
Thanks,
David
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