Hi Jim,
This is genotype data of 170 samples. I selected subsets of SNP
optimized for different types of germplasm. So it is a matrix with 170
rows and 1536, 384 or 96 columns of binary data (0, 1). I have 14 of
such matrices in a list.
x <- list()
for (i in 1:14) {
set.seed(i)
x[[i]] <- ma
I have no idea of what your data looks like, so using random numbers and
only going for nr=1, after about a minute I stopped it. Here is what Rprof
showed:
/cygdrive/c/perf: perl c:/perf/bin/readRprof.pl Rprof.out 1
0 75.8 root
1. 75.7 sapply
2. . 75.7 lapply
3. . . 75.7 FUN
4. .
first preallocate 'm' to the max (m <- numeric(nr)) and then run Rprof to
see where time is being spent. Since there was not reproducible data
provided, it is hard to analyze beyond this point. Time is probably being
spent in one of the functions
On Tue, Jun 10, 2008 at 4:49 AM, Marc <[EMAIL PRO
Hi,
I have the following function that I want to apply to a list of 14
matrices (1536 x 170) of binary data:
DRes <- function(x, nr = 1, metric = "mixed", ...) {
require(analogue)
require(ade4)
m <- c()
for (i in 1:nr) {
set.seed(i)
x1 <- x[, sample(dimnames(x)[[2]], length(x[1,]
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