On Wed, Jan 5, 2011 at 1:22 PM, David Winsemius <dwinsem...@comcast.net> wrote:
>
> On Jan 5, 2011, at 10:03 AM, Young Cho wrote:
>
>> Hi,
>>
>> I am doing some simulations and found a bottle neck in my R script. I made
>> an example:
>>
>>> a = matrix(rnorm(5000000),1000000,5)
>>> tt = Sys.time(); sum(a[,1]*a[,2]*a[,3]*a[,4]*a[,5]); Sys.time() - tt
>>
>> [1] -1291.026
>> Time difference of 0.2354031 secs
>>>
>>> tt = Sys.time(); sum(apply(a,1,prod)); Sys.time() - tt
>>
>> [1] -1291.026
>> Time difference of 20.23150 secs
>>
>> Is there a faster way of calculating sum of products (of columns, or of
>> rows)?
>
> You should look at crossprod and tcrossprod.
Hmm. Not sure that would help, David. You could use a matrix
multiplication of a %*% rep(1, ncol(a)) if you wanted the row sums but
of course you could also use rowSums to get those.
>> And is this an expected behavior?
>
> Yes. For loops and *apply strategies are slower than the proper use of
> vectorized functions.
To expand a bit on David's point, the apply function isn't magic. It
essentially loops over the rows, in this case. By multiplying columns
together you are performing the looping over the rows in compiled
code, which is much, much faster. If you want to do this kind of
operation effectively in R for a general matrix (i.e. not knowing in
advance that it has exactly 5 columns) you could use Reduce
> a <- matrix(rnorm(5000000),1000000,5)
> system.time(pr1 <- a[,1]*a[,2]*a[,3]*a[,4]*a[,5])
user system elapsed
0.15 0.09 0.37
> system.time(pr2 <- apply(a, 1, prod))
user system elapsed
22.090 0.140 22.902
> all.equal(pr1, pr2)
[1] TRUE
> system.time(pr3 <- Reduce(get("*"), as.data.frame(a), rep(1, nrow(a))))
user system elapsed
0.410 0.010 0.575
> all.equal(pr3, pr2)
[1] TRUE
>>
>> Thanks for your advice in advance,
>>
>
> --
>
> David Winsemius, MD
> West Hartford, CT
>
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