Thanks for the suggestion. The solution below is much better than my
round-about way.
combn(outcomes, 2, list )
I can't do much about the speed of combn() so I wanted to trim the fat
wherever else I could.
C
On 17 November 2010 15:10, Charles C. Berry <cbe...@tajo.ucsd.edu> wrote:
>
> On Wed, 17 Nov 2010, Chris Carleton wrote:
>
>> Hi List,
>>
>> I'm hoping to get opinions for enhancing the efficiency of the following
>> code designed to take a vector of probabilities (outcomes) and calculate a
>> union of the probability space. As part of the union calculation, combn()
>> must be used, which returns a matrix, and the parallelized version of
>> lapply() provided in the multicore package requires a list. I've found that
>> parallelization is very necessary for vectors of outcomes greater in length
>> than about 10 or 15 elements, which is why I need to make use of multicore
>> (and, therefore, convert the combn() matrix to a list). It would speed the
>> process up if there was a more direct way to convert the columns of combn()
>> to elements of a single list.
>
>
> I think you are mistaken.
>
> Is this what Rprof() tells you?
>
> On my system, combn() is the culprit
>
>> Rprof()
>> outcomes <- 1:25
>> nada <- replicate(200, {apply(combn(outcomes,2),2,column2list);NULL})
>> Rprof(NULL)
>> summaryRprof()
>
> $by.self
> self.time self.pct total.time total.pct
> "combn" 0.64 61.54 0.70 67.31
> "apply" 0.20 19.23 1.04 100.00
> "FUN" 0.10 9.62 1.04 100.00
> "!=" 0.04 3.85 0.04 3.85
> "<" 0.02 1.92 0.02 1.92
> "-" 0.02 1.92 0.02 1.92
> "is.null" 0.02 1.92 0.02 1.92
>
>
> And it hardly takes any time at that!
>
>
> HTH,
>
> Chuck
>
> p.s. Isn't
>
> as.data.frame( combn( outcomes, 2 ) )
> or
> combn(outcomes, 2, list )
>
> good enough?
>
>
> Any constructive suggestions will be greatly
>>
>> appreciated. Thanks for your consideration,
>>
>> C
>>
>> code:
>> ------------
>> unionIndependant <- function(outcomes) {
>> intsctn <- c()
>> column2list <- function(x){list(x)}
>> pb <-
>> ProgressBar(max=length(outcomes),stepLength=1,newlineWhenDone=TRUE)
>> for (i in 2:length(outcomes)){
>> increase(pb)
>> outcomes_ <- apply(combn(outcomes,i),2,column2list)
>> for (j in 1:length(outcomes_)){outcomes_[[j]] <-
>> outcomes_[[j]][[1]]}
>> outcomes_container <- mclapply(outcomes_,prod,mc.cores=3)
>> intsctn[i] <- sum(unlist(outcomes_container))
>> }
>> intsctn <- intsctn[-1]
>> return(sum(outcomes) - sum(intsctn[which(which((intsctn %in% intsctn))
>> %% 2 == 1)]) + sum(intsctn[which(which((intsctn %in% intsctn)) %% 2 == 0)])
>> + ((-1)^length(intsctn) * prod(outcomes)))
>> }
>> ------------
>> PS This code has been tested on vectors of up to length(outcomes) == 25 and
>> it should be noted that ProgressBar() requires the R.utils package.
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> Charles C. Berry Dept of Family/Preventive Medicine
> cbe...@tajo.ucsd.edu UC San Diego
> http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
>
>
>
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