Bert Gunter wrote:
>
> Serves me right, I suppose. Timing seems also very dependent on the
> dimensions of the matrix. Here's what I got with my inadequate test:
>
>
>> x <- matrix(rnorm(3e5),ncol=3)
>>
> ## via apply
>
>> system.time(apply(x,1,max))
>>
> user system elapsed
> 2.09 0.02 2.10
>
> ## via pmax
>
>> system.time(do.call(pmax,data.frame(x)))
>>
> user system elapsed
> 0.10 0.02 0.11
>
>
>
yes, similar to what i got. but with the transpose, the ratio is way
more than inverted:
waku = expression(matrix(apply(m, 1, max), nrow(m)))
bert = expression(do.call(pmax, data.frame(m)))
library(rbenchmark)
m = matrix(rnorm(1e6), ncol=10)
benchmark(replications=10, columns=c('test', 'elapsed'),
order='elapsed',
waku=waku,
bert=bert)
# test elapsed
# 2 bert 1.633
# 1 waku 9.974
m = t(m)
benchmark(replications=10, columns=c('test', 'elapsed'),
order='elapsed',
waku=waku,
bert=bert)
# test elapsed
# 1 waku 0.507
# 2 bert 27.261
> Draw your own conclusions!
>
my favourite: you should have specified what 'large matrices' means.
vQ
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