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median
2003 Oct 29
3
long algo
...ctical and straight forward.
However (the inevitable however), I am finding it very
slow for one of my operations:
it s basically an itertation over i and j in a pretty
big table (4* 4608). It takes 30 minutes!!!!
Thanks
Ps:if it can help here is the source:
median1<-matrix(nrow=4608,ncol=1)
median2<-matrix(nrow=4608,ncol=1)
median3<-matrix(nrow=4608,ncol=1)
median4<-matrix(nrow=4608,ncol=1)
v<-c(18,19,20,21,23)
for (i in 0:11)
{
for (j in 1:384)
{
median1[j+(i*384),]<-puce[j+(i*384),5]+median(puce[v+384*i,2]-puce[v+384*i,5])
median2[j+(i*...
2006 Apr 10
2
Suggestions to speed up median() and has.na()
...environment: namespace:stats>
Suggestion 1:
Replace the sort() calls with the .Internal(psort(x, partial)). This
will avoid unnecessary overhead, especially an expensive second check
for NAs using any(is.na(x)). Simple benchmarking with
x <- rnorm(10e6)
system.time(median(x))/system.time(median2(x))
where median2() is the function with the above replacements, gives
about 20-25% speed up.
Suggestion 2:
Create a has.na(x) function to replace any(is.na(x)) that returns TRUE
as soon as a NA value is detected. In the best case it returns after
the first index with TRUE, in the worst case it...