Thank you both for your advice. I ended up implementing both solutions and
testing them on a real dataset of 10,000 rows and 50 inds. The results are
very, very interesting.
For some context, the original two approaches, nested lapply and nested for
loops, performed at 1.501529
and 1.458963 mi
On Fri, Oct 8, 2010 at 12:47 PM, Gabor Grothendieck
wrote:
> On Fri, Oct 8, 2010 at 11:35 AM, epowell wrote:
>>
>> My data looks like this:
>>
>>> data
>> name G_hat_0_0 G_hat_1_0 G_hat_2_0 G_0 G_hat_0_1 G_hat_1_1 G_hat_2_1 G_1
>> 1 rs0 0.488000 0.448625 0.063375 1 0.480875 0.454500 0.0
On Fri, Oct 8, 2010 at 11:35 AM, epowell wrote:
>
> My data looks like this:
>
>> data
> name G_hat_0_0 G_hat_1_0 G_hat_2_0 G_0 G_hat_0_1 G_hat_1_1 G_hat_2_1 G_1
> 1 rs0 0.488000 0.448625 0.063375 1 0.480875 0.454500 0.064625 1
> 2 rs1 0.002375 0.955375 0.042250 1 0.00 0.06
You are loosing a lot of time by repeatedly calculating character
indices with paste() in every iteration. Two options:
-- 1) calculate these once outside the loop and then refer to them by
index
idx.names <- vector(mode="character", length=nind)
for (i in (0:(nind-1))) {idx[i+1] <-# ne
My data looks like this:
> data
name G_hat_0_0 G_hat_1_0 G_hat_2_0 G_0 G_hat_0_1 G_hat_1_1 G_hat_2_1 G_1
1 rs0 0.488000 0.448625 0.063375 1 0.480875 0.454500 0.064625 1
2 rs1 0.002375 0.955375 0.042250 1 0.00 0.062875 0.937125 2
3 rs2 0.050375 0.835875 0.113750
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