For the record, low bitcount RNGs are relatively sensitive to initialization
[1] such that rules like the one you reference might make sense for specific
designs, but the default RNG used in R is much much more sophisticated than
that [2].
[1] http://daviddeley.com/random/random4.htm
[2] ?Rando
It doesn't matter. The whole point is to make the pseudo-random sequence
repeatable... unless you have a specific reason to avoid repeatability.
On December 22, 2018 5:33:39 PM PST, Steven Yen wrote:
>I have known from the old days to set a random seed of a LARGE ODD
>NUMBER. Now I read instruc
I have known from the old days to set a random seed of a LARGE ODD
NUMBER. Now I read instructions of set.seed and it requires ANY INTEGER.
Any idea? Or, does it matter. Thanks.
--
st...@ntu.edu.tw (S.T. Yen)
[[alternative HTML version deleted]]
__
On 03/04/2018 07:14 PM, Henrik Bengtsson wrote:
On Sun, Mar 4, 2018 at 3:23 PM, Duncan Murdoch wrote:
...
An issue is that .Random.seed doesn't contain the full state of the RNG
system, so restoring it doesn't necessarily lead to an identical sequence of
output. The only way to guarantee th
On Sun, Mar 4, 2018 at 3:23 PM, Duncan Murdoch wrote:
> On 04/03/2018 5:54 PM, Henrik Bengtsson wrote:
>>
>> The following helps identify when .GlobalEnv$.Random.seed has changed:
>>
>> rng_tracker <- local({
>>last <- .GlobalEnv$.Random.seed
>>function(...) {
>> curr <- .GlobalEnv$.R
On Sun, Mar 4, 2018 at 10:18 AM, Paul Gilbert wrote:
> On Mon, Feb 26, 2018 at 3:25 PM, Gary Black
> wrote:
>
> (Sorry to be a bit slow responding.)
>
> You have not supplied a complete example, which would be good in this case
> because what you are suggesting could be a serious bug in R or a pa
On 04/03/2018 5:54 PM, Henrik Bengtsson wrote:
The following helps identify when .GlobalEnv$.Random.seed has changed:
rng_tracker <- local({
last <- .GlobalEnv$.Random.seed
function(...) {
curr <- .GlobalEnv$.Random.seed
if (!identical(curr, last)) {
warning(".Random.seed
The following helps identify when .GlobalEnv$.Random.seed has changed:
rng_tracker <- local({
last <- .GlobalEnv$.Random.seed
function(...) {
curr <- .GlobalEnv$.Random.seed
if (!identical(curr, last)) {
warning(".Random.seed changed")
last <<- curr
}
TRUE
}
})
a
Thank you, everybody, who replied! I appreciate your valuable advise! I will
move the location of the set.seed() command to after all packages have been
installed and loaded.
Best regards,
Gary
Sent from my iPad
> On Mar 4, 2018, at 12:18 PM, Paul Gilbert wrote:
>
> On Mon, Feb 26, 2018 at
On Mon, Feb 26, 2018 at 3:25 PM, Gary Black
wrote:
(Sorry to be a bit slow responding.)
You have not supplied a complete example, which would be good in this
case because what you are suggesting could be a serious bug in R or a
package. Serious journals require reproducibility these days. For
I am willing to go out on that limb and say the answer to the OP question is
yes, the RN sequence in R should be reproducible. I agree though that it
doesn't look like he is actually taking care not to run code that would disturb
the generator.
--
Sent from my phone. Please excuse my brevity.
If your computations involve the parallel package then set.seed(seed)
may not produce repeatable results. E.g.,
> cl <- parallel::makeCluster(3) # Create cluster with 3 nodes on local
host
> set.seed(100); runif(2)
[1] 0.3077661 0.2576725
> parallel::parSapply(cl, 101:103, function(i)runif(2, i,
In case you don't get an answer from someone more knowledgeable:
1. I don't know.
2. But it is possible that other packages that are loaded after set.seed()
fool with the RNG.
3. So I would call set.seed just before you invoke each random number
generation to be safe.
Cheers,
Bert
Bert Gunte
Hi all,
For some odd reason when running naïve bayes, k-NN, etc., I get slightly
different results (e.g., error rates, classification probabilities) from run
to run even though I am using the same random seed.
Nothing else (input-wise) is changing, but my results are somewhat different
from run
14 matches
Mail list logo
