But why do you want the same seed for the different features? That is not
the right way to use stochastic methods.
Best,
Kasper
On Tue, Mar 12, 2019 at 5:20 PM Bemis, Kylie <k.be...@northeastern.edu>
wrote:
> Hi all,
>
> I remember similar questions coming up before, but couldn’t track any down
> that directly pertain to my situation.
>
> Suppose I want to use bplapply() in a function to fit models to many
> features, and I am applying over features. The models are stochastic, and I
> want the results to be reproducible, and preferably use the same RNG seed
> for each feature. So I could do:
>
> fitModels <- function(object, seed=1, BPPARAM=bpparam()) {
> bplapply(object, function(x) {
> set.seed(seed)
> fitModel(x)
> }, BPPARAM=BPPARAM)
> }
>
> But the BioC guidelines say not to use set.seed() inside function code,
> and I’ve seen other questions answered saying not to use “seed” as a
> function parameter in this way.
>
> Is it preferable to check and modify .Random.seed directly, or is there
> some other standard way of doing this?
>
> Thanks,
> Kylie
>
> ~~~
> Kylie Ariel Bemis
> Khoury College of Computer Sciences
> Northeastern University
> kuwisdelu.github.io<https://kuwisdelu.github.io>
>
>
>
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> Bioc-devel@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/bioc-devel
>
[[alternative HTML version deleted]]
_______________________________________________
Bioc-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/bioc-devel
