
hey guys, i've been following this discussion about the simulation, and being a beginner myself, im really unsure of the best method.

 

I hve the same problem as the initial one, except i need 1000 samples of size 15, and my distribution is Exp(1). I've adjusted some of the loop formulas for my n=15, but im unsure how to proceed in the quickest way.

 

Can someone please help?

 

Much appreciated :)
 
> From: r.turner@auckland.ac.nz
> Date: Thu, 14 May 2009 10:26:38 +1200
> To: carl@witthoft.com
> CC: r-help@r-project.org
> Subject: Re: [R] Simulation
> 
> 
> On 14/05/2009, at 10:04 AM, Carl Witthoft wrote:
> 
> > So far nobody seems to have warned the OP about seeding.
> >
> > Presumably Debbie wants 1000 different sets of samples, but as we all
> > know there are ways to get the same sequence (initial seed) every 
> > time.
> > If there's a starting seed for one of the "generate a single giant
> > matrix" methods proposed, the whole matrix will be the same for a 
> > given
> > seed.
> > If rnorm is called 1000 times (hopefully w/ different random (oops)
> > seeds), the entire set of samples will be different.
> >
> > and so on.
> 
> I really don't get this. The OP wanted 1000 independent samples,
> each of size 100. Whether she does
> 
> set.seed(42)
> M <- matrix(rnorm(100*1000),nrow=1000) # Each row is a sample.
> 
> or
> 
> L <- list()
> set.seed(42)
> for(i in 1:1000) L[[i]] <- rnorm(100) # Each list entry is a sample.
> 
> she gets this, i.e. the desired result. Setting a seed serves to make
> the results reproducible. This works via either approach. Making 
> results
> reproducible in this manner is advisable, but seed-setting is nothing 
> that the OP
> needs to be *warned* about.
> 
> cheers,
> 
> Rolf Turner
> 
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