[Rcpp-devel] Favourite Rcpp examples for newbies ?

[Jonathan Olmsted]

I think I've seen this somewhere out there on the webs...

http://dirk.eddelbuettel.com/blog/2011/07/14/



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J.P. Olmsted

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On Thu, Aug 1, 2013 at 11:31 AM, Jonathan Olmsted <jolmsted at princeton.edu>wrote:

> Just my quick thoughts:
>
> Bayesian MCMC is what brought me to Rcpp. So, I have always found those
> examples the most compelling. This is precisely because good R coding can't
> improve performance on these problems and the gain in computational
> performance really justified the development cost as opposed to cases where
> you come out with a time profit only in the *very* long-run.
>
> In particular, I usually work with models that are conceptually similar to
> factor analysis (i.e., nothing on the "right-hand side is observed"). This
> is relevant because the next place one might go if R is slow for these
> problems is JAGS. Or maybe it's the first place you go. I'm not sure.
> Anyway, JAGS (as of when I last looked into it) doesn't handle the things I
> was doing efficiently. It used a less efficient sampler because it didn't
> detect that conditional on every other parameter, I was updating something
> that came from a Normal dist. So, with just a vanilla Gibbs step, I could
> out perform JAGS' slice sampler (where efficiency = increase in Effective
> Sample Size / time).
>
> I settled on Rcpp for these problems before Stan was really out there, so
> I haven't doubled-back to see how Stan would factor in to someone's
> decision-making process. Taken together:
>
>    1. If you are doing MCMC in R for a model that isn't packaged, you
>    have to code it by hand
>    2. Even if your R code is efficient, your runtime will be slow
>    3. Outside options don't get you too much improvement (e.g. JAGS), but
>    they get you some
>    4. For some problems, that just isn't fast enough and Rcpp-based C++
>    code can help you get you a lot more speedup with a reduced learning curve
>
> Particularly nice about this class of problems is that it
> "straightforward" on the implementation side. It focuses on computation
> (not advanced features of C++ or the nuances of R objects), the algorithm
> is conceptually simple, and you don't have to rely on boost or armadillo or
> anything beyond basic C++. Lastly, with all the recent sugar, MCMC Rcpp
> code is veryR-like.
>
> Also, while it's never a complaint of mine, there is often some conceptual
> hurdle for folks looking at an example like simulating pi and seeing how it
> applies to their work. At least with MCMC, it is a bit closer to a real
> application. I'm not saying it's anyone else's job to jump that hurdle for
> them, but if it can be removed for free, why not?
>
> Apologies for long-windedness.
>
> -Jonathan
>
>
>
>
> -------------------------------------------------------------------------
> J.P. Olmsted
>
> 029 Corwin (Office)
> 130 Corwin Hall (Mail)
>
> Politics Department
> Princeton University
> Princeton, NJ 08544
>
>
> t: 609.258.6202
> f: 609.258.1110
> jolmsted at princeton.edu
> http://about.me/olmjo
> -------------------------------------------------------------------------
>
>
> On Thu, Aug 1, 2013 at 10:19 AM, Dirk Eddelbuettel <edd at debian.org> wrote:
>
>>
>> I'm giving a talk to the Chicago RUG that is limited to 30 mins, and I
>> would
>> like to include some nice examples (besides standards like Fibonacci and
>> SimulatingPi).  The other talk is on ggplot(2), so the house may be full
>> with
>> new users / non-C++ hackers.
>>
>> What examples should I talk about?  Bonus points for links for list
>> questions, StackOverflow questions, or Rcpp Gallery posts.
>>
>> Dirk
>>
>> --
>> Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com
>> _______________________________________________
>> Rcpp-devel mailing list
>> Rcpp-devel at lists.r-forge.r-project.org
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>>
>
>
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