[Rd] [R-SIG-Mac] rnorm.halton
simon.urbanek at r-project.org
Sat Oct 10 20:02:24 CEST 2009
I forgot to answer the second part of your e-mail -- see below.
On Oct 10, 2009, at 12:04 PM, Christophe Dutang wrote:
> Hi all,
> I need to transform classic 32bit Fortran code to 64bit Fortran
> code, see the discussion [R-SIG-Mac] rnorm.halton. But I'm clearly a
> beginner in Fortran...
> Does someone already do this for his package?
> From here, http://techpubs.sgi.com/library/tpl/cgi-bin/getdoc.cgi?coll=linux&db=bks&fname=/SGI_Developer/Porting_Guide/ch03.html
> , I identify the following changes (Fortran types) to the Fortran
> - INTEGER to INTEGER*8
> - REAL*8 to REAL*16
> The code I would like to change is available on R forge here : http://r-forge.r-project.org/plugins/scmsvn/viewcvs.php/pkg/randtoolbox/src/LowDiscrepancy.f?rev=4229&root=rmetrics&view=markup
> Another question I have is how do I tell the R code to use the 64bit
> version of my code on 64bit machine?
> In the current implementation, the file quasiRNG.R calls directly
> the Fortran code with .Fortran.
> How could I use the 64bit version directly in the R code?
You don't have a choice -- whether 32 or 64 bits are used is defined
by R, because you cannot mix 32-bit and 64-bit code. So in 64-bit R
your package will be compiled into 64-bit. In 32-bit R your package
will be compiled in 32-bit. You can see which R you are using with
8 * .Machine$sizeof.pointer
(Note: The Mac binaries we provide are multi-arch so they [the Leopard
build] do in fact include both 32-bit and 64-bit -- which one gets
started depends on the --arch flag, see R docs for details on multi-
> I suspect I need to have a quasiRNG.c file where I will use
> preprocessor statement that will select the good version of the
> function to call. Is that correct?
No (see previous e-mail).
> Thanks in advance
> Le 15 sept. 2009 à 18:25, Anirban Mukherjee a écrit :
>> Sorry, what I should have said was Halton numbers are quasi-random,
>> and not pseudo-random. Quasi-random is the technically appropriate
>> Halton sequences are low discrepancy: the subsequence looks/smells
>> random. Hence, they are often used in quasi monte carlo simulations.
>> To be precise, there is only 1 Halton sequence for a particular
>> Repeated calls to Halton should return the same numbers. The first
>> column is the Halton sequence for 2. the second for 3, the third
>> for 5
>> and so on using the first M primes (for M columns). (You can also
>> scramble the sequence to avoid this.)
>> I am using them to integrate over a multivariate normal space. If you
>> take 1000 random draws, then sum f() over the draws is the
>> of f(). If f() is very non-linear (and/or multi-variate) then even
>> with large N, its often hard to get a good integral. With quasi-
>> draws, the integration is better for the same N. (One uses the
>> distribution function.) For an example, you can look at Train's paper
>> (page 4 and 5 have a good explanation) at:
>> In the context of simulated maximum likelihood estimation, such
>> integrals are very common. Of course true randomness has its own
>> place/importance: its just that quasi-random numbers can be very
>> useful in certain contexts.
>> PS: qnorm(halton()) gets around the problem of the random deviates
>> not working.
>> On Tue, Sep 15, 2009 at 11:37 AM, David Winsemius
>> <dwinsemius at comcast.net> wrote:
>>> On Sep 15, 2009, at 11:10 AM, Anirban Mukherjee wrote:
>>>> Thanks everyone for your replies. Particularly David.
>>>> The numbers are pseudo-random. Repeated calls should/would give the
>>>> same output.
>>> As I said, this package is not one with which I have experience. It
>>> has _not_ however the case that repeated calls to (typical?) random
>>> number functions give the same output when called repeatedly:
>>> > rnorm(10)
>>>  -0.8740195 2.1827411 -0.1473012 -1.4406262 0.1820631
>>> -1.3151244 -0.4813703 0.8177692
>>>  0.2076117 1.8697418
>>> > rnorm(10)
>>>  -0.7725731 0.8696742 -0.4907099 0.1561859 0.5913528
>>> -0.8441891 0.2285653 -0.1231755
>>>  0.5190459 -0.7803617
>>> > rnorm(10)
>>>  -0.9585881 -0.0458582 1.1967342 0.6421980 -0.5290280
>>> -1.0735112 0.6346301 0.2685760
>>>  1.5767800 1.0864515
>>> > rnorm(10)
>>>  -0.60400852 -0.06611533 1.00787048 1.48289305 0.54658888
>>> -0.67630052 0.52664127 -0.36449997
>>>  0.88039397 0.56929333
>>> I cannot imagine a situation where one would _want_ the output to be
>>> the same on repeated calls unless one reset a seed. Unless perhaps I
>>> am not understanding the meaning of "random" in the financial
>>>> Currently, Halton works fine when used to just get the
>>>> Halton sequence, but the random deviates call is not working in
>>>> 64 bit
>>>> R. For now, I will generate the numbers in 32 bit R, save them and
>>>> then load them back in when using 64 bit R. The package maintainers
>>>> can look at it if/when they get a chance and/or access to 64 bit R.
>>>> On Tue, Sep 15, 2009 at 9:01 AM, David Winsemius <dwinsemius at comcast.net
>>>>> I get very different output from the two versions of Mac OSX R as
>>>>> well. The 32 bit version puts out a histogram that has an
>>>>> almost symmetric unimodal distribution. The 64 bit version
>>>>> created a
>>>>> bimodal distribution with one large mode near 0 and another
>>>>> mode near 10E+37. Postcript output attached.
>>>> Anirban Mukherjee | Assistant Professor, Marketing | LKCSB, SMU
>>>> 5062 School of Business, 50 Stamford Road, Singapore 178899 |
>>>> R-SIG-Mac mailing list
>>>> R-SIG-Mac at stat.math.ethz.ch
>>> David Winsemius, MD
>>> Heritage Laboratories
>>> West Hartford, CT
>> Anirban Mukherjee | Assistant Professor, Marketing | LKCSB, SMU
>> 5062 School of Business, 50 Stamford Road, Singapore 178899 |
>> R-SIG-Mac mailing list
>> R-SIG-Mac at stat.math.ethz.ch
> Christophe Dutang
> Ph.D. student at ISFA, Lyon, France
> website: http://dutangc.free.fr
> R-devel at r-project.org mailing list
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