Hi all,

Indeed there is a problem with the Halton function under the 64 bit  
architecture. For those who want to take a look, I put two csv files  
here http://dutangc.free.fr/pub/

I will ask Diethelm, the author of this Fortran code, to see what can  
be done.

Christophe

Le 15 sept. 2009 à 18:53, Anirban Mukherjee a écrit :

> I did not compile R. I used the Leopard installer from http://r.research.att.com/ 
>  which installs both the 64 bit and the 32 bit frame works and apps.
>
> There may be a problem with the gap between the two, but it seemed  
> like the xcode version recommended for Leopard was the same as the  
> xcode version for Snow Leopard, and I have the Fortran compiler from  
> the same website. I don't get any errors when compiling the package.
>
> Let me know if I can help. I am not a Fortran programmer, so  
> unfortunately cannot be helpful there. Thanks!
>
> Best,
> A
>
> On Sep 15, 2009, at 12:40 PM, Christophe Dutang wrote:
>
>> Ok so you mistype pseudo instead of quasi. My Email was useless... I
>> will get snow Leopard in the coming days and will try to reproduce
>> your problem. Did you compile R 64 yourself?
>>
>> iPhone.fan
>>
>> Le 15 sept. 2009 à 18:25, Anirban Mukherjee <anirbanm@smu.edu.sg> a
>> écrit :
>>
>> > Sorry, what I should have said was Halton numbers are quasi-random,
>> > and not pseudo-random. Quasi-random is the technically appropriate
>> > terminology.
>> >
>> > 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  
>> prime.
>> > 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  
>> expectation
>> > 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- 
>> random
>> > draws, the integration is better for the same N. (One uses the  
>> inverse
>> > distribution function.) For an example, you can look at Train's  
>> paper
>> > (page 4 and 5 have a good explanation) at:
>> >
>> > http://elsa.berkeley.edu/wp/train0899.pdf
>> >
>> > 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.
>> >
>> > Regards,
>> > Anirban
>> >
>> > 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@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)
>> >>   [1] -0.8740195  2.1827411 -0.1473012 -1.4406262  0.1820631
>> >> -1.3151244 -0.4813703  0.8177692
>> >>   [9]  0.2076117  1.8697418
>> >>  > rnorm(10)
>> >>   [1] -0.7725731  0.8696742 -0.4907099  0.1561859  0.5913528
>> >> -0.8441891  0.2285653 -0.1231755
>> >>   [9]  0.5190459 -0.7803617
>> >>  > rnorm(10)
>> >>   [1] -0.9585881 -0.0458582  1.1967342  0.6421980 -0.5290280
>> >> -1.0735112  0.6346301  0.2685760
>> >>   [9]  1.5767800  1.0864515
>> >>  > rnorm(10)
>> >>   [1] -0.60400852 -0.06611533  1.00787048  1.48289305  0.54658888
>> >> -0.67630052  0.52664127 -0.36449997
>> >>   [9]  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  
>> domain?
>> >>
>> >> --
>> >> David
>> >>
>> >>>   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.
>> >>>
>> >>> Thanks!
>> >>>
>> >>> Best,
>> >>> Anirban
>> >>>
>> >>> On Tue, Sep 15, 2009 at 9:01 AM, David Winsemius <dwinsemius@comcast.net
>> >>>> wrote:
>> >>>> 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  
>> expected,
>> >>>> almost symmetric unimodal distribution. The 64 bit version
>> >>>> created a
>> >>>> bimodal distribution with one large mode near 0 and another  
>> smaller
>> >>>> mode near 10E+37. Postcript output attached.
>> >>>>
>> >>>
>> >>>
>> >>>
>> >>> --
>> >>> Anirban Mukherjee | Assistant Professor, Marketing | LKCSB, SMU
>> >>> 5062 School of Business, 50 Stamford Road, Singapore 178899 |
>> >>> +65-6828-1932
>> >>>
>> >>> _______________________________________________
>> >>> R-SIG-Mac mailing list
>> >>> R-SIG-Mac@stat.math.ethz.ch
>> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mac
>> >>
>> >> David Winsemius, MD
>> >> Heritage Laboratories
>> >> West Hartford, CT
>> >>
>> >>
>> >
>> >
>> >
>> > --
>> > Anirban Mukherjee | Assistant Professor, Marketing | LKCSB, SMU
>> > 5062 School of Business, 50 Stamford Road, Singapore 178899 |  
>> +65-6828-1932
>> >
>> > _______________________________________________
>> > R-SIG-Mac mailing list
>> > R-SIG-Mac@stat.math.ethz.ch
>> > https://stat.ethz.ch/mailman/listinfo/r-sig-mac
>>
>

--
Christophe Dutang
Ph.D. student at ISFA, Lyon, France
website: http://dutangc.free.fr







	[[alternative HTML version deleted]]

