[Rd] [R-SIG-Mac] rnorm.halton

Sat Oct 10 19:56:16 CEST 2009

Christophe,

you're looking at the wrong docs -- normally there is nothing you need  
to change FORTRAN code when switching between 32-bit and 64-bit. There  
is no separate "64-bit code" or "32-bit code". None of the scalar  
types used by R in interfaces changes (int, double and INTEGER, REAL*8  
are both the same). You only need to change your code if you make  
assumptions about the size of pointers which increases from 4 bytes to  
8 bytes (and normally you should not be making those assumptions). I  
don't think you should touch any Fortran code.

For the C code, the only thing that changes (beside pointer size) is  
"long" data types. If you use those in your code, you must be aware  
that the corresponding size change. I suspect that if you have a  
discrepancy in your 32/64-bit binaries then you either have a bug  
somewhere (Fortran or C) or your C code is not 64-bit clean (i.e.  
makes some 32-bit assumptions that are not true in 64-bit environment  
- check the use of "long" data types and pointers). Again, the  
solution is to fix the code to be clean, it will still be the same for  
both 32-bit and 64-bit.

Cheers,
Simon

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  
> code:
>
>  - 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?
>
> 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?
>
> Thanks in advance
>
> Christophe
>
>
> 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
>> 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 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)
>>>  [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 at 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 at 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 at 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
>
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