I am a bit confused now I am using rinterface and not robjects so how do I
managed to do the same with the rinterface? If I try to multiply two
IntSexpVectors I just get a combination of vectors as in python rather than
an actual sum of the two as in R.
2012/2/23 Laurent Gautier <lgaut...@gmail.com>
> On 2012-02-23 18:16, Bruno Santos wrote:
>
> Thank you Laurent for your quick reply.
>
> The if k[i] == 0 in R is implement as being + p * (x == 0) which will
> add p if k[i]==0 or zero if not. I was trying to came up with a closer
> implementation but I cannot do sums or multiplications between Robjects is
> that right?
>
>
> sum(log((1-p) * dnbinom(x, mu = mu, size = size, log = FALSE) + p * (x ==
> 0)))
>
>
> (rlog(1-p).ro * dnbinom(x, mu = mu, size = size, log =
> rinterface.FALSE)).ro + (p.ro * (x.ro == 0))
>
> ...or something is that style (I did not test the line above). That's
> still a lot at the robjects level, and moving down to rinterface should be
> a little faster.
>
>
>
>
> for elem in dnbinom(x=k, mu=mu, size=size,
> log=rinterface.BoolSexpVector([False])):
> if k[i] == 0:result+=rlog((1-p)*elem+p)[0]
> else: result+=rlog((1-p)*elem)[0]
>
>
>
>
>
>
> 2012/2/23 Laurent Gautier <lgaut...@gmail.com>
>
>> On 2012-02-23 17:57, Bruno Santos wrote:
>>
>> Hello everyone'
>>
>> I am trying to port an R function to estimate some parameters to the
>> rpy2 but the performance in python is appaling. So I was wondering if there
>> is a best way of doing this. What I have at the moment is:
>> R code
>> finbinom <-function(pars,x)
>> {
>> p<-pars[1]
>> mu <- pars[2]
>> size <- pars[3]
>> if(any(pars <= 0)) return(NA)
>> if(p >= 1) return(NA)
>> sum(log((1-p) * dnbinom(x, mu = mu, size = size, log = FALSE) + p * (x
>> == 0)))
>> }
>>
>> opNB <- optim(par = c(0.9, 100, 1), fn = finbinom, control =
>> list(fnscale = -1), x = distances)
>>
>> Python
>> def zero_inflated(Pars,k):
>> p,mu,size = Pars
>> if p<=0 or mu<=0 or size<=0:return rinterface.NA_Real
>> if p>=1:return rinterface.NA_Real
>> i=0
>> result=0
>> #return rsum(rlog(rinterface.FloatSexpVector([1-p]) * dnbinom(k, mu =
>> mu, size = size, log = rinterface.BoolSexpVector([False]))))
>>
>> for elem in dnbinom(x=k, mu=mu, size=size,
>> log=rinterface.BoolSexpVector([False])):
>> if k[i] == 0:result+=rlog((1-p)*elem+p)[0]
>> else: result+=rlog((1-p)*elem)[0]
>>
>>
>> if size is large, this is potentially a bottleneck. Note that when
>> comparing the performances with pure R one should consider comparing
>> identical code: the R bit doesn't seem to have the conditional branch if
>> k[i] == 0 (or did I miss something).
>>
>>
>> return result
>> cost_zinb = rinterface.rternalize(zero_inflated)
>> start_params = [0.9, 100, 1]
>> res = stats.optim(start_params, cost_zinb, control = rlist(fnscale = -1),
>> k=vDistances)
>> print 'Distances between sRNAs:',res[0],#res[1],res[2],res[3]
>>
>> Thank you very much in advance.
>> Best,
>> Bruno
>>
>>
>>
>> ------------------------------------------------------------------------------
>> Virtualization & Cloud Management Using Capacity Planning
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>>
>>
>>
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>>
>>
>
>
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Cloud computing makes use of virtualization - but cloud computing
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