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]
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
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