Statsmodels Poisson glm different than R

I am trying to fit some models (Spatial interaction models) according to some code which is provided in R. I have been able to get some of the code to work using statsmodels in a python framework but some of them do not match at all. I believe that the code I have for R and Python should give identical results. Does anyone see any differences? Or is there some fundamental differences which might be throwing things off? The R code is the original code which matches the numbers given in a tutorial (Found here: http://www.bartlett.ucl.ac.uk/casa/pdf/paper181).

R sample Code:

library(mosaic)
Data = fetchData('http://dl.dropbox.com/u/8649795/AT_Austria.csv')
Model = glm(Data~Origin+Destination+Dij+offset(log(Offset)), family=poisson(link="log"), data = Data)
cor = cor(Data$Data, Model$fitted, method = "pearson", use = "complete")
rsquared = cor * cor
rsquared

R output:

> Model = glm(Data~Origin+Destination+Dij+offset(log(Offset)), family=poisson(link="log"), data = Data)
Warning messages:
1: glm.fit: fitted rates numerically 0 occurred 
2: glm.fit: fitted rates numerically 0 occurred 
> cor = cor(Data$Data, Model$fitted, method = "pearson", use = "complete")
> rsquared = cor * cor
> rsquared
[1] 0.9753279

Python Code:

import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
from scipy.stats.stats import pearsonr

Data= pd.DataFrame(pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv'))
Model = smf.glm('Data~Origin+Destination+Dij', data=Data, offset=np.log(Data['Offset']), family=sm.families.Poisson(link=sm.families.links.log)).fit()

cor = pearsonr(doubleConstrained.fittedvalues, Data["Data"])[0]

print "R-squared for doubly-constrained model is: " + str(cor*cor)

Python Output:

R-squared for doubly-constrained model is: 0.104758481123

It looks like GLM has convergence problems here in statsmodels. Maybe in R too, but R only gives these warnings.

Warning messages:
1: glm.fit: fitted rates numerically 0 occurred 
2: glm.fit: fitted rates numerically 0 occurred

That could mean something like perfect separation in Logit/Probit context. I'd have to think about it for a Poisson model.

R is doing a better, if subtle, job of telling you that something may be wrong in your fitting. If you look at the fitted likelihood in statsmodels for instance, it's -1.12e27. That should be a clue right there that something is off.

Using Poisson model directly (I always prefer maximum likelihood to GLM when possible), I can replicate the R results (but I get a convergence warning). Tellingly, again, the default newton-raphson solver fails, so I use bfgs.

import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
from scipy.stats.stats import pearsonr

data= pd.DataFrame(pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv'))

mod = smf.poisson('Data~Origin+Destination+Dij', data=data, offset=np.log(data['Offset'])).fit(method='bfgs')

print mod.mle_retvals['converged']