我正在尝试根据 R 中提供的一些代码来拟合一些模型(空间交互模型)。我已经能够让一些代码在 python 框架中使用 statsmodels 工作,但其中一些根本不匹配。我相信我为 R 和 Python 编写的代码应该给出相同的结果。有没有人看到任何差异?或者是否有一些根本的差异可能会导致事情失败?R 代码是与教程中给出的数字相匹配的原始代码(在此处找到:http ://www.bartlett.ucl.ac.uk/casa/pdf/paper181 )。
R示例代码:
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
输出:
> 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
蟒蛇代码:
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)
蟒蛇输出:
R-squared for doubly-constrained model is: 0.104758481123