我不确定为什么我对简单的OLS获得稍微不同的结果，具体取决于我是通过panda's experimental rpy interface在R中执行回归还是在Python中使用statsmodels。
import pandas
from rpy2.robjects import r
from functools import partial
loadcsv = partial(pandas.DataFrame.from_csv,
index_col="seqn", parse_dates=False)
demoq = loadcsv("csv/DEMO.csv")
rxq = loadcsv("csv/quest/RXQ_RX.csv")
num_rx = {}
for seqn, num in rxq.rxd295.iteritems():
try:
val = int(num)
except ValueError:
val = 0
num_rx[seqn] = val
series = pandas.Series(num_rx, name="num_rx")
demoq = demoq.join(series)
import pandas.rpy.common as com
df = com.convert_to_r_dataframe(demoq)
r.assign("demoq", df)
r('lmout
r('print(summary(lmout))') # print from R从R起，我得到以下总结：
Call:
lm(formula = demoq$num_rx ~ demoq$ridageyr)
Residuals:
Min 1Q Median 3Q Max
-2.9086 -0.6908 -0.2940 0.1358 15.7003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1358216 0.0241399 -5.626 1.89e-08 ***
demoq$ridageyr 0.0358161 0.0006232 57.469 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.545 on 9963 degrees of freedom
Multiple R-squared: 0.249, Adjusted R-squared: 0.2489
F-statistic: 3303 on 1 and 9963 DF, p-value: < 2.2e-16使用statsmodels.api执行OLS：
import statsmodels.api as sm
results = sm.OLS(demoq.num_rx, demoq.ridageyr).fit()
results.summary()结果与R的输出相似，但不一样：
OLS Regression Results
Adj. R-squared: 0.247
Log-Likelihood: -18488.
No. Observations: 9965 AIC: 3.698e+04
Df Residuals: 9964 BIC: 3.698e+04
coef std err t P>|t| [95.0% Conf. Int.]
ridageyr 0.0331 0.000 82.787 0.000 0.032 0.034安装过程有点麻烦。但是，有一个ipython笔记本here，可以重现不一致性。