Minimal example of rpy2 regression using pandas data frame

Question

What is the recommended way (if any) for doing linear regression using a pandas dataframe? I can do it, but my method seems very elaborate. Am I making things unnecessarily

Answer 1

The R and Python are not strictly identical because you build a data frame in Python/rpy2 whereas you use vectors (without a data frame) in R.

Otherwise, the conversion shipping with rpy2 appears to be working here:

from rpy2.robjects import pandas2ri
pandas2ri.activate()
robjects.globalenv['dataframe'] = dataframe
M = stats.lm('y~x', data=base.as_symbol('dataframe'))

The result:

>>> print(base.summary(M).rx2('coefficients'))
            Estimate Std. Error  t value  Pr(>|t|)
(Intercept)      0.6  1.1489125 0.522233 0.6376181
x                0.8  0.3464102 2.309401 0.1040880