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

I can add to unutbu's answer by outlining how to retrieve particular elements of the coefficients table including, crucially, the p-values.

def r_matrix_to_data_frame(r_matrix):
    """Convert an R matrix into a Pandas DataFrame"""
    import pandas as pd
    from rpy2.robjects import pandas2ri
    array = pandas2ri.ri2py(r_matrix)
    return pd.DataFrame(array,
                        index=r_matrix.names[0],
                        columns=r_matrix.names[1])

# Let's start from unutbu's line retrieving the coefficients:
coeffs = R.summary(M).rx2('coefficients')
df = r_matrix_to_data_frame(coeffs)

This leaves us with a DataFrame which we can access in the normal way:

In [179]: df['Pr(>|t|)']
Out[179]:
(Intercept)    0.637618
x              0.104088
Name: Pr(>|t|), dtype: float64

In [181]: df.loc['x', 'Pr(>|t|)']
Out[181]: 0.10408803866182779