Difference(s) between scipy.stats.linregress, numpy.polynomial.polynomial.polyfit and statsmodels.api.OLS
It seems all three functions can do simple linear regression, e.g.
scipy.stats.linregress(x, y)
numpy.polynomial.polynomial.polyfit(x, y, 1)
x = statsmodels.api.add_constant(x)
statsmodels.api.OLS(y, x)
I wonder if there is any real difference between the three methods? I know that statsmodels are built on top of scipy, and scipy is kinda dependent on numpy for many things, so I expect that they should not differ vastly, but devil is always in the details.
More specifically, if we use the numpy method above, how do we get the p-value of the slope which is given by default by the other two methods?
I am using them in Python 3, if that makes any difference.
Answer
The three are very different but overlap in the parameter estimation for the very simple example with only one explanatory variable.
By increasing generality:
scipy.stats.linregress only handles the case of a single explanatory variable with specialized code and calculates a few extra statistics.
numpy.polynomial.polynomial.polyfit estimates the regression for a polynomial of a single variable, but doesn't return much in terms of extra statisics.
statsmodels OLS is a generic linear model (OLS) estimation class. It doesn't prespecify what the explanatory variables are and can handle any multivariate array of explanatory variables, or formulas and pandas DataFrames. It not only returns the estimated parameters, but also a large set of results staistics and methods for statistical inference and prediction.
For completeness of options for estimating linear models in Python (outside of Bayesian analysis), we should also consider scikit-learn LinearRegression and similar linear models, which are useful for selecting among a large number of explanatory variables but does not have the large number of results that statsmodels provides.