OLS using statsmodel.formula.api versus statsmodel.api

Question

Can anyone explain to me the difference between ols in statsmodel.formula.api versus ols in statsmodel.api?

Using the Advertising data from the ISLR text, I ran an ols using both, and got different results. I then compared with scikit-learn's LinearRegression.

import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression

df = pd.read_csv("C:\...\Advertising.csv")

x1 = df.loc[:,['TV']]
y1 = df.loc[:,['Sales']]

print "Statsmodel.Formula.Api Method"
model1 = smf.ols(formula='Sales ~ TV', data=df).fit()
print model1.params

print "\nStatsmodel.Api Method"
model2 = sm.OLS(y1, x1)
results = model2.fit()
print results.params

print "\nSci-Kit Learn Method"
model3 = LinearRegression()
model3.fit(x1, y1)
print model3.coef_
print model3.intercept_

The output is as follows:

Statsmodel.Formula.Api Method
Intercept    7.032594
TV           0.047537
dtype: float64

Statsmodel.Api Method
TV    0.08325
dtype: float64

Sci-Kit Learn Method
[[ 0.04753664]]
[ 7.03259355]

The statsmodel.api method returns a different parameter for TV from the statsmodel.formula.api and the scikit-learn methods.

What kind of ols algorithm is statsmodel.api running that would produce a different result? Does anyone have a link to documentation that could help answer this question?

Answer 1

The difference is due to the presence of intercept or not:

• in statsmodels.formula.api, similarly to the R approach, a constant is automatically added to your data and an intercept in fitted

• in statsmodels.api, you have to add a constant yourself (see the documentation here). Try using add_constant from statsmodels.api

  x1 = sm.add_constant(x1)
  

Answer 2

Came across this issue today and wanted to elaborate on @stellasia's answer because the statsmodels documentation is perhaps a bit ambiguous.

Unless you are using actual R-style string-formulas when instantiating OLS, you need to add a constant (literally a column of 1s) under both statsmodels.formulas.api and plain statsmodels.api. @Chetan is using R-style formatting here (formula='Sales ~ TV'), so he will not run into this subtlety, but for people with some Python knowledge but no R background this could be very confusing.

Furthermore it doesn't matter whether you specify the hasconst parameter when building the model. (Which is kind of silly.) In other words, unless you are using R-style string formulas, hasconst is ignored even though it is supposed to

[Indicate] whether the RHS includes a user-supplied constant

because, in the footnotes

No constant is added by the model unless you are using formulas.

The example below shows that both .formulas.api and .api will require a user-added column vector of 1s if not using R-style string formulas.

# Generate some relational data
np.random.seed(123)
nobs = 25 
x = np.random.random((nobs, 2)) 
x_with_ones = sm.add_constant(x, prepend=False)
beta = [.1, .5, 1] 
e = np.random.random(nobs)
y = np.dot(x_with_ones, beta) + e

Now throw x and y into Excel and run Data>Data Analysis>Regression, making sure "Constant is zero" is unchecked. You'll get the following coefficients:

Intercept       1.497761024
X Variable 1    0.012073045
X Variable 2    0.623936056

Now, try running this regression on x, not x_with_ones, in either statsmodels.formula.api or statsmodels.api with hasconst set to None, True, or False. You'll see that in each of those 6 scenarios, there is no intercept returned. (There are only 2 parameters.)

import statsmodels.formula.api as smf
import statsmodels.api as sm

print('smf models')
print('-' * 10)
for hc in [None, True, False]:
    model = smf.OLS(endog=y, exog=x, hasconst=hc).fit()
    print(model.params)

# smf models
# ----------
# [ 1.46852293  1.8558273 ]
# [ 1.46852293  1.8558273 ]
# [ 1.46852293  1.8558273 ]

Now running things correctly with a column vector of 1.0s added to x. You can use smf here but it's really not necessary if you're not using formulas.

print('sm models')
print('-' * 10)
for hc in [None, True, False]:
    model = sm.OLS(endog=y, exog=x_with_ones, hasconst=hc).fit()
    print(model.params)

# sm models
# ----------
# [ 0.01207304  0.62393606  1.49776102]
# [ 0.01207304  0.62393606  1.49776102]
# [ 0.01207304  0.62393606  1.49776102]