Why do I get only one parameter from a statsmodels OLS fit

Here is what I am doing:

$ python Python 2.7.6 (v2.7.6:3a1db0d2747e, Nov 10 2013, 00:42:54) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin >>> import statsmodels.api as sm >>> statsmodels.__version__ '0.5.0' >>> import numpy >>> y = numpy.array([1,2,3,4,5,6,7,8,9]) >>> X = numpy.array([1,1,2,2,3,3,4,4,5]) >>> res_ols = sm.OLS(y, X).fit() >>> res_ols.params array([ 1.82352941]) 

I had expected an array with two elements?!? The intercept and the slope coefficient?

6 Answers

Try this:

X = sm.add_constant(X) sm.OLS(y,X) 

as in the documentation:

An intercept is not included by default and should be added by the user

statsmodels.tools.tools.add_constant

Just to be complete, this works:

>>> import numpy >>> import statsmodels.api as sm >>> y = numpy.array([1,2,3,4,5,6,7,8,9]) >>> X = numpy.array([1,1,2,2,3,3,4,4,5]) >>> X = sm.add_constant(X) >>> res_ols = sm.OLS(y, X).fit() >>> res_ols.params array([-0.35714286, 1.92857143]) 

It does give me a different slope coefficient, but I guess that figures as we now do have an intercept.

Try this, it worked for me:

import statsmodels.formula.api as sm from statsmodels.api import add_constant X_train = add_constant(X_train) X_test = add_constant(X_test) model = sm.OLS(y_train,X_train) results = model.fit() y_pred=results.predict(X_test) results.params 

I'm running 0.6.1 and it looks like the "add_constant" function has been moved into the statsmodels.tools module. Here's what I ran that worked:

res_ols = sm.OLS(y, statsmodels.tools.add_constant(X)).fit() 

i did add the code X = sm.add_constant(X) but python did not return the intercept value so using a little algebra i decided to do it myself in code:

this code computes regression over 35 samples, 7 features plus one intercept value that i added as feature to the equation:

import statsmodels.api as sm from sklearn import datasets ## imports datasets from scikit-learn import numpy as np import pandas as pd x=np.empty((35,8)) # (numSamples, oneIntercept + numFeatures)) feature_names = np.empty((8,)) y = np.empty((35,)) dbfv = open("dataset.csv").readlines() interceptConstant = 1; i = 0 # reading data and writing in numpy arrays while i<len(dbfv): cells = dbfv[i].split(",") j = 0 x[i][j] = interceptConstant feature_names[j] = str(j) while j<len(cells)-1: x[i][j+1] = cells[j] feature_names[j+1] = str(j+1) j += 1 y[i] = cells[len(cells)-1] i += 1 # creating dataframes df = pd.DataFrame(x, columns=feature_names) target = pd.DataFrame(y, columns=["TARGET"]) X = df y = target["TARGET"] model = sm.OLS(y, X).fit() print(model.params) # predictions = model.predict(X) # make the predictions by the model # Print out the statistics print(model.summary()) 

Try this

X = sm.add_constant(X) ols= sm.OLS(y,X) res_ols= ols.fit() res_ols.params res_ols.params[0] res_ols.params[1] print(res_ols.summary())