Why Do I Get Only One Parameter from a Statsmodels Ols Fit

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())