Python Statsmodels: Ols Regressor Not Predicting

I wrote the following piece of code but I just cannot get the 'predict' method to work: import statsmodels.api as sm from statsmodels.formula.api import ols ols_model = ols('Consum

Solution 1:

Since you work with the formulas in the model, the formula information will also be used in the interpretation of the exog in predict.

I think you need to use a dataframe or a dictionary with the correct name of the explanatory variable(s).

ols_model.predict({'Disposable_Income':[1000.0]})

or something like

df_predict = pd.DataFrame([[1000.0]], columns=['Disposable_Income'])
ols_model.predict(df_predict)

Another option is to avoid formula handling in predict if the full design matrix for prediction, including constant, is available

AFAIR, this should also work:

ols_model.predict([[1, 1000.0]], transform=False)

Solution 2:

Not sure if this is the best approach, but after lots and lots of fiddling around, I got this code to work (seems abit clumsy and inefficient):

Say I want to predict the value at X=10 and X=1000:

import statsmodels.api as sm
from statsmodels.formula.api import ols
ols_model = ols('Consumption ~ Disposable_Income', df).fit()
regressor = ols('Consumption ~ Disposable_Income', df)
regressor.predict(ols_model.params, exog=[[1,10],[1,1000]])