# -*- coding: utf-8 -*-
"""Example for out of sample prediction

This is a variation on tut_ols.py, that shows the use of the
predict method

Note: uncomment plt.show() to display graphs
"""

import numpy as np
import scikits.statsmodels.api as sm

# create some data set

nsample = 50
sig = 0.25
x1 = np.linspace(0, 20, nsample)
X = np.c_[x1, np.sin(x1), (x1-5)**2, np.ones(nsample)]
beta = [0.5, 0.5, -0.02, 5.]
y_true = np.dot(X, beta)
y = y_true + sig * np.random.normal(size=nsample)

#setup and estimate the model

olsmod = sm.OLS(y, X)
olsres = olsmod.fit()
print olsres.params
print olsres.bse

# use predict method of model class, not in the results class
# (we had a discussion but it is still in the model)

ypred = olsmod.predict(X) # predict insample

# create a new sample of explanatory variables Xnew, predict and plot

x1n = np.linspace(20.5,25, 10)
Xnew = np.c_[x1n, np.sin(x1n), (x1n-5)**2, np.ones(10)]
ynewpred =  olsmod.predict(Xnew) # predict out of sample
print ypred

import matplotlib.pyplot as plt
plt.figure()
plt.plot(x1, y, 'o', x1, y_true, 'b-')
plt.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)),'r')
plt.title('OLS prediction, blue: true and data, fitted/predicted values:red')

#plt.show()