"""Example: scikits.statsmodels.OLS
"""
from scikits.statsmodels.datasets.longley import Load
import scikits.statsmodels as models
import numpy as np
data = Load()
data.exog = models.tools.add_constant(data.exog)
ols_model = models.OLS(data.endog, data.exog)
ols_results = ols_model.fit()
# the Longley dataset is well known to have high multicollinearity
# one way to find the condition number is as follows
# normalize the independent variables to have unit length, Greene 4.9
norm_x = np.ones_like(data.exog)
for i in range(ols_model.df_model):
norm_x[:,i] = data.exog[:,i]/np.linalg.norm(data.exog[:,i])
norm_xtx = np.dot(norm_x.T,norm_x)
eigs = np.linalg.eigvals(norm_xtx)
collin = np.sqrt(eigs.max()/eigs.min())
print collin
# clearly there is a big problem with multicollinearity
# the rule of thumb is any number of 20 requires attention
# for instance, consider the longley dataset with the last observation dropped
ols_results2 = models.OLS(data.endog[:-1], data.exog[:-1,:]).fit()
# all of our coefficients change considerably in percentages
if __name__ == '__main__':
#A: josef-pktd
import scikits.statsmodels as sm
from scikits.statsmodels import OLS
from scikits.statsmodels.datasets.longley import Load
import scikits.statsmodels as sm
from scikits.statsmodels.iolib.table import (SimpleTable, default_txt_fmt,
default_latex_fmt, default_html_fmt)
import numpy as np
data = Load()
data.exog = sm.tools.add_constant(data.exog)
for inidx, outidx in LeaveOneOut(len(data.endog)):
res = sm.OLS(data.endog[inidx], data.exog[inidx,:]).fit()
print data.endog[outidx], res.model.predict(data.exog[outidx,:]),
print data.endog[outidx] - res.model.predict(data.exog[outidx,:])
resparams = []
for inidx, outidx in LeavePOut(len(data.endog), 2):
res = sm.OLS(data.endog[inidx], data.exog[inidx,:]).fit()
#print data.endog[outidx], res.model.predict(data.exog[outidx,:]),
#print ((data.endog[outidx] - res.model.predict(data.exog[outidx,:]))**2).sum()
resparams.append(res.params)
resparams = np.array(resparams)
doplots = 1