GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
---------------------------------------------------------------------------
'''
import LIVEstimator as liv
import sampleSimulation as ss
import pysal.spreg as foreignModel
# get the first 100 observations for test
Y, D, X, Z = ss.underiv_data(200)
# first needs to estimate the propensity score
psResult = foreignModel.Probit(D, Z)
psHat = psResult.predy
# then get the parameter
residY, beta = liv.LLRresidualEst(Y, X, psHat)
assert (beta[0] - 0.3139) < 0.0001
assert (beta[1] - 0.2651) < 0.0001
print 'LLRresidualEst LIV passes unit test'
'''
If the beta can be correctly estimated, then the rest of the procedure of LIV method does not need unit test
'''
polyOrder = 4
theta = liv.LLRKpPolyParam(residY, psHat, polyOrder)
#print test1, test2 #from mpi4py import MPI #import numpy #comm = MPI.COMM_WORLD #rank = comm.Get_rank() #if rank == 0: # data = numpy.arange(1000, dtype = 'i') # comm.Send([data, MPI.INT], dest = 1, tag = 77) #elif rank == 1: # data = numpy.empty(1000, dtype = 'i') # comm.Recv([data, MPI.INT], source = 0, tag = 77) import sampleSimulation as ss import localLinearRegression as LLR import numpy Y,D,X,Z = ss.underiv_data(500) h = numpy.arange(0.05,2,0.05) LLR.optimalBandwidthSelection(Y, X[:,0:1], h) # Retrieves the result calculated by job1 # The value of job1() is the same as sum_primes(100) # If the job has not been finished yet, execution will wait here until result is available
along with this program. If not, see <http://www.gnu.org/licenses/>.
---------------------------------------------------------------------------
'''
import sampleSimulation as ss
import numpy as np
import estimatorInterface as ei
# integration is numericially unstable, thus disable the warning!
import warnings
warnings.filterwarnings('ignore')
# load the data
Y, D, X, Z = ss.underiv_data(5000)
# Set the x for the MTE
x0 = np.mean(X, 0)
x0.shape = (1, 2)
testEstObj = ei.treatmentEffectEst()
'''
**************************************************
Heck it: Linear Model, Normal Distribution
**************************************************
'''
# calculate the MTE
linear = 1
normal = 1
LLRMTE = 0
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
---------------------------------------------------------------------------
'''
import LIVEstimator as liv
import sampleSimulation as ss
import pysal.spreg as foreignModel
# get the first 100 observations for test
Y,D,X,Z = ss.underiv_data(200)
# first needs to estimate the propensity score
psResult = foreignModel.Probit(D,Z)
psHat = psResult.predy
# then get the parameter
residY, beta = liv.LLRresidualEst(Y, X, psHat)
assert (beta[0]-0.3139)<0.0001
assert (beta[1]-0.2651)<0.0001
print 'LLRresidualEst LIV passes unit test'
'''
If the beta can be correctly estimated, then the rest of the procedure of LIV method does not need unit test
'''
polyOrder = 4
