#Backward Elimination with p-values only:
import statsmodels.formula.api as sm
def backwardElimination(x, sl):
numVars = len(x[0])
for i in range(0, numVars):
regressor_OLS = sm.OLS(y, x).fit()
maxVar = max(regressor_OLS.pvalues).astype(float)
if maxVar > sl:
for j in range(0, numVars - i):
if (regressor_OLS.pvalues[j].astype(float) == maxVar):
x = np.delete(x, j, 1)
regressor_OLS.summary()
return x
SL = 0.05
X_opt = X[:, [0, 1, 2, 3, 4, 5]]
X_Modeled = backwardElimination(X_opt, SL)
#Backward Elimination with p-values and Adjusted R Squared:
import statsmodels.formula.api as sm
def backwardElimination(x, SL):
numVars = len(x[0])
temp = np.zeros((50,6)).astype(int)
for i in range(0, numVars):
regressor_OLS = sm.OLS(y, x).fit()
maxVar = max(regressor_OLS.pvalues).astype(float)
adjR_before = regressor_OLS.rsquared_adj.astype(float)
if maxVar > SL:
for j in range(0, numVars - i):
if (regressor_OLS.pvalues[j].astype(float) == maxVar):
temp[:,j] = x[:, j]
x = np.delete(x, j, 1)
tmp_regressor = sm.OLS(y, x).fit()
adjR_after = tmp_regressor.rsquared_adj.astype(float)
if (adjR_before >= adjR_after):
x_rollback = np.hstack((x, temp[:,[0,j]]))
x_rollback = np.delete(x_rollback, j, 1)
print (regressor_OLS.summary())
return x_rollback
else:
continue
regressor_OLS.summary()
return x
SL = 0.05
X_opt = X[:, [0, 1, 2, 3, 4, 5]]
X_Modeled = backwardElimination(X_opt, SL)
