How Does "Statsmodels. Regression. Linear_Model. Wls" Work?
I Have Used 'Statsmodels. Regression. Linear_Model' to Do Wls. but I Have No Idea About How to Give Weight My Regression. Does Anyone Know How the Weight Be...
I have used 'statsmodels.regression.linear_model' to do WLS.
But I have no idea about how to give weight my regression.
Does anyone know how the weight be given and how it work?
import numpy as np
import statsmodels.api as sm
Y = [1,2,3,4,5,6,7]
X = range(1,8)
W= [1,1,1,1,1,1,1]
X = sm.add_constant(X)
wls_model = sm.WLS(Y,X, weights=W)
results = wls_model.fit()
results.params
print results.params
#[ -1.55431223e-15 1.00000000e+00]
import numpy as np
import statsmodels.api as sm
Y = [1,2,3,4,5,6,7]
X = range(1,8)
W= range(1,8)
X = sm.add_constant(X)
wls_model = sm.WLS(Y,X, weights=W)
results = wls_model.fit()
results.params
print results.params
#[0 1]
why when weight is range(1,8) the slope and intercept is 1 and 0. but when weight is "1" the intercept is not 0.
1 Answer
In your example, the data is linear anyway, so the the regression will be a perfect fit, no matter what your weights. But if you change your data to have an outlier in the first position like this
Y = [-5,2,3,4,5,6,7]
then with constant weights you get
[-3.42857143 1.64285714]
but with W = range(1,8) you get
[-1.64285714 1.28571429]
which is closer to what you want without the outlier.
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