mgwr.gwr.
GWR
(coords, y, X, bw, family=<spglm.family.Gaussian object>, offset=None, sigma2_v1=True, kernel='bisquare', fixed=False, constant=True, dmat=None, sorted_dmat=None, spherical=False)[source]¶Geographically weighted regression. Can currently estimate Gaussian, Poisson, and logistic models(built on a GLM framework). GWR object prepares model input. Fit method performs estimation and returns a GWRResults object.
Parameters: 


Examples
#basic model calibration
>>> import libpysal as ps
>>> from mgwr.gwr import GWR
>>> data = ps.io.open(ps.examples.get_path('GData_utm.csv'))
>>> coords = list(zip(data.by_col('X'), data.by_col('Y')))
>>> y = np.array(data.by_col('PctBach')).reshape((1,1))
>>> rural = np.array(data.by_col('PctRural')).reshape((1,1))
>>> pov = np.array(data.by_col('PctPov')).reshape((1,1))
>>> african_amer = np.array(data.by_col('PctBlack')).reshape((1,1))
>>> X = np.hstack([rural, pov, african_amer])
>>> model = GWR(coords, y, X, bw=90.000, fixed=False, kernel='bisquare')
>>> results = model.fit()
>>> print(results.params.shape)
(159, 4)
#predict at unsampled locations
>>> index = np.arange(len(y))
>>> test = index[10:]
>>> X_test = X[test]
>>> coords_test = np.array(coords)[test]
>>> model = GWR(coords, y, X, bw=94, fixed=False, kernel='bisquare')
>>> results = model.predict(coords_test, X_test)
>>> print(results.params.shape)
(10, 4)
Attributes: 


Methods
fit ([ini_params, tol, max_iter, solve, …]) 
Method that fits a model with a particular estimation routine. 
predict (points, P[, exog_scale, exog_resid, …]) 
Method that predicts values of the dependent variable at unsampled locations 
df_model  
df_resid 