mgwr.gwr.
MGWR
(coords, y, X, selector, sigma2_v1=True, kernel='bisquare', fixed=False, constant=True, dmat=None, sorted_dmat=None, spherical=False)[source]¶Multiscale GWR estimation and inference.
Parameters: 


Examples
#basic model calibration
>>> import libpysal as ps
>>> from mgwr.gwr import MGWR
>>> from mgwr.sel_bw import Sel_BW
>>> 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))
>>> fb = np.array(data.by_col('PctFB')).reshape((1,1))
>>> african_amer = np.array(data.by_col('PctBlack')).reshape((1,1))
>>> X = np.hstack([fb, african_amer, rural])
>>> X = (X  X.mean(axis=0)) / X.std(axis=0)
>>> y = (y  y.mean(axis=0)) / y.std(axis=0)
>>> selector = Sel_BW(coords, y, X, multi=True)
>>> selector.search(multi_bw_min=[2])
[92.0, 101.0, 136.0, 158.0]
>>> model = MGWR(coords, y, X, selector, fixed=False, kernel='bisquare', sigma2_v1=True)
>>> results = model.fit()
>>> print(results.params.shape)
(159, 4)
Attributes: 


Methods
fit () 
Method that extracts information from Sel_BW (selector) object and prepares GAM estimation results for MGWRResults object. 
predict () 
Not implemented. 
df_model  
df_resid 