spglm.glm.
GLM
(y, X, family=<spglm.family.Gaussian object>, offset=None, y_fix=None, constant=True)[source]¶Generalised linear models. Can currently estimate Guassian, Poisson and Logisitc regression coefficients. GLM object prepares model input and fit method performs estimation which then returns a GLMResults object.
Parameters: |
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Examples
>>> import libpysal
>>> from spglm.glm import GLM
>>> from spglm.family import Gaussian
>>> db = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
>>> y = np.array(db.by_col("HOVAL"))
>>> y = np.reshape(y, (49,1))
>>> X = []
>>> X.append(db.by_col("INC"))
>>> X.append(db.by_col("CRIME"))
>>> X = np.array(X).T
>>> model = GLM(y, X, family=Gaussian())
>>> results = model.fit()
>>> results.params
array([46.42818268, 0.62898397, -0.48488854])
Attributes: |
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Methods
fit ([ini_betas, tol, max_iter, solve]) |
Method that fits a model with a particular estimation routine. |
df_model | |
df_resid |
__init__
(y, X, family=<spglm.family.Gaussian object>, offset=None, y_fix=None, constant=True)[source]¶Initialize class
Methods
__init__ (y, X[, family, offset, y_fix, constant]) |
Initialize class |
df_model () |
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df_resid () |
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fit ([ini_betas, tol, max_iter, solve]) |
Method that fits a model with a particular estimation routine. |
Attributes
mean_y |
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std_y |