spglm.glm.GLM¶

class 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:

y : array n*1, dependent variable. X : array n*k, independent variable, exlcuding the constant. family : string Model type: ‘Gaussian’, ‘Poisson’, ‘Binomial’ offset : array n*1, the offset variable at the ith location. For Poisson model this term is often the size of the population at risk or the expected size of the outcome in spatial epidemiology. Default is None where Ni becomes 1.0 for all locations. y_fix : array n*1, the fix intercept value of y

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:

y : array n*1, dependent variable. X : array n*k, independent variable, including constant. family : string Model type: ‘Gaussian’, ‘Poisson’, ‘logistic’ n : integer Number of observations k : integer Number of independent variables df_model : float k-1, where k is the number of variables (including intercept) df_residual : float observations minus variables (n-k) mean_y : float Mean of y std_y : float Standard deviation of y fit_params : dict Parameters passed into fit method to define estimation routine.

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()
df_resid()
fit([ini_betas, tol, max_iter, solve])	Method that fits a model with a particular estimation routine.

Attributes

mean_y
std_y