# Generalized Linear Models¶

class pymc3.glm.linear.LinearComponent(x, y, intercept=True, labels=None, priors=None, vars=None, name='', model=None, offset=0.0)

Creates linear component, y_est is accessible via attribute

Parameters: name (str - name, associated with the linear component) – x (pd.DataFrame or np.ndarray) – y (pd.Series or np.array) – intercept (bool - fit with intercept or not?) – labels (list - replace variable names with these labels) – priors (dict - priors for coefficients) – use Intercept key for defining Intercept prior defaults to Flat.dist() use Regressor key for defining default prior for all regressors defaults to Normal.dist(mu=0, tau=1.0E-6) vars (dict - random variables instead of creating new ones) – offset (scalar, or numpy/theano array with the same shape as y) – this can be used to specify an a priori known component to be included in the linear predictor during fitting.
class pymc3.glm.linear.GLM(x, y, intercept=True, labels=None, priors=None, vars=None, family='normal', name='', model=None, offset=0.0)

Creates glm model, y_est is accessible via attribute

Parameters: name (str - name, associated with the linear component) – x (pd.DataFrame or np.ndarray) – y (pd.Series or np.array) – intercept (bool - fit with intercept or not?) – labels (list - replace variable names with these labels) – priors (dict - priors for coefficients) – use Intercept key for defining Intercept prior defaults to Flat.dist() use Regressor key for defining default prior for all regressors defaults to Normal.dist(mu=0, tau=1.0E-6) init (dict - test_vals for coefficients) – vars (dict - random variables instead of creating new ones) – family (pymc3..families object) – offset (scalar, or numpy/theano array with the same shape as y) – this can be used to specify an a priori known component to be included in the linear predictor during fitting.