Generalized Linear Models¶

class
pymc3.glm.linear.
GLM
(*args, **kwargs)¶ 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=1.0E6)
 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.

classmethod
from_formula
(formula, data, priors=None, vars=None, family='normal', name='', model=None, offset=0.0, eval_env=0)¶ Creates GLM from formula.
 Parameters
 formula: str  a `patsy` formula
 data: a dictlike object that can be used to look up variables referenced
in formula
 eval_env: either a `patsy.EvalEnvironment` or else a depth represented as
an integer which will be passed to patsy.EvalEnvironment.capture(). See patsy.dmatrix and patsy.EvalEnvironment for details.
 Other arguments are documented in the constructor.

class
pymc3.glm.linear.
LinearComponent
(*args, **kwargs)¶ 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=1.0E6)
 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.

classmethod
from_formula
(formula, data, priors=None, vars=None, name='', model=None, offset=0.0, eval_env=0)¶ Creates linear component from patsy formula.
 Parameters
 formula: str  a patsy formula
 data: a dictlike object that can be used to look up variables referenced
in formula
 eval_env: either a `patsy.EvalEnvironment` or else a depth represented as
an integer which will be passed to patsy.EvalEnvironment.capture(). See patsy.dmatrix and patsy.EvalEnvironment for details.
 Other arguments are documented in the constructor.