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=1.0E-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.

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 dict-like 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=1.0E-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.

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 dict-like 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.