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.