def _validate_priors(self, level_sigma_prior, level_nu_prior,
slope_sigma_prior, slope_nu_prior,
y, sdy):
if level_sigma_prior is None:
sdy = self._compute_sdy(sdy, y, "level_sigma_prior")
level_sigma_prior = R.SdPrior(
sigma_guess=.01 * sdy,
upper_limit=sdy)
if not isinstance(level_sigma_prior, R.SdPrior):
raise Exception("Unexpected type for level_sigma_prior.")
if slope_sigma_prior is None:
sdy = self._compute_sdy(sdy, y, "slope_sigma_prior")
slope_sigma_prior = R.SdPrior(
sigma_guess=.01 * sdy,
upper_limit=sdy)
if not isinstance(slope_sigma_prior, R.SdPrior):
raise Exception("Unexpected type for slope_sigma_prior.")
if level_nu_prior is None:
level_nu_prior = R.UniformPrior(0.1, 100)
if not isinstance(level_nu_prior, R.DoubleModel):
raise Exception("Unexpected type for level_nu_prior.")
if slope_nu_prior is None:
slope_nu_prior = R.UniformPrior(0.1, 100)
if not isinstance(slope_nu_prior, R.DoubleModel):
raise Exception("Unexpected type for slope_nu_prior.")
self._level_sigma_prior = level_sigma_prior
self._slope_sigma_prior = slope_sigma_prior
self._level_nu_prior = level_nu_prior
self._slope_nu_prior = slope_nu_prior
def __init__(self,
x,
y=None,
expected_r2=.5,
prior_df=.01,
expected_model_size=1,
prior_information_weight=.01,
diagonal_shrinkage=.5,
optional_coefficient_estimate=None,
max_flips=-1,
mean_y=None,
sdy=None,
prior_inclusion_probabilities=None,
sigma_upper_limit=np.Inf,
tail_thickness_prior=R.UniformPrior(0.1, 100)):
"""
Args:
tail_thickness_prior: An object with a boom() method, which returns a
boom.DoubleModel describing the prior on the tail thickness
parameter.
All other arguments are as documented in RegressionSpikeSlabPrior.
"""
super().__init__(
x=x,
y=y,
expected_r2=expected_r2,
prior_df=prior_df,
expected_model_size=expected_model_size,
prior_information_weight=prior_information_weight,
diagonal_shrinkage=diagonal_shrinkage,
optional_coefficient_estimate=optional_coefficient_estimate,
max_flips=max_flips,
mean_y=mean_y,
sdy=sdy,
prior_inclusion_probabilities=prior_inclusion_probabilities,
sigma_upper_limit=sigma_upper_limit)
self._nu_prior = tail_thickness_prior
