def _get_intercept_stats(self, add_slopes=True):
# start with mean and variance of Y on the link scale
mod = GLM(
endog=self.model.y.data,
exog=np.repeat(1, len(self.model.y.data)),
family=self.model.family.smfamily(),
missing="drop" if self.model.dropna else "none",
).fit()
mu = mod.params
# multiply SE by sqrt(N) to turn it into (approx.) SD(Y) on link scale
sd = (mod.cov_params()[0] * len(mod.mu)) ** 0.5
# modify mu and sd based on means and SDs of slope priors.
if len(self.model.fixed_terms) > 1 and add_slopes:
means = np.array([x["mu"] for x in self.priors.values()])
sds = np.array([x["sd"] for x in self.priors.values()])
# add to intercept prior
index = list(self.priors.keys())
mu -= np.dot(means, self.stats["mean_x"][index])
sd = (sd ** 2 + np.dot(sds ** 2, self.stats["mean_x"][index] ** 2)) ** 0.5
return mu, sd
def get_intercept_stats(self, add_slopes=True):
# Start with mean and variance of Y on the link scale
mod = GLM(
endog=self.model.response.data,
exog=np.repeat(1, len(self.model.response.data)),
family=self.model.family.smfamily(self.model.family.smlink),
missing="drop" if self.model.dropna else "none",
).fit()
mu = mod.params
# Multiply SE by sqrt(N) to turn it into (approx.) sigma(Y) on link scale
sigma = (mod.cov_params()[0] * len(mod.mu)) ** 0.5
# Modify mu and sigma based on means and sigmas of slope priors.
if len(self.model.common_terms) > 1 and add_slopes:
# prior["mu"] and prior["sigma"] have more than one value when the term is categoric.
means = np.hstack([prior["mu"] for prior in self.priors.values()])
sigmas = np.hstack([prior["sigma"] for prior in self.priors.values()])
x_mean = np.hstack([self.model.terms[term].data.mean(axis=0) for term in self.priors])
mu -= np.dot(means, x_mean)
sigma = (sigma ** 2 + np.dot(sigmas ** 2, x_mean ** 2)) ** 0.5
return mu, sigma
