priors.py 文件源码

def _get_intercept_stats(self, add_slopes=True):
        # start with mean and variance of Y on the link scale
        mod = sm.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))**.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))**.5

        return mu, sd