Ordinal regression docs (bambinos#719)

* ordinal model with cumulative link notebook * ordinal model with cumulative link function ordinal models (cumulative and sratio) * unified explanation for cumulative and sequential models * sratio model and data * code review changes * remove intercept in models * zero mu vector prior for sratio family * code review and add section on default priors * explicit explanation of K and k and added summary section * Zero inflated docs (bambinos#725) * zero inflated poisson and hurdle poisson models * grammar fix and sort imports * interpret coeff. and model comparison section * code review changes * change wording in hurdle Poisson section * change posterior predictive bins to use np.arange * ordinal model with cumulative link function ordinal models (cumulative and sratio) * use plot_ppc_discrete for posterior predictive samples * add plots explaining the ordinal outcome of the dataset --------- Co-authored-by: Gabriel Stechschulte <[email protected]>
GStechschulte · Oct 3, 2023 · 976709c · 976709c
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diff --git a/bambi/priors/scaler.py b/bambi/priors/scaler.py
@@ -114,7 +114,7 @@ def scale_threshold(self):
             threshold = self.model.components["threshold"]
             if isinstance(threshold, ConstantComponent) and threshold.prior.auto_scale:
                 response_level_n = len(np.unique(self.response_component.response_term.data))
-                mu = np.round(np.linspace(-2, 2, num=response_level_n - 1), 2)
+                mu = np.zeros(response_level_n - 1)
                 threshold.prior = Prior("Normal", mu=mu, sigma=1)
 
     def scale(self):