class_docstr: Scale prior distributions parameters.
class_docstr_tok: ["Scale","prior","distributions","parameters","."]
code: def __init__(self, model):
      self.model = model
      self.response_component = model.response_component
      self.has_intercept = (
          self.response_component.intercept_term is not None
      )
      self.priors = {}
  
      # Compute mean and std of the response
      if isinstance(self.model.family, (Gaussian, StudentT)):
          self.response_mean = np.mean(
              self.response_component.response_term.data
          )
          self.response_std = np.std(
              self.response_component.response_term.data
          )
      else:
          self.response_mean = 0
          self.response_std = 1
  
file_path: bambi/bambi/priors/scaler.py
imports: import numpy
  import pymc
  from bambi.families.univariate import Cumulative, Gaussian, StoppingRatio, StudentT, VonMises
  from bambi.model_components import ConstantComponent
  from bambi.priors.prior import Prior
label_desc: Use image node_id 1 to create a new PriorScaler object  with example: obj = PriorScaler(model)