docstr: Return (unnormalized) log-likelihood for GLM.
  
  Note that self.scale is interpreted as a variance in old_model, so
  we divide the residuals by its sqrt.
docstr_tok: ["Return","(","unnormalized",")","log-likelihood","for","GLM",".","Note","that","self.scale","is","interpreted","as","a","variance","in","old_model",",","so","we","divide","the","residuals","by","its","sqrt","."]
code: def deviance(self, Y=None, results=None, scale=1.0):
      """
      Return (unnormalized) log-likelihood for GLM.
  
      Note that self.scale is interpreted as a variance in old_model, so
      we divide the residuals by its sqrt.
      """
      if results is None:
          results = self.results
      if Y is None:
          Y = self.Y
      return self.family.deviance(Y, results.mu) / scale
  
file_path: nipy/nipy/algorithms/statistics/models/glm.py
imports: import numpy
  from .None import family
  from .regression import WLSModel
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.deviance(Y, results, scale) and returns: unknown