class_docstr: A class to model one-parameter exponential
  families.
  
  INPUTS:
     link      -- a Link instance
     variance  -- a variance function (models means as a function
                  of mean)
class_docstr_tok: ["A","class","to","model","one-parameter","exponential","families",".","INPUTS",":","link","--","a","Link","instance","variance","--","a","variance","function","(","models","means","as","a","function","of","mean",")"]
docstr: The deviance residuals, defined as the residuals
  in the deviance.
  
  Without knowing the link, they default to Pearson residuals
  
  resid_P = (Y - mu) * sqrt(weight(mu))
  
  INPUTS:
     Y     -- response variable
     mu    -- mean parameter
  
  OUTPUTS: resid
     resid -- deviance residuals
docstr_tok: ["The","deviance","residuals",",","defined","as","the","residuals","in","the","deviance",".","Without","knowing","the","link",",","they","default","to","Pearson","residuals","resid_P","=","(","Y","-","mu",")","*","sqrt","(","weight","(","mu",")",")","INPUTS",":","Y","--","response","variable","mu","--","mean","parameter","OUTPUTS",":","resid","resid","--","deviance","residuals"]
code: def devresid(self, Y, mu):
      """
      The deviance residuals, defined as the residuals
      in the deviance.
  
      Without knowing the link, they default to Pearson residuals
  
      resid_P = (Y - mu) * sqrt(weight(mu))
  
      INPUTS:
         Y     -- response variable
         mu    -- mean parameter
  
      OUTPUTS: resid
         resid -- deviance residuals
      """
  
      return (Y - mu) * np.sqrt(self.weights(mu))
  
imports: import numpy
  from .None import links
  from .None import varfuncs
label_desc: Use image node_id 6 for calling the Family obj's underlying member method code with example usage: obj.devresid(Y, mu) and returns: unknown