class_docstr: Results class for Beta regression
  
  This class inherits from GenericLikelihoodModelResults and not all
  inherited methods might be appropriate in this case.
class_docstr_tok: ["Results","class","for","Beta","regression","This","class","inherits","from","GenericLikelihoodModelResults","and","not","all","inherited","methods","might","be","appropriate","in","this","case","."]
docstr: Get an instance of MLEInfluence with influence and outlier measures
  
  Returns
  -------
  infl : MLEInfluence instance
      The instance has methods to calculate the main influence and
      outlier measures as attributes.
  
  See Also
  --------
  statsmodels.stats.outliers_influence.MLEInfluence
  
  Notes
  -----
  Support for mutli-link and multi-exog models is still experimental
  in MLEInfluence. Interface and some definitions might still change.
  
  Note: Difference to R betareg: Betareg has the same general leverage
  as this model. However, they use a linear approximation hat matrix
  to scale and studentize influence and residual statistics.
  MLEInfluence uses the generalized leverage as hat_matrix_diag.
  Additionally, MLEInfluence uses pearson residuals for residual
  analusis.
  
  References
  ----------
  todo
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code: def get_influence(self):
      """
      Get an instance of MLEInfluence with influence and outlier measures
  
      Returns
      -------
      infl : MLEInfluence instance
          The instance has methods to calculate the main influence and
          outlier measures as attributes.
  
      See Also
      --------
      statsmodels.stats.outliers_influence.MLEInfluence
  
      Notes
      -----
      Support for mutli-link and multi-exog models is still experimental
      in MLEInfluence. Interface and some definitions might still change.
  
      Note: Difference to R betareg: Betareg has the same general leverage
      as this model. However, they use a linear approximation hat matrix
      to scale and studentize influence and residual statistics.
      MLEInfluence uses the generalized leverage as hat_matrix_diag.
      Additionally, MLEInfluence uses pearson residuals for residual
      analusis.
  
      References
      ----------
      todo
  
      """
      from statsmodels.stats.outliers_influence import MLEInfluence
  
      return MLEInfluence(self)
  
imports: import numpy
  from scipy.special import gammaln
  import patsy
  import statsmodels.base.wrapper
  import statsmodels.regression.linear_model
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.base.model import GenericLikelihoodModel, GenericLikelihoodModelResults, _LLRMixin
  from statsmodels.genmod import families
label_desc: Use image node_id 8 for calling the BetaResults obj's underlying member method code with example usage: obj.get_influence() and returns: MLEInfluence