class_docstr: Sliced Inverse Regression (SIR)
  
  Parameters
  ----------
  endog : array_like (1d)
      The dependent variable
  exog : array_like (2d)
      The covariates
  
  References
  ----------
  KC Li (1991).  Sliced inverse regression for dimension reduction.
  JASA 86, 316-342.
class_docstr_tok: ["Sliced","Inverse","Regression","(","SIR",")","Parameters","--","--","--","--","--","endog",":","array_like","(","1d",")","The","dependent","variable","exog",":","array_like","(","2d",")","The","covariates","References","--","--","--","--","--","KC","Li","(","1991",")",".","Sliced","inverse","regression","for","dimension","reduction",".","JASA","86",",","316-342","."]
code: def _regularized_objective(self, A):
      # The objective function for regularized SIR
  
      p = self.k_vars
      covx = self._covx
      mn = self._slice_means
      ph = self._slice_props
      v = 0
      A = np.reshape(A, (p, self.ndim))
  
      # The penalty
      for k in range(self.ndim):
          u = np.dot(self.pen_mat, A[:, k])
          v += np.sum(u * u)
  
      # The SIR objective function
      covxa = np.dot(covx, A)
      q, _ = np.linalg.qr(covxa)
      qd = np.dot(q, np.dot(q.T, mn.T))
      qu = mn.T - qd
      v += np.dot(ph, (qu * qu).sum(0))
  
      return v
  
imports: import warnings
  import numpy
  import pandas
  from statsmodels.base import model
  import statsmodels.base.wrapper
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
label_desc: Use image node_id 2 for calling the SlicedInverseReg obj's underlying member method code with example usage: obj._regularized_objective(A) and returns: v