code: def _margeff_cov_params_count(
      model, cov_margins, params, exog, count_ind, method, J
  ):
      r"""
      Returns the Jacobian for discrete regressors for use in margeff_cov_params.
  
      For discrete regressors the marginal effect is
  
      \Delta F = F(XB) | d += 1 - F(XB) | d -= 1
  
      The row of the Jacobian for this variable is given by
  
      (f(XB)*X | d += 1 - f(XB)*X | d -= 1) / 2
  
      where F is the default prediction for the model.
      """
      for i in count_ind:
          exog0 = exog.copy()
          exog0[:, i] -= 1
          dfdb0 = model._derivative_predict(params, exog0, method)
          exog0[:, i] += 2
          dfdb1 = model._derivative_predict(params, exog0, method)
          dfdb = dfdb1 - dfdb0
          if dfdb.ndim >= 2:  # for overall
              dfdb = dfdb.mean(0) / 2
          if J > 1:
              K = dfdb.shape[1] / (J - 1)
              cov_margins[i::K, :] = dfdb
          else:
              # dfdb could be too short if there are extra params, k_extra > 0
              cov_margins[
                  i, : len(dfdb)
              ] = dfdb  # how each F changes with change in B
      return cov_margins
  
imports: from statsmodels.compat.python import lzip
  import numpy
  from scipy.stats import norm
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 13 for calling a global function with example usage: _margeff_cov_params_count(model, cov_margins, params, exog, count_ind, method, J) and returns: cov_margins