class_docstr: A class for holding various results obtained from fitting one data
  set using lmer in R.
  
  Parameters
  ----------
  meth : str
      Either "ml" or "reml".
  irfs : str
      Either "irf", for independent random effects, or "drf" for
      dependent random effects.
  ds_ix : int
      The number of the data set
class_docstr_tok: ["A","class","for","holding","various","results","obtained","from","fitting","one","data","set","using","lmer","in","R.","Parameters","--","--","--","--","--","meth",":","str","Either","``","ml","''","or","``","reml","''",".","irfs",":","str","Either","``","irf","''",",","for","independent","random","effects",",","or","``","drf","''","for","dependent","random","effects",".","ds_ix",":","int","The","number","of","the","data","set"]
code: def __init__(self, meth, irfs, ds_ix):
      bname = "_%s_%s_%d" % (meth, irfs, ds_ix)
  
      self.coef = getattr(lme_r_results, "coef" + bname)
      self.vcov_r = getattr(lme_r_results, "vcov" + bname)
      self.cov_re_r = getattr(lme_r_results, "cov_re" + bname)
      self.scale_r = getattr(lme_r_results, "scale" + bname)
      self.loglike = getattr(lme_r_results, "loglike" + bname)
  
      if hasattr(lme_r_results, "ranef_mean" + bname):
          self.ranef_postmean = getattr(
              lme_r_results, "ranef_mean" + bname
          )
          self.ranef_condvar = getattr(
              lme_r_results, "ranef_condvar" + bname
          )
          self.ranef_condvar = np.atleast_2d(self.ranef_condvar)
  
      # Load the data file
      cur_dir = os.path.dirname(os.path.abspath(__file__))
      rdir = os.path.join(cur_dir, "results")
      fname = os.path.join(rdir, "lme%02d.csv" % ds_ix)
      with open(fname, encoding="utf-8") as fid:
          rdr = csv.reader(fid)
          header = next(rdr)
          data = [[float(x) for x in line] for line in rdr]
      data = np.asarray(data)
  
      # Split into exog, endog, etc.
      self.endog = data[:, header.index("endog")]
      self.groups = data[:, header.index("groups")]
      ii = [i for i, x in enumerate(header) if x.startswith("exog_fe")]
      self.exog_fe = data[:, ii]
      ii = [i for i, x in enumerate(header) if x.startswith("exog_re")]
      self.exog_re = data[:, ii]
  
imports: from statsmodels.compat.platform import PLATFORM_OSX
  import os
  import csv
  import warnings
  import numpy
  import pandas
  from scipy import sparse
  import pytest
  from statsmodels.regression.mixed_linear_model import MixedLM, MixedLMParams, _smw_solver, _smw_logdet
  from numpy.testing import assert_almost_equal, assert_equal, assert_allclose, assert_
  from statsmodels.base import _penalties
  import statsmodels.tools.numdiff
  from .results import lme_r_results
label_desc: Use image node_id 1 to create a new R_Results object  with example: obj = R_Results(meth, irfs, ds_ix)