code: def test_r(self):
      # values from R,
      # > xi <- 1:5
      # > yi <- c(0,2,14,19,30)    # number of mice responding to dose xi
      # > mi <- rep(40, 5)         # number of mice exposed
      # > glmI <- glm(cbind(yi, mi -yi) ~ xi, family = binomial)
      # > imI <- influence.measures(glmI)
      # > t(imI$infmat)
  
      # dfbeta/dfbetas and dffits do not make sense to me and are furthe away from
      # looo than mine
      # resid seem to be resid_deviance based and not resid_pearson
      # I did not compare cov.r
      infl1 = self.infl1
      cooks_d = [
          0.25220202795934726,
          0.26107981497746285,
          1.28985614424132389,
          0.08449722285516942,
          0.36362110845918005,
      ]
      hat = [
          0.2594393406119333,
          0.3696442663244837,
          0.3535768402250521,
          0.389209198535791057,
          0.6281303543027403,
      ]
  
      assert_allclose(infl1.hat_matrix_diag, hat, rtol=5e-6)
      assert_allclose(infl1.cooks_distance[0], cooks_d, rtol=1e-5)
  
imports: from statsmodels.compat.pandas import testing
  import os.path
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  import pytest
  from statsmodels.regression.linear_model import OLS
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.genmod import families
  from statsmodels.stats.outliers_influence import MLEInfluence
label_desc: Use image node_id 3 for calling the TestInfluenceBinomialGLMMLE obj's underlying member method code with example usage: obj.test_r() without return types