code: def test_predict_formula(self):
      n = 100
      np.random.seed(34234)
      time = 50 * np.random.uniform(size=n)
      status = np.random.randint(0, 2, n).astype(np.float64)
      exog = np.random.uniform(1, 2, size=(n, 2))
  
      df = pd.DataFrame(
          {
              "time": time,
              "status": status,
              "exog1": exog[:, 0],
              "exog2": exog[:, 1],
          }
      )
  
      # Works with "0 +" on RHS but issues warning
      fml = "time ~ exog1 + np.log(exog2) + exog1*exog2"
      model1 = PHReg.from_formula(fml, df, status=status)
      result1 = model1.fit()
  
      from patsy import dmatrix
  
      dfp = dmatrix(model1.data.design_info, df)
  
      pr1 = result1.predict()
      pr2 = result1.predict(exog=df)
      pr3 = model1.predict(
          result1.params, exog=dfp
      )  # No standard errors
      pr4 = model1.predict(
          result1.params, cov_params=result1.cov_params(), exog=dfp
      )
  
      prl = (pr1, pr2, pr3, pr4)
      for i in range(4):
          for j in range(i):
              assert_allclose(
                  prl[i].predicted_values, prl[j].predicted_values
              )
  
      prl = (pr1, pr2, pr4)
      for i in range(3):
          for j in range(i):
              assert_allclose(
                  prl[i].standard_errors, prl[j].standard_errors
              )
  
imports: import itertools
  import os
  import numpy
  from statsmodels.duration.hazard_regression import PHReg
  from numpy.testing import assert_allclose, assert_equal, assert_
  import pandas
  import pytest
  from .results import survival_r_results
  from .results import survival_enet_r_results
label_desc: Use image node_id 6 for calling the TestPHReg obj's underlying member method code with example usage: obj.test_predict_formula() without return types