class_docstr: Significance test for continuous variables in a nonparametric regression.
  
  The null hypothesis is ``dE(Y|X)/dX_not_i = 0``, the alternative hypothesis
  is ``dE(Y|X)/dX_not_i != 0``.
  
  Parameters
  ----------
  model : KernelReg instance
      This is the nonparametric regression model whose elements
      are tested for significance.
  test_vars : tuple, list of integers, array_like
      index of position of the continuous variables to be tested
      for significance. E.g. (1,3,5) jointly tests variables at
      position 1,3 and 5 for significance.
  nboot : int
      Number of bootstrap samples used to determine the distribution
      of the test statistic in a finite sample. Default is 400
  nested_res : int
      Number of nested resamples used to calculate lambda.
      Must enable the pivot option
  pivot : bool
      Pivot the test statistic by dividing by its standard error
      Significantly increases computational time. But pivot statistics
      have more desirable properties
      (See references)
  
  Attributes
  ----------
  sig : str
      The significance level of the variable(s) tested
      "Not Significant": Not significant at the 90% confidence level
                          Fails to reject the null
      "*": Significant at the 90% confidence level
      "**": Significant at the 95% confidence level
      "***": Significant at the 99% confidence level
  
  Notes
  -----
  This class allows testing of joint hypothesis as long as all variables
  are continuous.
  
  References
  ----------
  Racine, J.: "Consistent Significance Testing for Nonparametric Regression"
  Journal of Business & Economics Statistics.
  
  Chapter 12 in [1].
docstr: Computes only lambda -- the main part of the test statistic
code: def _compute_lambda(self, Y, X):
      """Computes only lambda -- the main part of the test statistic"""
      n = np.shape(X)[0]
      Y = _adjust_shape(Y, 1)
      X = _adjust_shape(X, self.k_vars)
      b = KernelReg(
          Y,
          X,
          self.var_type,
          self.model.reg_type,
          self.bw,
          defaults=EstimatorSettings(efficient=False),
      ).fit()[1]
  
      b = b[:, self.test_vars]
      b = np.reshape(b, (n, len(self.test_vars)))
      # fct = np.std(b)  # Pivot the statistic by dividing by SE
      fct = 1.0  # Do not Pivot -- Bootstrapping works better if Pivot
      lam = ((b / fct) ** 2).sum() / float(n)
      return lam
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from ._kernel_base import GenericKDE, EstimatorSettings, gpke, LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR, kernel_func
label_desc: Use image node_id 4 for calling the TestRegCoefC obj's underlying member method code with example usage: obj._compute_lambda(Y, X) and returns: lam