class_docstr: Inherits time series methods from TSM class.
  
  **** LOCAL LEVEL MODEL ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
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docstr: Creates the negative log likelihood of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  The negative log logliklihood of the model
docstr_tok: ["Creates","the","negative","log","likelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","The","negative","log","logliklihood","of","the","model"]
code: def neg_loglik(self, beta):
      """Creates the negative log likelihood of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      The negative log logliklihood of the model
      """
      _, _, _, F, v = self._model(self.data, beta)
      loglik = 0.0
      for i in range(0, self.data.shape[0]):
          loglik += np.linalg.slogdet(F[:, :, i])[1] + np.dot(
              v[i], np.dot(np.linalg.pinv(F[:, :, i]), v[i])
          )
      return -(
          -((self.data.shape[0] / 2) * np.log(2 * np.pi))
          - 0.5 * loglik.T[0].sum()
      )
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 6 for calling the LLEV obj's underlying member method code with example usage: obj.neg_loglik(beta) and returns: unknown