class_docstr: Maximum Likelihood Estimation of Linear Model with t-distributed errors
  
  This is an example for generic MLE.
  
  Except for defining the negative log-likelihood method, all
  methods and results are generic. Gradients and Hessian
  and all resulting statistics are based on numerical
  differentiation.
docstr: Loglikelihood of linear model with t distributed errors.
  
  Parameters
  ----------
  params : ndarray
      The parameters of the model. The last 2 parameters are degrees of
      freedom and scale.
  
  Returns
  -------
  loglike : ndarray
      The log likelihood of the model evaluated at `params` for each
      observation defined by self.endog and self.exog.
  
  Notes
  -----
  .. math:: \ln L=\sum_{i=1}^{n}\left[-\lambda_{i}+y_{i}x_{i}^{\prime}\beta-\ln y_{i}!\right]
  
  The t distribution is the standard t distribution and not a standardized
  t distribution, which means that the scale parameter is not equal to the
  standard deviation.
  
  self.fixed_params and self.expandparams can be used to fix some
  parameters. (I doubt this has been tested in this model.)
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code: def nloglikeobs(self, params):
      """
      Loglikelihood of linear model with t distributed errors.
  
      Parameters
      ----------
      params : ndarray
          The parameters of the model. The last 2 parameters are degrees of
          freedom and scale.
  
      Returns
      -------
      loglike : ndarray
          The log likelihood of the model evaluated at `params` for each
          observation defined by self.endog and self.exog.
  
      Notes
      -----
      .. math:: \\ln L=\\sum_{i=1}^{n}\\left[-\\lambda_{i}+y_{i}x_{i}^{\\prime}\\beta-\\ln y_{i}!\\right]
  
      The t distribution is the standard t distribution and not a standardized
      t distribution, which means that the scale parameter is not equal to the
      standard deviation.
  
      self.fixed_params and self.expandparams can be used to fix some
      parameters. (I doubt this has been tested in this model.)
      """
      # print len(params),
      # store_params.append(params)
      if self.fixed_params is not None:
          # print 'using fixed'
          params = self.expandparams(params)
  
      beta = params[:-2]
      df = params[-2]
      scale = np.abs(params[-1])  # TODO check behavior around zero
      loc = np.dot(self.exog, beta)
      endog = self.endog
      x = (endog - loc) / scale
      # next part is stats.t._logpdf
      lPx = sps_gamln((df + 1) / 2) - sps_gamln(df / 2.0)
      lPx -= 0.5 * np_log(df * np_pi) + (df + 1) / 2.0 * np_log(
          1 + (x**2) / df
      )
      lPx -= np_log(scale)  # correction for scale
      return -lPx
  
file_path: statsmodels/statsmodels/miscmodels/tmodel.py
imports: import numpy
  from scipy import special, stats
  from statsmodels.base.model import GenericLikelihoodModel
  from statsmodels.tsa.arma_mle import Arma
label_desc: Use image node_id 4 for calling the TLinearModel obj's underlying member method code with example usage: obj.nloglikeobs(params) and returns: unknown