class_docstr: Class to handle multiple one-sample mixed effects models
      
docstr: Compute the log-likelihood of (Y, V1) under the model
  
  Parameters
  ----------
  Y, array of shape (n_samples, n_tests) or (n_samples)
     the estimated effects
  V1, array of shape (n_samples, n_tests) or (n_samples)
      first-level variance
  
  Returns
  -------
  logl: array of shape self.n_tests,
        the log-likelihood of the model
docstr_tok: ["Compute","the","log-likelihood","of","(","Y",",","V1",")","under","the","model","Parameters","--","--","--","--","--","Y",",","array","of","shape","(","n_samples",",","n_tests",")","or","(","n_samples",")","the","estimated","effects","V1",",","array","of","shape","(","n_samples",",","n_tests",")","or","(","n_samples",")","first-level","variance","Returns","--","--","--","-","logl",":","array","of","shape","self.n_tests",",","the","log-likelihood","of","the","model"]
code: def log_like(self, Y, V1):
      """Compute the log-likelihood of (Y, V1) under the model
  
      Parameters
      ----------
      Y, array of shape (n_samples, n_tests) or (n_samples)
         the estimated effects
      V1, array of shape (n_samples, n_tests) or (n_samples)
          first-level variance
  
      Returns
      -------
      logl: array of shape self.n_tests,
            the log-likelihood of the model
      """
      Y, V1 = check_arrays(Y, V1)
      tvar = self.V2 + V1
      logl = np.sum(((Y - self.Y_) ** 2) / tvar, 0)
      logl += np.sum(np.log(tvar), 0)
      logl += np.log(2 * np.pi) * Y.shape[0]
      logl *= -0.5
      return logl
  
file_path: nipy/nipy/algorithms/statistics/mixed_effects_stat.py
imports: import numpy
label_desc: Use image node_id 2 for calling the MixedEffectsModel obj's underlying member method code with example usage: obj.log_like(Y, V1) and returns: logl