class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN DYNAMIC REGRESSION MODELS ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
  
  family : 
      e.g. pf.Normal(0,1)
docstr: Creates simplest approximating model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  T, Z, R, Q : np.array
      State space matrices used in KFS algorithm
  
  h_approx : float
      Value to use for the H matrix
  
  Returns
  ----------
  
  H : np.array
      Approximating measurement variance matrix
  
  mu : np.array
      Approximating measurement constants
docstr_tok: ["Creates","simplest","approximating","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","T",",","Z",",","R",",","Q",":","np.array","State","space","matrices","used","in","KFS","algorithm","h_approx",":","float","Value","to","use","for","the","H","matrix","Returns","--","--","--","--","--","H",":","np.array","Approximating","measurement","variance","matrix","mu",":","np.array","Approximating","measurement","constants"]
code: def _general_approximating_model(self, beta, T, Z, R, Q, h_approx):
      """Creates simplest approximating model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      T, Z, R, Q : np.array
          State space matrices used in KFS algorithm
  
      h_approx : float
          Value to use for the H matrix
  
      Returns
      ----------
  
      H : np.array
          Approximating measurement variance matrix
  
      mu : np.array
          Approximating measurement constants
      """
  
      H = np.ones(self.data.shape[0]) * h_approx
      mu = np.zeros(self.data.shape[0])
  
      return H, mu
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .dynlin import *
label_desc: Use image node_id 15 for calling the NDynReg obj's underlying member method code with example usage: obj._general_approximating_model(beta, T, Z, R, Q, h_approx) and returns: H,  mu