class_docstr: Inherits time series methods from TSM parent class.
  
  **** AUTOREGRESSIVE INTEGRATED MOVING AVERAGE EXOGENOUS (ARIMAX) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the univariate time series data that will be used.
  
  formula : string
      patsy string notation describing the regression
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  ma : int
      Field to specify how many MA lags the model will have.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  family : family object
      E.g pf.Normal(), pf.t(), pf.Laplace()...
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docstr: Obtains model scale, shape, skewness latent variables for
  a 2d array of simulations.
  
  Parameters
  ----------
  transformed_lvs : np.array
      Transformed latent variable vector (2d - with draws of each variable)
  
  Returns
  ----------
  - Tuple of np.arrays (each being scale, shape and skewness draws)
docstr_tok: ["Obtains","model","scale",",","shape",",","skewness","latent","variables","for","a","2d","array","of","simulations",".","Parameters","--","--","--","--","--","transformed_lvs",":","np.array","Transformed","latent","variable","vector","(","2d","-","with","draws","of","each","variable",")","Returns","--","--","--","--","--","-","Tuple","of","np.arrays","(","each","being","scale",",","shape","and","skewness","draws",")"]
code: def _get_scale_and_shape_sim(self, transformed_lvs):
      """Obtains model scale, shape, skewness latent variables for
      a 2d array of simulations.
  
      Parameters
      ----------
      transformed_lvs : np.array
          Transformed latent variable vector (2d - with draws of each variable)
  
      Returns
      ----------
      - Tuple of np.arrays (each being scale, shape and skewness draws)
      """
  
      if self.scale is True:
          if self.shape is True:
              model_shape = self.latent_variables.z_list[
                  -1
              ].prior.transform(transformed_lvs[-1, :])
              model_scale = self.latent_variables.z_list[
                  -2
              ].prior.transform(transformed_lvs[-2, :])
          else:
              model_shape = np.zeros(transformed_lvs.shape[1])
              model_scale = self.latent_variables.z_list[
                  -1
              ].prior.transform(transformed_lvs[-1, :])
      else:
          model_scale = np.zeros(transformed_lvs.shape[1])
          model_shape = np.zeros(transformed_lvs.shape[1])
  
      if self.skewness is True:
          model_skewness = self.latent_variables.z_list[
              -3
          ].prior.transform(transformed_lvs[-3, :])
      else:
          model_skewness = np.zeros(transformed_lvs.shape[1])
  
      return model_scale, model_shape, model_skewness
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .arma_recursions import arimax_recursion
label_desc: Use image node_id 5 for calling the ARIMAX obj's underlying member method code with example usage: obj._get_scale_and_shape_sim(transformed_lvs) and returns: model_scale,  model_shape,  model_skewness