class_docstr: Results from applying the Kalman filter to a state space model.
  
  Parameters
  ----------
  model : Representation
      A Statespace representation
  
  Attributes
  ----------
  nobs : int
      Number of observations.
  nobs_diffuse : int
      Number of observations under the diffuse Kalman filter.
  k_endog : int
      The dimension of the observation series.
  k_states : int
      The dimension of the unobserved state process.
  k_posdef : int
      The dimension of a guaranteed positive definite
      covariance matrix describing the shocks in the
      measurement equation.
  dtype : dtype
      Datatype of representation matrices
  prefix : str
      BLAS prefix of representation matrices
  shapes : dictionary of name,tuple
      A dictionary recording the shapes of each of the
      representation matrices as tuples.
  endog : ndarray
      The observation vector.
  design : ndarray
      The design matrix, :math:`Z`.
  obs_intercept : ndarray
      The intercept for the observation equation, :math:`d`.
  obs_cov : ndarray
      The covariance matrix for the observation equation :math:`H`.
  transition : ndarray
      The transition matrix, :math:`T`.
  state_intercept : ndarray
      The intercept for the transition equation, :math:`c`.
  selection : ndarray
      The selection matrix, :math:`R`.
  state_cov : ndarray
      The covariance matrix for the state equation :math:`Q`.
  missing : array of bool
      An array of the same size as `endog`, filled
      with boolean values that are True if the
      corresponding entry in `endog` is NaN and False
      otherwise.
  nmissing : array of int
      An array of size `nobs`, where the ith entry
      is the number (between 0 and `k_endog`) of NaNs in
      the ith row of the `endog` array.
  time_invariant : bool
      Whether or not the representation matrices are time-invariant
  initialization : str
      Kalman filter initialization method.
  initial_state : array_like
      The state vector used to initialize the Kalamn filter.
  initial_state_cov : array_like
      The state covariance matrix used to initialize the Kalamn filter.
  initial_diffuse_state_cov : array_like
      Diffuse state covariance matrix used to initialize the Kalamn filter.
  filter_method : int
      Bitmask representing the Kalman filtering method
  inversion_method : int
      Bitmask representing the method used to
      invert the forecast error covariance matrix.
  stability_method : int
      Bitmask representing the methods used to promote
      numerical stability in the Kalman filter
      recursions.
  conserve_memory : int
      Bitmask representing the selected memory conservation method.
  filter_timing : int
      Whether or not to use the alternate timing convention.
  tolerance : float
      The tolerance at which the Kalman filter
      determines convergence to steady-state.
  loglikelihood_burn : int
      The number of initial periods during which
      the loglikelihood is not recorded.
  converged : bool
      Whether or not the Kalman filter converged.
  period_converged : int
      The time period in which the Kalman filter converged.
  filtered_state : ndarray
      The filtered state vector at each time period.
  filtered_state_cov : ndarray
      The filtered state covariance matrix at each time period.
  predicted_state : ndarray
      The predicted state vector at each time period.
  predicted_state_cov : ndarray
      The predicted state covariance matrix at each time period.
  forecast_error_diffuse_cov : ndarray
      Diffuse forecast error covariance matrix at each time period.
  predicted_diffuse_state_cov : ndarray
      The predicted diffuse state covariance matrix at each time period.
  kalman_gain : ndarray
      The Kalman gain at each time period.
  forecasts : ndarray
      The one-step-ahead forecasts of observations at each time period.
  forecasts_error : ndarray
      The forecast errors at each time period.
  forecasts_error_cov : ndarray
      The forecast error covariance matrices at each time period.
  llf_obs : ndarray
      The loglikelihood values at each time period.
class_docstr_tok: ["Results","from","applying","the","Kalman","filter","to","a","state","space","model",".","Parameters","--","--","--","--","--","model",":","Representation","A","Statespace","representation","Attributes","--","--","--","--","--","nobs",":","int","Number","of","observations",".","nobs_diffuse",":","int","Number","of","observations","under","the","diffuse","Kalman","filter",".","k_endog",":","int","The","dimension","of","the","observation","series",".","k_states",":","int","The","dimension","of","the","unobserved","state","process",".","k_posdef",":","int","The","dimension","of","a","guaranteed","positive","definite","covariance","matrix","describing","the","shocks","in","the","measurement","equation",".","dtype",":","dtype","Datatype","of","representation","matrices","prefix",":","str","BLAS","prefix","of","representation","matrices","shapes",":","dictionary","of","name",",","tuple","A","dictionary","recording","the","shapes","of","each","of","the","representation","matrices","as","tuples",".","endog",":","ndarray","The","observation","vector",".","design",":","ndarray","The","design","matrix",",",":","math",":","`","Z","`",".","obs_intercept",":","ndarray","The","intercept","for","the","observation","equation",",",":","math",":","`","d","`",".","obs_cov",":","ndarray","The","covariance","matrix","for","the","observation","equation",":","math",":","`","H","`",".","transition",":","ndarray","The","transition","matrix",",",":","math",":","`","T","`",".","state_intercept",":","ndarray","The","intercept","for","the","transition","equation",",",":","math",":","`","c","`",".","selection",":","ndarray","The","selection","matrix",",",":","math",":","`","R","`",".","state_cov",":","ndarray","The","covariance","matrix","for","the","state","equation",":","math",":","`","Q","`",".","missing",":","array","of","bool","An","array","of","the","same","size","as","`","endog","`",",","filled","with","boolean","values","that","are","True","if","the","corresponding","entry","in","`","endog","`","is","NaN","and","False","otherwise",".","nmissing",":","array","of","int","An","array","of","size","`","nobs","`",",","where","the","ith","entry","is","the","number","(","between","0","and","`","k_endog","`",")","of","NaNs","in","the","ith","row","of","the","`","endog","`","array",".","time_invariant",":","bool","Whether","or","not","the","representation","matrices","are","time-invariant","initialization",":","str","Kalman","filter","initialization","method",".","initial_state",":","array_like","The","state","vector","used","to","initialize","the","Kalamn","filter",".","initial_state_cov",":","array_like","The","state","covariance","matrix","used","to","initialize","the","Kalamn","filter",".","initial_diffuse_state_cov",":","array_like","Diffuse","state","covariance","matrix","used","to","initialize","the","Kalamn","filter",".","filter_method",":","int","Bitmask","representing","the","Kalman","filtering","method","inversion_method",":","int","Bitmask","representing","the","method","used","to","invert","the","forecast","error","covariance","matrix",".","stability_method",":","int","Bitmask","representing","the","methods","used","to","promote","numerical","stability","in","the","Kalman","filter","recursions",".","conserve_memory",":","int","Bitmask","representing","the","selected","memory","conservation","method",".","filter_timing",":","int","Whether","or","not","to","use","the","alternate","timing","convention",".","tolerance",":","float","The","tolerance","at","which","the","Kalman","filter","determines","convergence","to","steady-state",".","loglikelihood_burn",":","int","The","number","of","initial","periods","during","which","the","loglikelihood","is","not","recorded",".","converged",":","bool","Whether","or","not","the","Kalman","filter","converged",".","period_converged",":","int","The","time","period","in","which","the","Kalman","filter","converged",".","filtered_state",":","ndarray","The","filtered","state","vector","at","each","time","period",".","filtered_state_cov",":","ndarray","The","filtered","state","covariance","matrix","at","each","time","period",".","predicted_state",":","ndarray","The","predicted","state","vector","at","each","time","period",".","predicted_state_cov",":","ndarray","The","predicted","state","covariance","matrix","at","each","time","period",".","forecast_error_diffuse_cov",":","ndarray","Diffuse","forecast","error","covariance","matrix","at","each","time","period",".","predicted_diffuse_state_cov",":","ndarray","The","predicted","diffuse","state","covariance","matrix","at","each","time","period",".","kalman_gain",":","ndarray","The","Kalman","gain","at","each","time","period",".","forecasts",":","ndarray","The","one-step-ahead","forecasts","of","observations","at","each","time","period",".","forecasts_error",":","ndarray","The","forecast","errors","at","each","time","period",".","forecasts_error_cov",":","ndarray","The","forecast","error","covariance","matrices","at","each","time","period",".","llf_obs",":","ndarray","The","loglikelihood","values","at","each","time","period","."]
code: def __init__(self, model):
      super().__init__(model)
  
      # Setup caches for uninitialized objects
      self._kalman_gain = None
      self._standardized_forecasts_error = None
  
imports: import contextlib
  from warnings import warn
  import numpy
  from .representation import OptionWrapper, Representation, FrozenRepresentation
  from .tools import reorder_missing_matrix, reorder_missing_vector
  from .None import tools
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 1 to create a new FilterResults object from inherited base classes: FrozenRepresentation with example: obj = FilterResults(model)