class_docstr: Class to hold results from fitting a state space model.
  
  Parameters
  ----------
  model : MLEModel instance
      The fitted model instance
  params : ndarray
      Fitted parameters
  
  Attributes
  ----------
  model : Model instance
      A reference to the model that was fit.
  nobs : float
      The number of observations used to fit the model.
  params : ndarray
      The parameters of the model.
class_docstr_tok: ["Class","to","hold","results","from","fitting","a","state","space","model",".","Parameters","--","--","--","--","--","model",":","MLEModel","instance","The","fitted","model","instance","params",":","ndarray","Fitted","parameters","Attributes","--","--","--","--","--","model",":","Model","instance","A","reference","to","the","model","that","was","fit",".","nobs",":","float","The","number","of","observations","used","to","fit","the","model",".","params",":","ndarray","The","parameters","of","the","model","."]
docstr: Summarize the Model
  
  Parameters
  ----------
  alpha : float, optional
      Significance level for the confidence intervals. Default is 0.05.
  start : int, optional
      Integer of the start observation. Default is 0.
  model_name : str
      The name of the model used. Default is to use model class name.
  
  Returns
  -------
  summary : Summary instance
      This holds the summary table and text, which can be printed or
      converted to various output formats.
  
  See Also
  --------
  statsmodels.iolib.summary.Summary
docstr_tok: ["Summarize","the","Model","Parameters","--","--","--","--","--","alpha",":","float",",","optional","Significance","level","for","the","confidence","intervals",".","Default","is","0.05.","start",":","int",",","optional","Integer","of","the","start","observation",".","Default","is","0.","model_name",":","str","The","name","of","the","model","used",".","Default","is","to","use","model","class","name",".","Returns","--","--","--","-","summary",":","Summary","instance","This","holds","the","summary","table","and","text",",","which","can","be","printed","or","converted","to","various","output","formats",".","See","Also","--","--","--","--","statsmodels.iolib.summary.Summary"]
code: def summary(
      self,
      alpha=0.05,
      start=None,
      title=None,
      model_name=None,
      display_params=True,
  ):
      """
      Summarize the Model
  
      Parameters
      ----------
      alpha : float, optional
          Significance level for the confidence intervals. Default is 0.05.
      start : int, optional
          Integer of the start observation. Default is 0.
      model_name : str
          The name of the model used. Default is to use model class name.
  
      Returns
      -------
      summary : Summary instance
          This holds the summary table and text, which can be printed or
          converted to various output formats.
  
      See Also
      --------
      statsmodels.iolib.summary.Summary
      """
      from statsmodels.iolib.summary import Summary
  
      # Model specification results
      model = self.model
      if title is None:
          title = "Statespace Model Results"
  
      if start is None:
          start = 0
      if self.model._index_dates:
          ix = self.model._index
          d = ix[start]
          sample = ["%02d-%02d-%02d" % (d.month, d.day, d.year)]
          d = ix[-1]
          sample += ["- " + "%02d-%02d-%02d" % (d.month, d.day, d.year)]
      else:
          sample = [str(start), " - " + str(self.nobs)]
  
      # Standardize the model name as a list of str
      if model_name is None:
          model_name = model.__class__.__name__
  
      # Diagnostic tests results
      try:
          het = self.test_heteroskedasticity(method="breakvar")
      except Exception:  # FIXME: catch something specific
          het = np.array([[np.nan] * 2])
      try:
          lb = self.test_serial_correlation(method="ljungbox")
      except Exception:  # FIXME: catch something specific
          lb = np.array([[np.nan] * 2]).reshape(1, 2, 1)
      try:
          jb = self.test_normality(method="jarquebera")
      except Exception:  # FIXME: catch something specific
          jb = np.array([[np.nan] * 4])
  
      # Create the tables
      if not isinstance(model_name, list):
          model_name = [model_name]
  
      top_left = [("Dep. Variable:", None)]
      top_left.append(("Model:", [model_name[0]]))
      for i in range(1, len(model_name)):
          top_left.append(("", ["+ " + model_name[i]]))
      top_left += [
          ("Date:", None),
          ("Time:", None),
          ("Sample:", [sample[0]]),
          ("", [sample[1]]),
      ]
  
      top_right = [
          ("No. Observations:", [self.nobs]),
          ("Log Likelihood", ["%#5.3f" % self.llf]),
      ]
      if hasattr(self, "rsquared"):
          top_right.append(("R-squared:", ["%#8.3f" % self.rsquared]))
      top_right += [
          ("AIC", ["%#5.3f" % self.aic]),
          ("BIC", ["%#5.3f" % self.bic]),
          ("HQIC", ["%#5.3f" % self.hqic]),
      ]
  
      if hasattr(self, "filter_results"):
          if (
              self.filter_results is not None
              and self.filter_results.filter_concentrated
          ):
              top_right.append(("Scale", ["%#5.3f" % self.scale]))
      else:
          top_right.append(("Scale", ["%#5.3f" % self.scale]))
  
      if hasattr(self, "cov_type"):
          top_left.append(("Covariance Type:", [self.cov_type]))
  
      format_str = lambda array: [  # noqa:E731
          ", ".join([f"{i:.2f}" for i in array])
      ]
      diagn_left = [
          ("Ljung-Box (Q):", format_str(lb[:, 0, -1])),
          ("Prob(Q):", format_str(lb[:, 1, -1])),
          ("Heteroskedasticity (H):", format_str(het[:, 0])),
          ("Prob(H) (two-sided):", format_str(het[:, 1])),
      ]
  
      diagn_right = [
          ("Jarque-Bera (JB):", format_str(jb[:, 0])),
          ("Prob(JB):", format_str(jb[:, 1])),
          ("Skew:", format_str(jb[:, 2])),
          ("Kurtosis:", format_str(jb[:, 3])),
      ]
  
      summary = Summary()
      summary.add_table_2cols(
          self, gleft=top_left, gright=top_right, title=title
      )
      if len(self.params) > 0 and display_params:
          summary.add_table_params(
              self, alpha=alpha, xname=self.param_names, use_t=False
          )
      summary.add_table_2cols(
          self, gleft=diagn_left, gright=diagn_right, title=""
      )
  
      # Add warnings/notes, added to text format only
      etext = []
      if hasattr(self, "cov_type") and "description" in self.cov_kwds:
          etext.append(self.cov_kwds["description"])
      if self._rank < (len(self.params) - len(self.fixed_params)):
          cov_params = self.cov_params()
          if len(self.fixed_params) > 0:
              mask = np.ix_(
                  self._free_params_index, self._free_params_index
              )
              cov_params = cov_params[mask]
          etext.append(
              "Covariance matrix is singular or near-singular,"
              " with condition number %6.3g. Standard errors may be"
              " unstable." % _safe_cond(cov_params)
          )
  
      if etext:
          etext = [f"[{i + 1}] {text}" for i, text in enumerate(etext)]
          etext.insert(0, "Warnings:")
          summary.add_extra_txt(etext)
  
      return summary
  
imports: from collections import OrderedDict
  import contextlib
  import warnings
  import numpy
  import pandas
  from scipy.stats import norm
  from statsmodels.base.data import PandasData
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.eval_measures import aic, aicc, bic, hqic
  from statsmodels.tools.sm_exceptions import PrecisionWarning
  from statsmodels.tools.numdiff import _get_epsilon, approx_fprime, approx_fprime_cs, approx_hess_cs
  from statsmodels.tools.tools import pinv_extended
  import statsmodels.tsa.base.tsa_model
  from statsmodels.tsa.statespace.tools import _safe_cond
label_desc: Use image node_id 22 for calling the StateSpaceMLEResults obj's underlying member method code with example usage: obj.summary(alpha, start, title, model_name, display_params) and returns: summary