Ksmooth0 {astsa} | R Documentation |

Returns both the filtered values and smoothed values for the state-space model.

Ksmooth0(num, y, A, mu0, Sigma0, Phi, cQ, cR)

`num` |
number of observations |

`y` |
data matrix, vector or time series |

`A` |
time-invariant observation matrix |

`mu0` |
initial state mean vector |

`Sigma0` |
initial state covariance matrix |

`Phi` |
state transition matrix |

`cQ` |
Cholesky-type decomposition of state error covariance matrix Q – see details below |

`cR` |
Cholesky-type decomposition of observation error covariance matrix R – see details below |

`cQ`

and `cR`

are the Cholesky-type decompositions of `Q`

and `R`

. In particular, `Q = t(cQ)%*%cQ`

and `R = t(cR)%*%cR`

is all that is required (assuming `Q`

and `R`

are valid covariance matrices).

`xs ` |
state smoothers |

`Ps ` |
smoother mean square error |

`x0n ` |
initial mean smoother |

`P0n ` |
initial smoother covariance |

`J0 ` |
initial value of the J matrix |

`J ` |
the J matrices |

`xp ` |
one-step-ahead prediction of the state |

`Pp ` |
mean square prediction error |

`xf ` |
filter value of the state |

`Pf ` |
mean square filter error |

`like ` |
the negative of the log likelihood |

`Kn ` |
last value of the gain |

D.S. Stoffer

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts are https://www.stat.pitt.edu/stoffer/tsa4/ and https://www.stat.pitt.edu/stoffer/tsda/.

[Package *astsa* version 1.14 Index]