Kfilter0 {astsa}R Documentation

Kalman Filter - Time Invariant Model

Description

Returns the filtered values for the basic time invariant state-space model; inputs are not allowed.

Usage

Kfilter0(num, y, A, mu0, Sigma0, Phi, cQ, cR)

Arguments

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

Details

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).

Value

xp

one-step-ahead state prediction

Pp

mean square prediction error

xf

filter value of the state

Pf

mean square filter error

like

the negative of the log likelihood

innov

innovation series

sig

innovation covariances

Kn

last value of the gain, needed for smoothing

Author(s)

D.S. Stoffer

References

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]