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 |
Practically, the script only requires that Q or R may be reconstructed as t(cQ)%*%(cQ)
or t(cR)%*%(cR)
, respectively, which allows more flexibility.
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
http://www.stat.pitt.edu/stoffer/tsa4/
See also http://www.stat.pitt.edu/stoffer/tsa4/chap6.htm for an explanation of the difference between levels 0, 1, and 2.