Kfilter2 {astsa} | R Documentation |
Returns the filtered values for the state space model. In addition, the script returns the evaluation of the likelihood at the given parameter values and the innovation sequence.
Kfilter2(num, y, A, mu0, Sigma0, Phi, Ups, Gam, Theta, cQ, cR, S, input)
num |
number of observations |
y |
data matrix, vector or time series |
A |
time-varying observation matrix, an array with |
mu0 |
initial state mean |
Sigma0 |
initial state covariance matrix |
Phi |
state transition matrix |
Ups |
state input matrix; use |
Gam |
observation input matrix; use |
Theta |
state error pre-matrix |
cQ |
Cholesky decomposition of state error covariance matrix Q – see details below |
cR |
Cholesky-type decomposition of observation error covariance matrix R – see details below |
S |
covariance-type matrix of state and observation errors |
input |
matrix or vector of inputs having the same row dimension as y; use |
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).
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 |
innov |
innovation series |
sig |
innovation covariances |
K |
last value of the gain, needed for smoothing |
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/.