Kfilter1 {astsa} | R Documentation |
Returns both the predicted and filtered values for a linear state space model. Also evaluates the likelihood at the given parameter values.
Kfilter1(num, y, A, mu0, Sigma0, Phi, Ups, Gam, cQ, cR, 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 |
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 |
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 |
Kn |
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/.