Returns the filtered values for the basic time invariant state-space model; inputs are not allowed.
Kfilter0(num, y, A, mu0, Sigma0, Phi, cQ, cR)one-step-ahead state prediction
mean square prediction error
filter value of the state
mean square filter error
the negative of the log likelihood
innovation series
innovation covariances
last value of the gain, needed for smoothing
number of observations
data matrix, vector or time series
time-invariant observation matrix
initial state mean vector
initial state covariance matrix
state transition matrix
Cholesky-type decomposition of state error covariance matrix Q -- see details below
Cholesky-type decomposition of observation error covariance matrix R -- see details below
D.S. Stoffer
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).
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/.