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)one-step-ahead prediction of the state
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-varying observation matrix, an array with dim=c(q,p,n)
initial state mean
initial state covariance matrix
state transition matrix
state input matrix; use Ups = 0 if not needed
observation input matrix; use Gam = 0 if not needed
Cholesky-type decomposition of state error covariance matrix Q -- see details below
Cholesky-type decomposition of observation error covariance matrix R -- see details below
matrix or vector of inputs having the same row dimension as y; use input = 0 if not needed
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/.