Estimation of the parameters in the general state space model via the EM algorithm. Inputs are not allowed; see the note.
EM1(num, y, A, mu0, Sigma0, Phi, cQ, cR, max.iter = 100, tol = 0.001)Estimate of Phi
Estimate of Q
Estimate of R
Estimate of initial state mean
Estimate of initial state covariance matrix
-log likelihood at each iteration
number of iterations to convergence
relative tolerance at convergence
number of observations
observation vector or time series; use 0 for missing values
observation matrices, an array with dim=c(q,p,n); use 0 for missing values
initial state mean
initial state covariance matrix
state transition matrix
Cholesky-like decomposition of state error covariance matrix Q -- see details below
R is diagonal here, so cR = sqrt(R) -- also, see details below
maximum number of iterations
relative tolerance for determining convergence
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/.