| LDS_EM {ldsr} | R Documentation |
Learn LDS model
Description
Estimate the hidden state and model parameters given observations and exogenous inputs using the EM algorithm. This is the key backend routine of this package.
Usage
LDS_EM(y, u, v, theta0, niter = 1000L, tol = 1e-05)
Arguments
y |
Observation matrix (may need to be normalized and centered before hand) (q rows, T columns) |
u |
Input matrix for the state equation (m_u rows, T columns) |
v |
Input matrix for the output equation (m_v rows, T columns) |
theta0 |
A vector of initial values for the parameters |
niter |
Maximum number of iterations, default 1000 |
tol |
Tolerance for likelihood convergence, default 1e-5. Note that the log-likelihood is normalized |
Value
A list of model results
theta: model parameters (A, B, C, D, Q, R, mu1, V1) resulted from Mstep
fit: results of Estep
liks : vector of loglikelihood over the iteration steps
Note
This code only works on one dimensional state and output at the moment. Therefore, transposing is skipped, and matrix inversion is treated as /, and log(det(Sigma)) is treated as log(Sigma).