Description Usage Arguments Value References See Also
Run Kalman filtering and smoothing at calculated MLE for parameters of calibration SSM. This is used to obtain point and covariance estimates for the actual OD flows X following estimation of other parameters.
1 2 |
mle |
numeric vector (length k+1) of parameters. theta[-1] = log(lambda), and theta[1] = log(phi) |
Ft |
evolution matrix (k x k) for OD flows; include fixed |
yt |
matrix (k x n) of observed link loads, one observation per column |
Zt |
observation matrix for system; should be routing matrix A |
Rt |
covariance matrix for observation equation; typically small and fixed |
k |
integer number of OD flows to infer |
tau |
numeric power parameter for mean-variance relationship |
initScale |
numeric inflation factor for time-zero state covariance; defaults to steady-state variance setting |
nugget |
small positive value to add to diagonal of state evolution covariance matrix to ensure numerical stability |
numeric marginal log-likelihood obtained via Kalman smoothing
list containing result of Kalman smoothing; see
SSModel
and KFS
for details
A.W. Blocker and E.M. Airoldi. Deconvolution of mixing time series on a graph. Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) 51-60, 2011.
Other calibrationModel: calibration_ssm
;
llCalibration
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