Usage
bayesianDynamicFilter(Y, A, prior, lambda0, sigma0, phi0, rho = 0.1, tau = 2, m = 1000, verbose = FALSE, Xdraws = 5 * m, Xburnin = m, Movedraws = 10, nThresh = 10, aggregate = FALSE, backward = FALSE, tStart = 1)
Arguments
Y
matrix (n x l) of observed link loads over time,
one observation per row
A
routing matrix (l x k) for network; must be of
full row rank
prior
list containing priors for lambda and phi;
must have
- mu, a matrix (n x k) containing
the prior means for the log-change in each lambda at each
time
- sigma, a matrix (n x k) containing the prior
standard deviations for the log-change in each lambda at
each time
- a list phi, containing the numeric prior
df and a numeric vector scale of length n
lambda0
numeric vector (length k) of time 0 prior
means for OD flows
sigma0
numeric vector (length k) of time 0 prior
standard deviations for OD flows
phi0
numeric starting value for phi at time 0
rho
numeric fixed autoregressive parameter for
dynamics on lambda; see reference for details
tau
numeric fixed power parameter for variance
structure on truncated normal noise; see reference for
details
m
integer number of particles to use
verbose
logical activates verbose diagnostic
output
Xdraws
integer number of draws to perform for
xsample RDA
Xburnin
integer number of burnin draws to discard
for xsample proposals RDA in addition to baseline
number of draws
Movedraws
integer number of iterations to run for
each move step
nThresh
numeric effective number of independent
particles below which redraw will be performed
aggregate
logical to activate aggregation of MCMC
results; highly
backward
logical to activate reverse filtering
(for smoothing
tStart
integer time index to begin iterations
from