Simulate data from our Bayesian melding model with Brownian Bridge and Brownian Motion (See the model description in BMAnimalTrack
).
1 2 
T 
Number of time points in the animal's path and DR path. 
K 
Number of GPS observations. 
s2H 
Variance parameter for Brownian Bridge. 
s2D 
Variance parameter for the Brownian motion. 
s2G 
Variance of the measurement error in the GPS observations. 
gind 
Optional. The time points where the GPS observations are obtained. Default is randomly generating from 1:T. 
betaVec 
Coefficients in the function h(t). When unspecified, no parametric bias term is considered. 
dMx 
Design matrix of dimension T. Default the polynomials. 
A 
Start point of the path. Default 0. 
B 
End point of the path. Default 0. 
scale 
Logical (TRUE of FALSE). Whether to standardize the columns of 
A data list with the following elements:
eta 
The simulated path of the animal, 
Y 
The GPS observations, 
Ytime 
The time points where the GPS observations are available, 
X 
DeadReckoned path 
Yang (Seagle) Liu <yang.liu@stat.ubc.ca>
Liu, Y., Battaile, B. C., Zidek, J. V., and Trites, A. (2014). Bayesian melding of the DeadReckoned path and gps measurements for an accurate and highresolution path of marine mammals. arXiv preprint arXiv: 1411.6683.
1 2 3 4 5 6 7 8  set.seed(1)
#Generating data from our
dlist < dataSim(T=100, K=10, s2H=1, s2D=0.1, betaVec=c(1))
gpsObs < dlist$Y
gpsTime < dlist$Ytime
drPath < dlist$X
wlist < as.dataList(drPath, gpsObs, gpsTime, timeUnit=1, s2G=0.01, dUnit=1, betaOrder=1)
##Examples continues in function "as.dataList".

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