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 |

`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` |
Dead-Reckoned 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 Dead-Reckoned path and gps measurements for an accurate and high-resolution 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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