rMR function

Sampling from a Moving-Resting Process with Embedded Brownian Motion

Sampling from a Moving-Resting Process with Embedded Brownian Motion

A moving-resting process consists of two states: moving and resting. The transition between the two states is modeled by an alternating renewal process, with exponentially distributed duration. An animal stays at the same location while resting, and moves according to a Brownian motion while moving.

rMR(time, lamM, lamR, sigma, s0, dim = 2, state = FALSE) rMovRes(time, lamM, lamR, sigma, s0, dim = 2) rMRME(time, lamM, lamR, sigma, sig_err, s0, dim = 2, state = FALSE)

Arguments

  • time: time points at which observations are to be simulated
  • lamM: rate parameter of the exponential duration while moving
  • lamR: rate parameter of the exponential duration while resting
  • sigma: volatility parameter of the Brownian motion while moving
  • s0: the state at time 0, must be one of "m" or "r", for moving and resting, respectively
  • dim: (integer) dimension of the Brownian motion
  • state: indicates whether the simulation show the states at given time points.
  • sig_err: s.d. of Gaussian white noise

Returns

A data.frame whose first column is the time points and whose other columns are coordinates of the locations.

Examples

tgrid <- seq(0, 10, length=1001) ## make it irregularly spaced tgrid <- sort(sample(tgrid, 800)) dat <- rMR(tgrid, 1, 1, 1, "m") plot(dat[,1], dat[,2], xlab="t", ylab="X(t)", type='l') dat2 <- rMR(tgrid, 1, 1, 1, "m", state = TRUE) head(dat2) dat3 <- rMRME(tgrid, 1, 1, 1, 0.01, "m", state = TRUE) head(dat3) plot(dat3[,1], dat3[,3], xlab="t", ylab="Z(t)=X(t)+GWN(0.01)", type="l")

References

Yan, J., Chen, Y., Lawrence-Apfel, K., Ortega, I. M., Pozdnyakov, V., Williams, S., and Meyer, T. (2014) A moving-resting process with an embedded Brownian motion for animal movements. Population Ecology. 56(2): 401--415.

Pozdnyakov, V., Elbroch, L., Labarga, A., Meyer, T., and Yan, J. (2017) Discretely observed Brownian motion governed by telegraph process: estimation. Methodology and Computing in Applied Probability. doi:10.1007/s11009-017-9547-6.

  • Maintainer: Chaoran Hu
  • License: GPL (>= 3.0)
  • Last published: 2024-01-10