smam (version 0.7.2)

rMRH: Sampling from a Moving-Resting-Handling Process with Embedded Brownian Motion

Description

A moving-resting-handling process consists of three states: moving, resting and handling. The transition between the three states is modeled by an alternating renewal process, with expenentially distributed duration. An animal stays at the same location while resting and handling (the choice of resting and handling depends on Bernoulli distribution), and moves according to a Brownian motion while moving state. The sequence of states is moving, resting or staying, moving, resting or staying ... or versus

Usage

rMRH(time, lamM, lamR, lamH, sigma, p, s0, dim = 2, state = FALSE)

Value

A data.frame whose first column is the time points and whose other columns are coordinates of the locations. If state is TRUE, the second column will be the simulation state.

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

lamH

rate parameter of the exponential duration while handling

sigma

volatility parameter of the Brownian motion while moving

p

probability of choosing resting, and 1-p is probability of choosing handling

s0

the state at time 0, must be one of "m" (moving), "r" (resting) or "h" (handling).

dim

(integer) dimension of the Brownian motion

state

indicates whether the simulation show the states at given time points.

Author

Chaoran Hu

References

Pozdnyakov, V., Elbroch, L.M., Hu, C., Meyer, T., and Yan, J. (2018+) On estimation for Brownian motion governed by telegraph process with multiple off states. <arXiv:1806.00849>

See Also

fitMRH for fitting model.

Examples

set.seed(06269)
tgrid <- seq(0, 8000, length.out=1001)
dat <- rMRH(time=tgrid, lamM=4, lamR=0.04, lamH=0.2,
            sigma=1000, p=0.5, s0="m", dim=2)
plot(dat$time, dat$X1, type='l')
plot(dat$time, dat$X2, type='l')
plot(dat$X1,   dat$X2, type='l')

set.seed(06269) ## show the usage of state
dat2 <- rMRH(time=tgrid, lamM=4, lamR=0.04, lamH=0.2,
             sigma=1000, p=0.5, s0="m", dim=2, state=TRUE)
head(dat)
head(dat2)