rMRH | R Documentation |
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
rMRH(time, lamM, lamR, lamH, sigma, p, s0, dim = 2, state = FALSE)
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. |
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.
Chaoran Hu
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>
fitMRH
for fitting model.
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)
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