View source: R/simulateRACVM.R
simulateRACVM | R Documentation |
Simulates 2D correlated velocity movement model with rotation and advection
simulateRACVM(
tau = 1,
eta = 1,
omega = 0,
mu = 0,
v0 = NULL,
Tmax = 100,
dt = 0.01
)
tau |
characteristic time scale of autocorrelation |
eta |
random speed parameter. In the case where |
omega |
rotational velocity |
mu |
advective velocity (can be complex value where real and imaginary parts are x and y components of velocity) |
v0 |
initial velocity |
Tmax |
max time |
dt |
time interval of simulation |
This function simulates a Rotational-Advective CVM explicitly using the governing OU equation, using a discretization in which dt << tau.
a list with the following elements
the time vector
the (complex) vector of velocities
the (complex) vector of locations
a two-column matrix of x-y coordinates of locations
a named list of model parameters.
estimateRACVM
require(smoove)
racvm1 <- simulateRACVM(tau = 4, eta = 1, omega = 2, mu = 0, Tmax = 100, dt = .01)
racvm2 <- simulateRACVM(tau = 4, eta = 1, omega = 0, mu = 1, Tmax = 100, dt = .01)
racvm3 <- simulateRACVM(tau = 4, eta = 1, omega = 2, mu = 1, Tmax = 100, dt = .01)
layout(rbind(c(1,2),c(1,3)))
sapply(list(racvm1, racvm2, racvm3),
function(x)
with(x, plot(
Z,
asp = 1,
type = "l",
main = paste0("RACVM(",
paste(unlist(parameters),
collapse = ", "), ")")
)))
racvm1 <- simulateRACVM(tau = 3, eta = 32, omega = .5, mu = 0, Tmax = 10, dt = .1)
with(racvm1, scan_track(x = Re(Z), y = Im(Z), time = T))
# Compare with simulateRACVM2
# racvm1b <- simulateRACVM2(tau = 4, eta = 1, omega = 2, mu = 0, Tmax = 100, dt = .01)
# racvm2b <- simulateRACVM2(tau = 4, eta = 1, omega = 0, mu = 1, Tmax = 100, dt = .01)
# racvm3b <- simulateRACVM2(tau = 4, eta = 1, omega = 2, mu = 1, Tmax = 100, dt = .01)
#
# racvm1 <- simulateRACVM(tau = 4, eta = 1, omega = 1, mu = 0, Tmax = 10, dt = .01)
# racvm1b <- simulateRACVM2(tau = 4, eta = 1, omega = 1, mu = 0, Tmax = 10, dt = .01)
# with(racvm1, scan_track(x = Re(Z), y = Im(Z), time = T))
# with(racvm1b, scan_track(x = Re(Z), y = Im(Z), time = T))
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