simm.crw {adehabitatLT}  R Documentation 
This function simulates a correlated random walk
simm.crw(date=1:100, h = 1, r = 0, x0=c(0,0), id="A1", burst=id, typeII=TRUE, proj4string=CRS())
date 
a vector indicating the date (in seconds) at which
relocations should be simulated. This vector can be of class

h 
the scaling parameter for the movement length 
r 
The concentration parameter for wrapped normal distribution of turning angles 
x0 
a vector of length 2 containing the coordinates of the startpoint of the trajectory 
id 
a character string indicating the identity of the simulated
animal (see 
burst 
a character string indicating the identity of the simulated
burst (see 
typeII 
logical. Whether the simulated trajectory should be of
type II ( 
proj4string 
a valid CRS object containing the projection
information (see 
Since the seminal paper of Kareiva and Shigesada (1983), most
biologists describe the trajectories of an animal with the help of
two distributions: the distribution of distances between successive
relocations, and the distribution of turning angles between successive
moves (relative angles in the class ltraj
). The CRW is
built iteratively. At each step of the simulation process,
the orientation of the move is drawn from a wrapped normal
distribution (with concentration parameter r
). The length of
the move is drawn from a chi distribution, multiplied by h *
sqrt(dt)
. h
is a scale parameter (the same as in the
function simm.brown()
, and the distribution is
multiplied by sqrt(t) to make it similar to the discretized Brownian
motion if r == 0
.
an object of class ltraj
This function requires the package CircStats
.
Clement Calenge clement.calenge@ofb.gouv.fr
Stephane Dray dray@biomserv.univlyon1.fr
Manuela Royer royer@biomserv.univlyon1.fr
Daniel Chessel chessel@biomserv.univlyon1.fr
Kareiva, P. M. & Shigesada, N. (1983) Analysing insect movement as a correlated random walk. Oecologia, 56: 234–238.
chi
, rwrpnorm
,
simm.brown
, ltraj
,
simm.crw
, simm.mba
suppressWarnings(RNGversion("3.5.0")) set.seed(876) u < simm.crw(1:500, r = 0.99, burst = "r = 0.99") v < simm.crw(1:500, r = 0.9, burst = "r = 0.9", h = 2) w < simm.crw(1:500, r = 0.6, burst = "r = 0.6", h = 5) x < simm.crw(1:500, r = 0, burst = "r = 0 (Uncorrelated random walk)", h = 0.1) z < c(u, v, w, x) plot(z, addpoints = FALSE, perani = FALSE)