ctmm (version 0.3.1)

simulate.ctmm: Simulate a continuous-time movement model

Description

This function generates an $O(n)$ hidden-Markov simulation of continuous-time movement models described in Fleming et al (2014) and Fleming et al (2015).

Usage

## S3 method for class 'ctmm':
simulate(object,nsim=1,seed=NULL,data=NULL,t=NULL,dt=NULL,res=1,...)

Arguments

object

A ctmm movement-model in the same format as the output of ctmm.fit or variogram.fit.

nsim

Not yet supported.

seed

Optional random seed to fix.

data

Optional telemetry object on which the simulation will be conditioned.

t

Array of numeric time values over which the process will be simulated.

dt

Timestep to run simulation over if data is specified.

res

Average number of locations to simulate per data time.

...

Unused options.

Value

• A simulated animal-tracking telemetry object with components t, x, and y.

Details

The simulation can be conditioned off of data, if specified. Otherwise, the simulation will be Gaussian. Details of the movement model parameters can be found in ctmm.fit.

References

C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, and W. F. Fagan. (2014). From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales. http://www.jstor.org/discover/10.1086/675504{The American Naturalist, 183(5), E154-E167.} C. H. Fleming and Y. Subasi and J. M. Calabrese. (2015). A maximum-entropy description of animal movement. http://journals.aps.org/pre/abstract/10.1103/PhysRevE.91.032107{Physical Review E, 91, 032107.}

See Also

ctmm.fit

Examples

#Load package and data
library(ctmm)

#prepare simulation parameters
t <- 1:1000
MODEL <- ctmm(tau=c(100,10),sigma=10,mu=c(0,0))

#simulate data
SIM <- simulate(MODEL,t=t)

#plot data with Gaussian model
plot(SIM,CTMM=MODEL)