ctmm (version 0.3.5)

simulate.ctmm:

Description

Given a ctmm movement model (and optional telemetry data to condition upon) these functions predict or simulate animal locations over a prescribed set of times.

Usage

predict(object,...)
# S3 method for ctmm
predict(object,data=NULL,t=NULL,dt=NULL,res=1,...)
# S3 method for telemetry
predict(object,CTMM=NULL,t=NULL,dt=NULL,res=1,...)
simulate(object,nsim=1,seed=NULL,...)
# S3 method for ctmm
simulate(object,nsim=1,seed=NULL,data=NULL,t=NULL,dt=NULL,res=1,...)
# S3 method for telemetry
simulate(object,nsim=1,seed=NULL,CTMM=NULL,t=NULL,dt=NULL,res=1,...)

Arguments

object

A ctmm movement-model or telemetry object, which requires an additional CTMM argument.

data

Optional telemetry object on which the prediction or simulation will be conditioned.

t

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

dt

Timestep to space the prediction or simulation over if data is specified.

res

Average number of locations to predict or simulate per data time.

CTMM

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.

...

Unused options.

Value

A simulated animal-tracking telemetry object with components t, x, and y, or a predicted telemetry object that also includes x-y covariances for the location point estimates x and y.

Details

The prediction or simulation necessarily requires a ctmm model object and optionally can be conditioned off of telemetry data, if specified. If no data is provided, the simulation will be purely Gaussian. Details of the movement model parameters can be found in ctmm.fit. The t argument fixes the output times to a specific array of times. The dt and res arguments are relative to the sampling schedule present in the optional telemetry object. The same span of time will be used, while dt will fix the sampling rate absolutely and res will fix the sampling rate relative to that of the data.

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. 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. C. H. Fleming and W. F. Fagan and T. Mueller and K. A. Olson and P. Leimgruber and J. M. Calabrese. (2016). Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data. http://onlinelibrary.wiley.com/doi/10.1890/15-1607/full.

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)