ctmm
movement model (and optional telemetry
data to condition upon) these functions predict or simulate animal locations over a prescribed set of times.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,...)
ctmm
movement-model or telemetry
object, which requires an additional CTMM
argument.telemetry
object on which the prediction or simulation will be conditioned.data
is specified.data
time.ctmm
movement-model in the same format as the output of ctmm.fit
or variogram.fit
.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
.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.ctmm.fit
#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)
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