simulate.ctmm | R Documentation |
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.
predict(object,...)
## S3 method for class 'ctmm'
predict(object,data=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE,...)
## S3 method for class 'telemetry'
predict(object,CTMM=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE,...)
simulate(object,nsim=1,seed=NULL,...)
## S3 method for class 'ctmm'
simulate(object,nsim=1,seed=NULL,data=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE,
precompute=FALSE,...)
## S3 method for class 'telemetry'
simulate(object,nsim=1,seed=NULL,CTMM=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE,
precompute=FALSE,...)
object |
A |
data |
Optional |
CTMM |
A |
VMM |
An optional vertical |
t |
Optional array of numeric time values over which the process will be predicted or simulated. |
dt |
Timestep to space the prediction or simulation over if |
res |
Average number of locations to predict or simulate per |
complete |
Additionally calculate timestamps and geographic coordinates. |
nsim |
Generates a list of |
seed |
Optional random seed to fix. |
precompute |
Precalculate matrices of the Kalman filter (see details). |
... |
Unused options. |
The prediction or simulation necessarily requires a ctmm
model object.
If a telemetry
data
object is supplied, the output will be conditional on the data
(i.e., simulations that run through the data).
If no data
is provided then the output will be purely Gaussian, and times t
must be provided.
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.
The precompute
option can speed up calculations of multiple simulations of the same model, data, and irregular sampling schedule.
First run simulate
with precompute=TRUE
to calculate and store all of the necessary matrices of the Kalman filter.
A simulated telemetry
object will be produced, as usual, and the precomputed objects are stored in the environment.
Subsequent simulations with precompute=-1
will then apply these precomputed matrices for a computational cost savings.
If the sampling schedule is irregular, then this can result in faster simulations.
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
.
Predictions are autocorrelated and should not be treated as data.
C. H. Fleming.
C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, W. F. Fagan, “From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales”, The American Naturalist, 183:5, E154-E167 (2014) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1086/675504")}.
C. H. Fleming, D. Sheldon, E. Gurarie, W. F. Fagan, S. LaPoint, J. M. Calabrese, “Kálmán filters for continuous-time movement models”, Ecological Informatics, 40, 8-21 (2017) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecoinf.2017.04.008")}.
ctmm.fit
#Load package
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.