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,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE,...) # S3 method for telemetry predict(object,CTMM=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE,...) simulate(object,nsim=1,seed=NULL,...) # S3 method for ctmm simulate(object,nsim=1,seed=NULL,data=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE, precompute=FALSE,...) # S3 method for telemetry simulate(object,nsim=1,seed=NULL,CTMM=NULL,VMM=NULL,t=NULL,dt=NULL,res=1,complete=FALSE, precompute=FALSE,...)
An optional vertical
Optional array of numeric time values over which the process will be predicted or simulated.
Timestep to space the prediction or simulation over if
Average number of locations to predict or simulate per
Additionally calculate timestamps and geographic coordinates.
Not yet supported.
Optional random seed to fix.
Precalculate matrices of the Kalman filter (see details).
The prediction or simulation necessarily requires a
ctmm model object.
data object is supplied, the output will be conditional on the
data (i.e., simulations that run through the data).
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
t argument fixes the output times to a specific array of times.
res arguments are relative to the sampling schedule present in the optional
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.
precompute option can speed up calculations of multiple simulations of the same model, data, and irregular sampling schedule.
precompute=TRUE to calculate and store all of the necessary matrices of the Kalman filter.
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
y, or a predicted
telemetry object that also includes
y covariances for the location point estimates
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) 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) doi: 10.1016/j.ecoinf.2017.04.008 .
C. H. Fleming.
Predictions are autocorrelated and should not be treated as data.