ctmm (version 0.3.1)

ctmm.loglike: Calculate the log-likelihood of a continuous-time movement model

Description

This function evaluates an $O(n)$ Kalman-filter implementation of the exact non-Markovian likelihood function of continuous-time movement models described in Fleming et al (2014) and Fleming et al (2015), given 2D animal tracking data.

Usage

ctmm.loglike(data,CTMM,verbose=FALSE)

Arguments

data

telemetry data object for the 2D timeseries.

CTMM

ctmm model object with the necessary component tau and optional component isotropic.

verbose

Return a ctmm model object, including the log-likelihood, instead of only the log-likelihood.

Value

• By default, only the log-likelihood value is returned, given the specified model parameters and data. If verbose=TRUE, then a ctmm movement-model object list is returned with all of the components of CTMM plus following: [object Object],[object Object],[object Object],[object Object],[object Object]

Details

See ctmm.fit for a complete description of the parameter inputs.

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)
data(buffalo)

#Extract movement data for a single animal
cilla <- buffalo[[1]]

#Calculate likelihood of ~10 day range crossing time with discontinuous velocity
MODEL <- ctmm(tau=10*24*60*60)
ctmm.loglike(cilla,MODEL,verbose=TRUE)