mnllCCRW: Maximum likelihood estimation of the CCRW
In MarieAugerMethe/CCRWvsLW: Searching strategy movement models

Description Usage Arguments Details References See Also Examples

Minimize the negative log-likelihood for the CCRW though the EM-algorithm.

1	mnllCCRW(SL, TA, TA_C, missL, notMisLoc, SLmin, tol=5e-5)

`SL`	numeric vector containing the step lengths
`TA`	numeric vector containing the turning angles
`TA_C`	circular object containing the turning angles
`missL`	integer vector containing the number of time step between two steps. If no missing location it will be 1.
`notMisLoc`	integer vector containing the index of the locations that are not missing
`SLmin`	one numeric value representing the minimum step length
`tol`	double: value that indicates the maximum allowed difference between the parameters, default 5e-5

This function uses the function emHMM to minimize the negative log-likelihood. Note that emHMM requires setting starting parameter values. To decrease the chances of having results associated with a local peak in the likelihood, the function mnllCCRW applies the EM-algorithm function emHMM to set of different starting values combinations and choose the overall lowest negative log-likelihood estimate. To explore the effect of starting values, use directly emHMM function.

Please refer to Auger-Methe, M., A.E. Derocher, M.J. Plank, E.A. Codling, M.A. Lewis (2015-In Press) Differentiating the Levy walk from a composite correlated random walk. Methods in Ecology and Evolution. Preprint available at http://arxiv.org/abs/1406.4355

emHMM

simPath <- simmCCRW(500,0.9,0.9,0.1,0.01,5,1)
formPath <- movFormat(simPath)
mnllCCRW(formPath$SL, formPath$TA, formPath$TA_C, 
      formPath$missL, formPath$notMisLoc, formPath$SLmin)