mnllTBW: Maximum likelihood estimation of the TBW

In MarieAugerMethe/CCRWvsLW: Searching strategy movement models

Description

Minimize the negative log-likelihood for the TBW.

Usage

mnllTBW(SL, TA, SLmin=min(SL), SLmax=max(SL), lambda_BW=(1/mean(SL-SLmin)))
nllTBW(SL,TA,x,parF=list('SLmin'=SLmin,'SLmax'=SLmax))

Arguments

SL	numeric vector containing the step lengths
TA	numeric vector containing the turning angles
SLmin	one numeric value representing the minimum step length, default min(SL)
SLmax	one numeric value representing the maximum step length, default max(SL)
lambda_BW	one numeric value for the starting value of lambda, the default is the analytical solution BW
x	one numeric value for log lambda
parF	list for fix parameter values, see default

Details

The mnllTBW function uses the value the analytical solution for the BW as the starting value of the numerical minimser to get lambda for the TBW. It returns the parameter values, the minimum negative log likelihood, AIC and AICc values.

The nllTBW function evaluates the negative log likelihood value.

References

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

Examples

simPath <- simmBW(500,0.1,1)
formPath <- movFormat(simPath)
mnllTBW(formPath$SL, formPath$TA)

MarieAugerMethe/CCRWvsLW documentation built on May 7, 2019, 2:50 p.m.