kalman_rcpp: Kalman filter

In dylanirion/MSctmm: Multi State Continous-Time Movement Modeling

Kalman filter

Description

This code is adapted from the package ctmm (Calabrese et al., 2016) crawl (Johnson et al., 2008), and MScrawl (Michelot and Blackwell, 2019).

Usage

kalman_rcpp(data, nbStates, param, fixmu, Hmat)

Arguments

data	Matrix of data, including columns x, y, time, ID and state (in that order).
nbStates	Integer number of states.
param	Vector of movement parameters (tau_vel, tau_pos, and sigma)
fixmu	Vector of mean locations for the OUF process (x, y)
Hmat	Matrix of observation error variance (four columns, and one row for each row of data)

Value

Log-likelihood

References

Calabrese, J.M., Fleming, C.H. and Gurarie, E. (2016). ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process. Methods Ecol Evol, 7: 1124-1132. doi:10.1111/2041-210X.12559

Fleming, C.H., Sheldon, D., Gurarie, E., Fagan, W.F., LaPoint, S., Calabrese, J.M. (2017). Kálmán filters for continuous-time movement models. Ecol Inform, 40: 8-21. doi:10.1016/j.ecoinf.2017.04.008

Johnson, D.S., London, J.M., Lea, M.A., and Durban, J.W. (2008). Continuous-time correlated random walk model for animal telemetry data. Ecology, 89: 1208-1215. doi:10.1890/07-1032.1

Michelot, T., Blackwell, P.G. (2019). State‐switching continuous‐time correlated random walks. Methods Ecol Evol, 10: 637-649. doi:10.1111/2041-210X.13154

dylanirion/MSctmm documentation built on Sept. 27, 2024, 3:41 a.m.