smooth_rcpp: Kalman smoother

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

Kalman smoother

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

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

Arguments

data	Matrix of data, including columns x, y, time, ID and state (in that order).
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

a named List containing the predicted locations and velocities, and variance of these estimates.

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.