bsam (version 1.1.3)

bsam-package: Fit Bayesian state-space models to animal tracking data

Description

Models provided are DCRW (for location filtering), DCRWS (for location filtering and behavioural state estimation), and their hierarchical versions (hDCRW, hDCRWS) to estimate parameters jointly across multiple individual tracking datasets. The models are fit in JAGS using Markov chain Monte Carlo simulation methods. The models are intended to be fit to Argos satellite tracking data but options exist to allow fits to other tracking data types (type ?fit_ssm for details).

Details

Package:	bsam
Type:	Package
Version:	1.1.2
Date:	2017-07-01
License:	GPL-2
LazyLoad:	yes

Fit Bayesian state-space models to Argos satellite tracking data. Models provided are DCRW - for location filtering; DCRWS - for location filtering and behavioural state estimation with 2 behavioural states; hDCRW and hDCRWS - hierarchical models for location filtering only, and location filtering with behavioural state estimation, respectively, across multiple animals.

The hierarchical models may provide improved location and/or behavioural state estimates compared to fitting DCRW/DCRWS to individual datasets.

References

Jonsen ID, Mills Flemming J, Myers RA (2005) Robust state-space modeling of animal movement data. Ecology 86:2874-2880

Jonsen ID (2016) Joint estimation over multiple individuals improves behavioural state inference from animal movement data. Scientific Reports 6:20625

See Also

fit_ssm

Examples

# NOT RUN {
# Fit DCRW model for state filtering and regularization -
# using trivial adapt & samples values for speed
data(ellie1)
fit <- fit_ssm(ellie1, model = "DCRW", tstep = 1, adapt = 10, samples = 100, 
              thin = 1, span = 0.2)
diag_ssm(fit)
map_ssm(fit)
plot_fit(fit)
# }