bsam
Bayesian State-Space Models for Animal Movement
Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the 'moveHMM' package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as 'coda' and 'boa'.
bsam - Bayesian State-space models for Animal Movement.
bsam is an R package that fits Bayesian state-space models via JAGS to Argos satellite tracking data. The models filter error-prone Argos locations and estimate behavioural states associated with two fundamentally different movement patterns (directed, fast movements and relatively undirected, slow movements). The models can be fit to individual animal tracks or simultaneously to a group of individuals. Plotting functions are provided to help assess lack of MCMC convergence, map estimated tracks and visualise fit to the observations.
Read ?bsam for more details on functionality and ?fit_ssm for details and examples of how to use the package.
Installation
First ensure that you have a working copy of JAGS (>= 4.2.0) for the rjags package, see instructions below.
library(rjags)Get the released version of bsam from CRAN:
install.packages("bsam")Or download the current development version from GitHub:
# install.packages("devtools") devtools::install_github("ianjonsen/bsam")JAGS installation
Install JAGS: http://mcmc-jags.sourceforge.net/
News
bsam 1.1.2
- fixed indexing on priors for initial location state. A sufficiently short time step could cause the hierarchical models to crash on compilation.
bsam 1.1.1
- fixed the mis-ordering of animal id's in the summary and data output data.frames caused when a hierarchical model was fit to a dataset with >= 10 individuals
bsam 1.1.0.9000
- changed diag_ssm plot so that diagnostic panels for psi parameters are split into multiple pages of 5 rows/page when fitting a hierarchical model. Previous version packed all the psi diagnostic plots into a single page
bsam 1.1.0
- major bug fix - data indexing when fitting hierarchical models was causing improper fits to multi-individual datasets with > 2 individuals. Indexing now works as intended
- Added simulate function
- Prevented individual animal tracks from being re-ordered by split function so output track order now matches input track order
bsam 1.0.1.9003
- added list of initial values to the output list object. Making these available can aid diagnosing lack of convergence and inform choice of span argument value for generating location state initial values
bsam 1.0.1.9002
- added
get_summaryfunction to extract summarydata_framefromfit_ssmoutput objects. Thedata_framecan optionally be written to a .csv file
bsam 1.0.1.9001
- added
spandrworldxtraback to Imports list in DESCRIPTION. Ensures all required packages are installed, otherwisemap_ssmwill return an error when attempting to loadcountriesHighdata ifspand/orrworldxtraare not installed
bsam 1.0.1.9000
-
removed sp and rworldxtra from Imports
-
added BugReports URL to DESCRIPTION
bsam 1.0.0
-
Simplified movement models by removing the mean turn angle parameter. This tends to improve convergence for the behavioural switching models
-
Simplified the regularisation / interpolation in the observation models
-
Simplified data preparation code
-
Diagnostic plots (renamed from
diagSSMtodiag_ssm) now include the Gelman-Rubin-Brooks shrink factor plots for each parameter -
New mapping function (
map_ssm) uses coastline data fromrworldxtraandggplot2for core plotting functions -
New plot function (
plot_fit) to inspect fit to location data -
Renamed core function
fitSSMtofit_ssm -
Improved selection of random initial values for MCMC sampling
-
Initial values for location states are now based on a loess smooth through the observed locations. Users can control the degree of smoothing via the
spanargument tofit_ssm
bsam 0.43.1 (pre-CRAN release)
-
ported from source 2016-05-27 [email protected]il.com
-
converted to use roxygen2
-
Added a
NEWS.mdfile to track changes to the package.
f <- "http://web.science.mq.edu.au/~ijonsen/code/bsam_0.43.1.tar.gz"download.file(f, basename(f), mode = "wb")system(sprintf("tar zxvf %s", basename(f)))Rd2roxygen::Rd2roxygen("bsam")