Description Usage Arguments Details Value Author(s) References Examples

Fits state-space models to animal tracking data. User can choose between a first difference correlated random walk (DCRW) model, a switching model (DCRWS) for estimating location and behavioural states, and their hierarchical versions (hDCRW, hDCRWS). The models are structured for Argos satellite data but options exist for fitting to other tracking data types.

1 2 |

`data` |
A data frame containing the following columns, "id","date", "lc", "lon", "lat". "id" is a unique identifier for the tracking dataset. "date" is the GMT date-time of each observation with the following format "2001-11-13 07:59:59". "lc" is the Argos location quality class of each observation, values in ascending order of quality are "Z", "B", "A", "0", "1", "2", "3". "lon" is the observed longitude in decimal degrees. "lat" is the observed latitude in decimal degrees. The Z-class locations are assumed to have the same error distributions as B-class locations. Optionally, the input data.frame can specify the error standard deviations
for longitude and latitude (in units of degrees) in the last 2 columns,
named "lonerr" and "laterr", respectively. These errors are assumed to be
normally distributed. When specifying errors in the input data, all "lc"
values must be equal to "G". This approach allows the models to be fit to
data types other than Argos satellite data, e.g. geolocation data. See
WARNING: there is no guarantee that invoking these options will yield sensible results!
For GPS data, similar models can be fit via the |

`model` |
name of state-space model to be fit to data. This can be one of "DCRW", "DCRWS", "hDCRW", or "hDCRWS" |

`tstep` |
time step as fraction of a day, default is 1 (24 hours). |

`adapt` |
number of samples during the adaptation and update (burn-in) phase, adaptation and updates are fixed at adapt/2 |

`samples` |
number of posterior samples to generate after burn-in |

`thin` |
amount of thinning of to be applied to the posterior samples to minimize within-chain sample autocorrelation |

`span` |
parameter that controls the degree of smoothing by |

The models are fit using JAGS 4.2.0 (Just Another Gibbs Sampler, created and
maintained by Martyn Plummer; http://martynplummer.wordpress.com/;
http://mcmc-jags.sourceforge.net). `fit_ssm`

is a wrapper that first
calls `dat4jags`

, which prepares the input data, then calls `ssm`

or `hssm`

, which fit the specified state-space model to the data,
returning a list of results.

For DCRW and DCRWS models, a list is returned with each outer list elements corresponding to each unique individual id in the input data Within these outer elements are a "summary" data.frame of posterior mean and median state estimates (locations or locations and behavioural states), the name of the "model" fit, the "timestep" used, the input location "data", the number of location state estimates ("N"), and the full set of "mcmc" samples. For the hDCRW and hDCRWS models, a list is returned where results, etc are combined amongst the individuals

Ian Jonsen

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

Block et al. (2011) Tracking apex marine predator movements in a dynamic ocean. Nature 475:86-90

Jonsen et al. (2013) State-space models for biologgers: a methodological road map. Deep Sea Research II DOI: 10.1016/j.dsr2.2012.07.008

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

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## Not run:
# Fit DCRW model for state filtering and regularization
data(ellie)
fit <- fit_ssm(ellie, model = "DCRW", tstep = 2, adapt = 5000, samples = 5000,
thin = 5, span = 0.2)
diag_ssm(fit)
map_ssm(fit)
plot_fit(fit)
result <- get_summary(fit)
# Fit DCRWS model for state filtering, regularization and behavioural state estimation
fit.s <- fit_ssm(ellie, model = "DCRWS", tstep = 2, adapt = 5000, samples = 5000,
thin = 5, span = 0.2)
diag_ssm(fit.s)
map_ssm(fit.s)
plot_fit(fit.s)
result.s <- get_summary(fit.s)
# fit hDCRWS model to > 1 tracks simultaneously
# this may provide better parameter and behavioural state estimation
# by borrowing strength across multiple track datasets
hfit.s <- fit_ssm(ellie, model = "hDCRWS", tstep = 2, adapt = 5000, samples = 5000,
thin = 5, span = 0.2)
diag_ssm(hfit.s)
map_ssm(hfit.s)
plot_fit(hfit.s)
result.hs <- get_summary(hfit.s)
## End(Not run)
``` |

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