This function performs the reversible-jump MCMC algorithm using a Gibbs sampler, which estimates the breakpoints of the movement variables for each of the animal IDs. This is the first stage of the two-stage Bayesian model that estimates proportions of behavioral states by first segmenting individual tracks into relatively homogeneous segments of movement.

  breakpt = purrr::map(names(data), ~NULL)



A list where each element stores the data for a separate animal ID. List elements are data frames that only contain columns for the animal ID and for each of the discretized movement variables.


numeric. The total number of iterations of the MCMC chain.


numeric. A vector of the number of bins used to discretize each movement variable. These must be in the same order as the columns within data.


numeric. A single value used to specify the hyperparameter for the prior distribution. A standard value for alpha is typically 1, which corresponds with a vague prior on the Dirichlet distribution.


A list where each element stores a vector of breakpoints if pre-specifying where they may occur for each animal ID. By default this is set to NULL.


A list of model results is returned where elements include the breakpoints, number of breakpoints, and log marginal likelihood at each iteration of the MCMC chain for all animal IDs. The time it took the model to finish running for each animal ID are also stored and returned.


This model is run in parallel using the future package. To ensure that the model is run in parallel, the plan must be used with future::multisession as the argument for most operating systems. Otherwise, model will run sequentially by default if this is not set before running segment_behavior.


# \donttest{ #load data data(tracks.list) #subset only first track tracks.list<- tracks.list[1] #only retain id and discretized step length (SL) and turning angle (TA) columns tracks.list2<- purrr::map(tracks.list, subset, select = c(id, SL, TA)) set.seed(1) # Define model params alpha<- 1 ngibbs<- 1000 nbins<- c(5,8) future::plan(future::multisession, workers = 3) #run all MCMC chains in parallel dat.res<- segment_behavior(data = tracks.list2, ngibbs = ngibbs, nbins = nbins, alpha = alpha)
#> 2.335 sec elapsed
future::plan(future::sequential) #return to single core # }