This function uses a Gibbs sampler within a mixture model to estimate the optimal number of behavioral states, the state-dependent distributions, and to assign behavioral states to each observation. This model does not assume an underlying mechanistic process.
cluster_obs(dat, alpha, ngibbs, nmaxclust, nburn)A data frame that **only** contains columns for the discretized movement variables.
numeric. A single value used to specify the hyperparameter for the prior distribution.
numeric. The total number of iterations of the MCMC chain.
numeric. A single number indicating the maximum number of clusters to test.
numeric. The length of the burn-in phase.
A list of model results is returned where elements include the
phi matrix for each data stream, theta matrix, log likelihood
estimates for each iteration of the MCMC chain loglikel, a list of
the MAP estimates of the latent states for each observation z.MAP, a
matrix of the whole posterior of state assignments per observation
z.posterior, and a vector gamma1 of estimates for the gamma
hyperparameter.
The mixture model analyzes all animal IDs pooled together, thus providing a population-level estimate of behavioral states.
# \donttest{
data(tracks.list)
#convert from list to data frame
tracks.list<- dplyr::bind_rows(tracks.list)
#only retain id and discretized step length (SL) and turning angle (TA) columns
tracks<- subset(tracks.list, select = c(SL, TA))
set.seed(1)
# Define model params
alpha=0.1
ngibbs=1000
nburn=ngibbs/2
nmaxclust=7
dat.res<- cluster_obs(dat = tracks, alpha = alpha, ngibbs = ngibbs,
nmaxclust = nmaxclust, nburn = nburn)
# }