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)
| dat | A data frame that **only** contains columns for the discretized movement variables. |
|---|---|
| alpha | numeric. A single value used to specify the hyperparameter for the prior distribution. |
| ngibbs | numeric. The total number of iterations of the MCMC chain. |
| nmaxclust | numeric. A single number indicating the maximum number of clusters to test. |
| nburn | 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) # }