cluster_obs {bayesmove} | R Documentation |
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. |
The mixture model analyzes all animal IDs pooled together, thus providing a population-level estimate of behavioral states.
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.
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)