cluster_obs: Cluster observations into behavioral states

In bayesmove: Non-Parametric Bayesian Analyses of Animal Movement

Description

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.

Usage

cluster_obs(dat, alpha, ngibbs, nmaxclust, nburn)

Arguments

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.

Details

The mixture model analyzes all animal IDs pooled together, thus providing a population-level estimate of behavioral states.

Value

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.

Examples

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)

bayesmove documentation built on Oct. 22, 2021, 9:08 a.m.