get_MAP: Find the maximum a posteriori (MAP) estimate of the MCMC...

In bayesmove: Non-Parametric Bayesian Analyses of Animal Movement

Description

Identify the MCMC iteration that holds the MAP estimate. This will be used to inform get_breakpts as to which breakpoints should be retained on which to assign track segments to the observations of each animal ID.

Usage

get_MAP(dat, nburn)

Arguments

dat	A data frame where each row holds the log marginal likelihood values at each iteration of the MCMC chain.
nburn	numeric. The size of the burn-in phase after which the MAP estimate will be identified.

Value

A numeric vector of iterations at which the MAP estimate was found for each animal ID.

Examples

#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)  #run all MCMC chains in parallel
dat.res<- segment_behavior(data = tracks.list2, ngibbs = ngibbs, nbins = nbins,
                           alpha = alpha)


# Determine MAP iteration for selecting breakpoints and store breakpoints
MAP.est<- get_MAP(dat = dat.res$LML, nburn = ngibbs/2)

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