Extract bin estimates from Latent Dirichlet Allocation or mixture model

Source: R/LDA_helper_functions.R

Pulls model results for the estimates of bin proportions per movement variable from the posterior distribution. This can be used for visualization of movement variable distribution for each behavior estimated.

get_behav_hist(dat, nburn, ngibbs, nmaxclust, var.names)

Arguments

dat

The list object returned by the LDA model (cluster_segments) or mixture model (cluster_obs). Used for extracting the element phi.

nburn

numeric. The length of the burn-in phase.

ngibbs

numeric. The total number of iterations of the MCMC chain.

nmaxclust

numeric. The maximum number of clusters on which to attribute behaviors.

var.names

character. A vector of names used for each of the movement variables. Must be in the same order as were listed within the data frame returned by summarize_tsegs (if running LDA model).

Value

A data frame that contains columns for bin number, behavioral state, proportion represented by a given bin, and movement variable name. This is displayed in a long format, which is easier to visualize using

ggplot2.

Examples


# \donttest{
#load data
data(tracks.seg)

#select only id, tseg, SL, and TA columns
tracks.seg2<- tracks.seg[,c("id","tseg","SL","TA")]

#summarize data by track segment
obs<- summarize_tsegs(dat = tracks.seg2, nbins = c(5,8))

#cluster data with LDA
res<- cluster_segments(dat = obs, gamma1 = 0.1, alpha = 0.1, ngibbs = 1000,
                       nburn = 500, nmaxclust = 7, ndata.types = 2)

#Extract proportions of behaviors per track segment
theta.estim<- extract_prop(res = res, ngibbs = 1000, nburn = 500, nmaxclust = 7)

#run function for clustered segments
behav.res<- get_behav_hist(dat = res, nburn = 500, ngibbs = 1000, nmaxclust = 7,
                           var.names = c("Step Length","Turning Angle"))
# }