count_trips: Count trips of the ptMCMC particles

In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description

Count the full trips (from one extreme temperature chain to the other and back again; Katzgraber et al. 2006) for each of the ptMCMC particles, as identified by their id on initialization.

This function was designed to work within TS and process the output of est_changepoints as a component of diagnose_ptMCMC, but has been generalized and would work with any output from a ptMCMC as long as ids is formatted properly.

Usage

count_trips(ids)

Arguments

ids	matrix of identifiers of the particles in each chain for each iteration of the ptMCMC algorithm (rows: chains, columns: iterations).

Value

list of [1] vector of within particle trip counts ($trip_counts), and [2] vector of within-particle average trip rates ($trip_rates).

References

Katzgraber, H. G., S. Trebst, D. A. Huse. And M. Troyer. 2006. Feedback-optimized parallel tempering Monte Carlo. Journal of Statistical Mechanics: Theory and Experiment 3:P03018 link.

Examples

  data(rodents)
  document_term_table <- rodents$document_term_table
  document_covariate_table <- rodents$document_covariate_table
  LDA_models <- LDA_set(document_term_table, topics = 2)[[1]]
  data <- document_covariate_table
  data$gamma <- LDA_models@gamma
  weights <- document_weights(document_term_table)
  data <- data[order(data[,"newmoon"]), ]
  rho_dist <- est_changepoints(data, gamma ~ 1, 1, "newmoon", weights,
                               TS_control())
  count_trips(rho_dist$ids)

weecology/LDATS documentation built on March 28, 2020, 11:20 a.m.