prep_ids: Initialize and update the chain ids throughout the ptMCMC...

In LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Initialize and update the chain ids throughout the ptMCMC algorithm

Description

prep_ids creates and update_ids updates the active vector of identities (ids) for each of the chains in the ptMCMC algorithm. These ids are used to track trips of the particles among chains.

These functions were designed to work within TS and specifically est_changepoints, but have been generalized and would work within any general ptMCMC as long as control, ids, and swaps are formatted properly.

Usage

prep_ids(control = list())

update_ids(ids, swaps)

Arguments

control	A list of parameters to control the fitting of the Time Series model including the parallel tempering Markov Chain Monte Carlo (ptMCMC) controls. Values not input assume defaults set by TS_control.
ids	The existing vector of chain ids.
swaps	Chain configuration after among-temperature swaps.

Value

The vector of chain ids.

Examples

  prep_ids()

  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$gamma <- LDA_models@gamma
  weights <- document_weights(document_term_table)
  data <- data[order(data[,"newmoon"]), ]
  saves <- prep_saves(1, TS_control())
  inputs <- prep_ptMCMC_inputs(data, gamma ~ 1, 1, "newmoon", weights,
                               TS_control())
  cpts <- prep_cpts(data, gamma ~ 1, 1, "newmoon", weights, TS_control())
  ids <- prep_ids(TS_control())
  for(i in 1:TS_control()$nit){
    steps <- step_chains(i, cpts, inputs)
    swaps <- swap_chains(steps, inputs, ids)
    saves <- update_saves(i, saves, steps, swaps)
    cpts <- update_cpts(cpts, swaps)
    ids <- update_ids(ids, swaps)
  }

LDATS documentation built on Sept. 19, 2023, 5:08 p.m.