est_changepoints: Use ptMCMC to estimate the distribution of change point...
In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description Usage Arguments Value Examples

This function executes ptMCMC-based estimation of the change point location distributions for multinomial Time Series analyses.

1 2	est_changepoints(data, formula, nchangepoints, timename, weights, control = list())

`data`	`data.frame` including [1] the time variable (indicated in `timename`), [2] the predictor variables (required by `formula`) and [3], the multinomial response variable (indicated in `formula`) as verified by `check_timename` and `check_formula`. Note that the response variables should be formatted as a `data.frame` object named as indicated by the `response` entry in the `control` list, such as `gamma` for a standard TS analysis on LDA output.
`formula`	`formula` defining the regression between relationship the change points. Any predictor variable included must also be a column in `data` and any (multinomial) response variable must be a set of columns in `data`, as verified by `check_formula`.
`nchangepoints`	`integer` corresponding to the number of change points to include in the model. 0 is a valid input (corresponding to no change points, so a singular time series model), and the current implementation can reasonably include up to 6 change points. The number of change points is used to dictate the segmentation of the time series into chunks fit with separate models dictated by `formula`.
`timename`	`character` element indicating the time variable used in the time series.
`weights`	Optional class `numeric` vector of weights for each document. Defaults to `NULL`, translating to an equal weight for each document. When using `multinom_TS` in a standard LDATS analysis, it is advisable to weight the documents by their total size, as the result of `LDA` is a matrix of proportions, which does not account for size differences among documents. For most models, a scaling of the weights (so that the average is 1) is most appropriate, and this is accomplished using `document_weights`.
`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`.

List of saved data objects from the ptMCMC estimation of change point locations (unless nchangepoints is 0, then NULL is returned).

  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)
  formula <- gamma ~ 1
  nchangepoints <- 1
  control <- TS_control()
  data <- data[order(data[,"newmoon"]), ]
  rho_dist <- est_changepoints(data, formula, nchangepoints, "newmoon", 
                               weights, control)