TS_on_LDA: Conduct a set of Time Series analyses on a set of LDA models
In weecology/LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Description Usage Arguments Value Examples

This is a wrapper function that expands the main Time Series analyses function (TS) across the LDA models (estimated using LDA or LDA_set and the Time Series models, with respect to both continuous time formulas and the number of discrete changepoints. This function allows direct passage of the control parameters for the parallel tempering MCMC through to the main Time Series function, TS, via the ptMCMC_controls argument.

check_TS_on_LDA_inputs checks that the inputs to TS_on_LDA are of proper classes for a full analysis.

TS_on_LDA(LDA_models, document_covariate_table, formulas = ~1,
  nchangepoints = 0, timename = "time", weights = NULL,
  control = list())

check_TS_on_LDA_inputs(LDA_models, document_covariate_table,
  formulas = ~1, nchangepoints = 0, timename = "time",
  weights = NULL, control = list())

`LDA_models`	List of LDA models (class `LDA_set`, produced by `LDA_set`) or a singular LDA model (class `LDA`, produced by `LDA`).
`document_covariate_table`	Document covariate table (rows: documents, columns: time index and covariate options). Every model needs a covariate to describe the time value for each document (in whatever units and whose name in the table is input in `timename`) that dictates the application of the change points. In addition, all covariates named within specific models in `formula` must be included. Must be a conformable to a data table, as verified by `check_document_covariate_table`.
`formulas`	Vector of `formula`(s) for the continuous (non-change point) component of the time series models. Any predictor variable included in a formula must also be a column in the `document_covariate_table`. Each element (formula) in the vector is evaluated for each number of change points and each LDA model.
`nchangepoints`	Vector of `integer`s corresponding to the number of change points to include in the time series models. 0 is a valid input corresponding to no change points (i.e., a singular time series model), and the current implementation can reasonably include up to 6 change points. Each element in the vector is the number of change points used to segment the data for each formula (entry in `formulas`) component of the TS model, for each selected LDA model.
`timename`	`character` element indicating the time variable used in the time series. Defaults to `"time"`. The variable must be integer-conformable or a `Date`. If the variable named is a `Date`, the input is converted to an integer, resulting in the timestep being 1 day, which is often not desired behavior.
`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`.

TS_on_LDA: TS_on_LDA-class list of results from TS applied for each model on each LDA model input.

check_TS_inputs: An error message is thrown if any input is not proper, else NULL.

  data(rodents)
  document_term_table <- rodents$document_term_table
  document_covariate_table <- rodents$document_covariate_table
  LDAs <- LDA_set(document_term_table, topics = 2:3, nseeds = 2)
  LDA_models <- select_LDA(LDAs)
  weights <- document_weights(document_term_table)
  formulas <- c(~ 1, ~ newmoon)
  mods <- TS_on_LDA(LDA_models, document_covariate_table, formulas,
                    nchangepoints = 0:1, timename = "newmoon", weights)