prep_TS_data: Prepare the model-specific data to be used in the TS analysis...

In LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Prepare the model-specific data to be used in the TS analysis of LDA output

Description

Append the estimated topic proportions from a fitted LDA model to the document covariate table to create the data structure needed for TS.

Usage

prep_TS_data(document_covariate_table, LDA_models, mods, i = 1)

Arguments

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.
LDA_models	List of LDA models (class LDA_set, produced by LDA_set) or a singular LDA model (class LDA, produced by LDA).
mods	The data.table created by expand_TS that contains each of the models (defined by the LDA model to use and the and formula number of changepoints for the TS model). Indexed here by i.
i	integer index referencing the row in mods to use.

Value

Class data.frame object including [1] the time variable (indicated in control), [2] the predictor variables (required by formula) and [3], the multinomial response variable (indicated in formula), ready for input into TS.

Examples

  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 <- expand_TS(LDA_models, formulas = ~1, nchangepoints = 0)
  data1 <- prep_TS_data(document_covariate_table, LDA_models, mods)

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