package_TS: Summarize the Time Series model

In LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Summarize the Time Series model

Description

Calculate relevant summaries for the run of a Time Series model within TS and package the output as a TS_fit-class object.

Usage

package_TS(data, formula, timename, weights, control, rho_dist, eta_dist)

Arguments

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.
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.
rho_dist	List of saved data objects from the ptMCMC estimation of change point locations returned by est_changepoints (unless nchangepoints is 0, then NULL).
eta_dist	Matrix of draws (rows) from the marginal posteriors of the coefficients across the segments (columns), as estimated by est_regressors.

Value

TS_fit-class list containing the following elements, many of which are hidden for printing, but are accessible:

data

data input to the function.

formula

formula input to the function.

nchangepoints

nchangepoints input to the function.

weights

weights input to the function.

timename

timename input to the function.

control

control input to the function.

lls

Iteration-by-iteration logLik values for the full time series fit by multinom_TS.

rhos

Iteration-by-iteration change point estimates from est_changepoints.

etas

Iteration-by-iteration marginal regressor estimates from est_regressors, which have been unconditioned with respect to the change point locations.

ptMCMC_diagnostics

ptMCMC diagnostics, see diagnose_ptMCMC

rho_summary

Summary table describing rhos (the change point locations), see summarize_rhos.

rho_vcov

Variance-covariance matrix for the estimates of rhos (the change point locations), see measure_rho_vcov.

eta_summary

Summary table describing ets (the regressors), see summarize_etas.

eta_vcov

Variance-covariance matrix for the estimates of etas (the regressors), see measure_eta_vcov.

logLik

Across-iteration average of log-likelihoods (lls).

nparams

Total number of parameters in the full model, including the change point locations and regressors.

AIC

Penalized negative log-likelihood, based on logLik and nparams.

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

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