R: Latent Dirichlet Allocation Coupled with Time Series Analyses

# Latent Dirichlet Allocation Coupled with Time Series Analyses

## Documentation for package ‘LDATS’ version 0.3.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A C D E I J L M N P R S T U V

## -- A --

AICc | Calculate AICc
autocorr_plot | Produce the autocorrelation panel for the TS diagnostic plot of a parameter

## -- C --

check_changepoints | Check that a set of change point locations is proper
check_control | Check that a control list is proper
check_document_covariate_table | Check that the document covariate table is proper
check_document_term_table | Check that document term table is proper
check_formula | Check that a formula is proper
check_formulas | Check that formulas vector is proper and append the response variable
check_LDA_models | Check that LDA model input is proper
check_LDA_set_inputs | Run a set of Latent Dirichlet Allocation models
check_LDA_TS_inputs | Run a full set of Latent Dirichlet Allocations and Time Series models
check_multinom_TS_inputs | Fit a multinomial change point Time Series model
check_nchangepoints | Check that nchangepoints vector is proper
check_seeds | Check that nseeds value or seeds vector is proper
check_timename | Check that the time vector is proper
check_topics | Check that topics vector is proper
check_TS_inputs | Conduct a single multinomial Bayesian Time Series analysis
check_TS_on_LDA_inputs | Conduct a set of Time Series analyses on a set of LDA models
check_weights | Check that weights vector is proper
conform_LDA_TS_data | Run a full set of Latent Dirichlet Allocations and Time Series models
count_trips | Count trips of the ptMCMC particles

## -- D --

diagnose_ptMCMC | Calculate ptMCMC summary diagnostics
document_weights | Calculate document weights for a corpus

## -- E --

ecdf_plot | Produce the posterior distribution ECDF panel for the TS diagnostic plot of a parameter
est_changepoints | Use ptMCMC to estimate the distribution of change point locations
est_regressors | Estimate the distribution of regressors, unconditional on the change point locations
eta_diagnostics_plots | Plot the diagnostics of the parameters fit in a TS model
eval_step | Conduct a within-chain step of the ptMCMC algorithm
expand_TS | Expand the TS models across the factorial combination of LDA models, formulas, and number of change points

## -- I --

iftrue | Replace if TRUE

## -- J --

jornada | Jornada rodent data

## -- L --

LDATS | Package to conduct two-stage analyses combining Latent Dirichlet Allocation with Bayesian Time Series models
LDA_msg | Create the model-running-message for an LDA
LDA_plot_bottom_panel | Plot the results of an LDATS LDA model
LDA_plot_top_panel | Plot the results of an LDATS LDA model
LDA_set | Run a set of Latent Dirichlet Allocation models
LDA_set_control | Create control list for set of LDA models
LDA_TS | Run a full set of Latent Dirichlet Allocations and Time Series models
LDA_TS_control | Create the controls list for the LDATS model
logLik.LDA_VEM | Calculate the log likelihood of a VEM LDA model fit
logLik.multinom_TS_fit | Log likelihood of a multinomial TS model
logLik.TS_fit | Determine the log likelihood of a Time Series model
logsumexp | Calculate the log-sum-exponential (LSE) of a vector

## -- M --

measure_eta_vcov | Summarize the regressor (eta) distributions
measure_rho_vcov | Summarize the rho distributions
memoise_fun | Logical control on whether or not to memoise
messageq | Optionally generate a message based on a logical input
mirror_vcov | Create a properly symmetric variance covariance matrix
modalvalue | Determine the mode of a distribution
multinom_TS | Fit a multinomial change point Time Series model
multinom_TS_chunk | Fit a multinomial Time Series model chunk

## -- N --

normalize | Normalize a vector

## -- P --

package_chunk_fits | Package the output of the chunk-level multinomial models into a multinom_TS_fit list
package_LDA_set | Package the output from LDA_set
package_LDA_TS | Package the output of LDA_TS
package_TS | Summarize the Time Series model
package_TS_on_LDA | Package the output of TS_on_LDA
plot.LDA_set | Plot a set of LDATS LDA models
plot.LDA_TS | Plot the key results from a full LDATS analysis
plot.LDA_VEM | Plot the results of an LDATS LDA model
plot.TS_fit | Plot an LDATS TS model
posterior_plot | Produce the posterior distribution histogram panel for the TS diagnostic plot of a parameter
pred_gamma_TS_plot | Create the summary plot for a TS fit to an LDA model
prep_chunks | Prepare the time chunk table for a multinomial change point Time Series model
prep_cpts | Initialize and update the change point matrix used in the ptMCMC algorithm
prep_ids | Initialize and update the chain ids throughout the ptMCMC algorithm
prep_LDA_control | Set the control inputs to include the seed
prep_pbar | Initialize and tick through the progress bar
prep_proposal_dist | Pre-calculate the change point proposal distribution for the ptMCMC algorithm
prep_ptMCMC_inputs | Prepare the inputs for the ptMCMC algorithm estimation of change points
prep_saves | Prepare and update the data structures to save the ptMCMC output
prep_temp_sequence | Prepare the ptMCMC temperature sequence
prep_TS_data | Prepare the model-specific data to be used in the TS analysis of LDA output
print.LDA_TS | Print the selected LDA and TS models of LDA_TS object
print.TS_fit | Print a Time Series model fit
print.TS_on_LDA | Print a set of Time Series models fit to LDAs
print_model_run_message | Print the message to the console about which combination of the Time Series and LDA models is being run
process_saves | Prepare and update the data structures to save the ptMCMC output
proposed_step_mods | Fit the chunk-level models to a time series, given a set of proposed change points within the ptMCMC algorithm
propose_step | Conduct a within-chain step of the ptMCMC algorithm

## -- R --

rho_diagnostics_plots | Plot the diagnostics of the parameters fit in a TS model
rho_hist | Create the summary plot for a TS fit to an LDA model
rho_lines | Add change point location lines to the time series plot
rodents | Portal rodent data

## -- S --

select_LDA | Select the best LDA model(s) for use in time series
select_TS | Select the best Time Series model
set_gamma_colors | Prepare the colors to be used in the gamma time series
set_LDA_plot_colors | Prepare the colors to be used in the LDA plots
set_LDA_TS_plot_cols | Create the list of colors for the LDATS summary plot
set_rho_hist_colors | Prepare the colors to be used in the change point histogram
set_TS_summary_plot_cols | Create the list of colors for the TS summary plot
sim_LDA_data | Simulate LDA data from an LDA structure given parameters
sim_LDA_TS_data | Simulate LDA_TS data from LDA and TS model structures and parameters
sim_TS_data | Simulate TS data from a TS model structure given parameters
softmax | Calculate the softmax of a vector or matrix of values
step_chains | Conduct a within-chain step of the ptMCMC algorithm
summarize_etas | Summarize the regressor (eta) distributions
summarize_rhos | Summarize the rho distributions
swap_chains | Conduct a set of among-chain swaps for the ptMCMC algorithm

## -- T --

take_step | Conduct a within-chain step of the ptMCMC algorithm
trace_plot | Produce the trace plot panel for the TS diagnostic plot of a parameter
TS | Conduct a single multinomial Bayesian Time Series analysis
TS_control | Create the controls list for the Time Series model
TS_diagnostics_plot | Plot the diagnostics of the parameters fit in a TS model
TS_on_LDA | Conduct a set of Time Series analyses on a set of LDA models
TS_summary_plot | Create the summary plot for a TS fit to an LDA model

## -- U --

update_cpts | Initialize and update the change point matrix used in the ptMCMC algorithm
update_ids | Initialize and update the chain ids throughout the ptMCMC algorithm
update_pbar | Initialize and tick through the progress bar
update_saves | Prepare and update the data structures to save the ptMCMC output

## -- V --

verify_changepoint_locations | Verify the change points of a multinomial time series model