Latent Dirichlet Allocation Coupled with Time Series Analyses

Documentation for package ‘LDATS’ version 0.3.0

Help Pages

-- 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