test_internal_functions: Test C++ functions
In matthewjdenny/CCAS: Communication Content and Network Structure

View source: R/test_internal_functions.R

Allows the user to interface with the individual C++ functions.

test_internal_functions(Test_Log_Space_Multinomial_Sampler = FALSE,
  Test_Edge_Probability = FALSE, Test_Sum_Over_T_Edge_Probs = FALSE,
  Test_Prior_Pobability_Of_I_P_Params = FALSE,
  Test_Sample_New_I_P_Parameters = FALSE, Test_LSM_Contribution = FALSE,
  Test_LDA_Contribution = FALSE,
  Test_Update_Single_Token_Topic_Assignment = FALSE,
  Test_Update_All_Token_Topic_Assignments = FALSE,
  Test_Update_Interaction_Pattern_Parameters = FALSE,
  Test_Update_Topic_Interaction_Pattern_Assignments = FALSE,
  Test_Adaptive_Metropolis = FALSE, Test_Inference = FALSE,
  Test_RDirichlet = FALSE,
  Test_Sample_Token_Topics_From_Generative_Process = FALSE,
  Getting_It_Right = FALSE, Schien_Testing = FALSE, seed = NULL,
  distribution = NULL, intercepts = NULL, coefficients = NULL,
  latent_positions = NULL, document_sender = NULL,
  document_recipient = NULL, current_covariates = NULL,
  interaction_pattern = NULL, using_coefficients = NULL,
  edge_probabilities = NULL, tokens_in_document = NULL,
  current_token_topic_assignment = NULL,
  current_document_topic_counts = NULL, leave_out_current_token = NULL,
  topic_interaction_patterns = NULL, leave_out_topic = NULL,
  intercept_prior_mean = NULL, intercept_prior_standard_deviation = NULL,
  intercept_proposal_standard_deviations = NULL,
  coefficient_prior_mean = NULL,
  coefficient_prior_standard_deviation = NULL,
  coefficient_proposal_standard_deviations = NULL,
  latent_position_prior_mean = NULL,
  latent_position_prior_standard_deviation = NULL,
  latent_position_proposal_standard_deviations = NULL, topic = NULL,
  document_edge_values = NULL, word_type_topic_counts = NULL,
  topic_token_counts = NULL, current_word_type = NULL, alpha_m = NULL,
  beta_n = NULL, beta = NULL, rand_num = NULL, author_indexes = NULL,
  document_edge_matrix = NULL, document_topic_counts = NULL,
  token_topic_assignments = NULL, token_word_types = NULL,
  covariates = NULL, random_numbers = NULL, accept_rates = NULL,
  target_accept_rate = NULL, tollerance = NULL, update_size = NULL,
  iterations = NULL, metropolis_iterations = NULL,
  total_number_of_tokens = NULL, iterations_before_t_i_p_updates = NULL,
  update_t_i_p_every_x_iterations = NULL,
  perform_adaptive_metropolis = NULL, resample_word_types = NULL,
  slice_sample_alpha_m = NULL, slice_sample_step_size = NULL,
  parallel = NULL, use_cached_token_topic_distribution = NULL,
  cached_token_topic_distribution = NULL, u = NULL, num_documents = NULL,
  words_per_doc = NULL, num_topics = NULL, num_terms = NULL,
  num_actors = NULL, num_ip = NULL, num_ld = NULL, GiR_samples = NULL,
  forward_sample = TRUE, use_collapsed_topic_sampling = FALSE,
  verbose = TRUE)

`Test_Log_Space_Multinomial_Sampler`	Defualts to FALSE. If TRUE, then optional arguments distribution and seed must be provided.
`Test_Edge_Probability`	Defaults to FALSE. If TRUE, then optional arguments intercepts, coefficients, latent_pos, document_sender, document_recipient, current_covariates, interaction_pattern, and using_coefficients must be provided.
`Test_Sum_Over_T_Edge_Probs`	Defaults to FALSE. If TRUE, then optional arguments edge_probs, tokens_in_document, current_token_topic_assignment, current_document_topic_counts, leave_out_current_token, topic_interaction_patterns, document_sender,document_recipient, leave_out_topic must be provided.
`Test_Prior_Pobability_Of_I_P_Params`	Defaults to FALSE. If TRUE, then optional arguments intercepts, coefficients, latent_pos, intercept_prior_mean, intercept_prior_standard_deviation, coefficient_prior_mean, coefficient_prior_standard_deviation, latent_position_prior_mean, latent_position_prior_standard_deviation, using_coefficients must be provided.
`Test_Sample_New_I_P_Parameters`	Defaults to FALSE. If TRUE, then optional arguments intercepts, coefficients, latent_pos, intercept_proposal_standard_deviations, coefficient_proposal_standard_deviations, latent_position_proposal_standard_deviations, (all of which must be of length = number of interaction patterns, and should all be equal) using_coefficients must be provided.
`Test_LSM_Contribution`	Defaults to FALSE. If TRUE, then optional arguments edge_probs, tokens_in_document, topic, current_token_topic_assignment, current_document_topic_counts, document_edge_values, topic_interaction_patterns, document_sender must be provided.
`Test_LDA_Contribution`	Defaults to FALSE. If TRUE, then optional arguments tokens_in_document, current_token_topic_assignment, current_document_topic_counts, word_type_topic_counts, topic_token_counts, topic, current_word_type, alpha_m, beta_n, beta must be provided.
`Test_Update_Single_Token_Topic_Assignment`	Defaults to FALSE. If TRUE, then optional arguments edge_probs, tokens_in_document, current_token_topic_assignment, current_document_topic_counts, word_type_topic_counts, topic_token_counts, current_word_type, alpha_m, beta_n, document_edge_values, topic_interaction_patterns, document_sender, rand_num must be provided.
`Test_Update_All_Token_Topic_Assignments`	Defaults to FALSE. If TRUE, then optional arguments author_indexes, document_edge_matrix, topic_interaction_patterns, document_topic_counts, word_type_topic_counts, topic_token_counts, token_topic_assignments, token_word_types, intercepts, coefficients, latent_pos, covars, alpha_m, beta_n, random_numbers, using_coefficients must be provided. Make sure that random_numbers has length equal to the total number of tokens in the corpus.
`Test_Update_Interaction_Pattern_Parameters`	Defaults to FALSE. If TRUE, then optional arguments author_indexes, document_edge_matrix, document_topic_counts, topic_interaction_patterns, intercepts, coefficients, latent_pos, covars, using_coefficients, intercept_prior_mean, intercept_prior_standard_deviation, intercept_proposal_standard_deviations, coefficient_prior_mean, coefficient_prior_standard_deviation, coefficient_proposal_standard_deviations, latent_position_prior_mean, latent_position_prior_standard_deviation, latent_position_proposal_standard_deviations, rand_num, edge_probs must be provided.
`Test_Update_Topic_Interaction_Pattern_Assignments`	Defaults to FALSE. If TRUE, then optional arguments author_indexes, document_edge_matrix, document_topic_counts, topic_interaction_patterns, intercepts, coefficients, latent_pos, covars, using_coefficients, random_numbers, edge_probs must be provided.
`Test_Adaptive_Metropolis`	Defaults to FALSE. If TRUE, then optional arguments intercept_proposal_standard_deviations, coefficient_proposal_standard_deviations, latent_position_proposal_standard_deviations, accept_rates, target_accept_rate, tollerance, update_size must be provided.
`Test_Inference`	If TRUE, then optional arguments author_indexes, document_edge_matrix, document_topic_counts, topic_interaction_patterns, word_type_topic_counts, topic_token_counts, token_topic_assignments, token_word_types, intercepts, coefficients, latent_pos, covars, alpha_m, beta_n, using_coefficients, intercept_prior_mean, intercept_prior_standard_deviation, intercept_proposal_standard_deviations, coefficient_prior_mean, coefficient_prior_standard_deviation, coefficient_proposal_standard_deviations, latent_position_prior_mean, latent_position_prior_standard_deviation, latent_position_proposal_standard_deviations, target_accept_rate, tollerance, update_size, seed, iterations, metropolis_iterations, total_number_of_tokens, iterations_before_t_i_p_updates, update_t_i_p_every_x_iterations, perform_adaptive_metropolis must be provided.
`Test_RDirichlet`	If TRUE, then alpha_m must be provided.
`Test_Sample_Token_Topics_From_Generative_Process`	If TRUE, then optional arguments author_indexes, token_topic_assignments, token_word_types, alpha_m, beta_n, random_numbers, resample_word_types and use_collapsed_topic_sampling must be provided.
`Getting_It_Right`	If TRUE, then Geweke's getting it right test is performed
`Schien_Testing`	If TRUE, the Schien testing is performed. Defaults to FALSE.
`seed`	An integerg.
`distribution`	A log-space vector
`intercepts`	A vector.
`coefficients`	A matrix.
`latent_positions`	An array.
`document_sender`	An integer.
`document_recipient`	An integer.
`current_covariates`	A vector
`interaction_pattern`	An integer
`using_coefficients`	A boolean.
`edge_probabilities`	An array
`tokens_in_document`	An integer
`current_token_topic_assignment`	An integer
`current_document_topic_counts`	A vector.
`leave_out_current_token`	A logical
`topic_interaction_patterns`	a vector
`leave_out_topic`	An integer.
`intercept_prior_mean`	A double.
`intercept_prior_standard_deviation`	A double.
`intercept_proposal_standard_deviations`	A vector.
`coefficient_prior_mean`	A double.
`coefficient_prior_standard_deviation`	A double.
`coefficient_proposal_standard_deviations`	A vector.
`latent_position_prior_mean`	A double.
`latent_position_prior_standard_deviation`	A double.
`latent_position_proposal_standard_deviations`	A vector.
`topic`	An integer.
`document_edge_values`	A vector.
`word_type_topic_counts`	A matrix
`topic_token_counts`	A matrix.
`current_word_type`	An integer.
`alpha_m`	A vector
`beta_n`	A vector
`beta`	Is equal to sum(beta_n)
`rand_num`	An integer
`author_indexes`	A vector
`document_edge_matrix`	A matrix
`document_topic_counts`	A matrix
`token_topic_assignments`	A List
`token_word_types`	A List
`covariates`	An array
`random_numbers`	A vector
`accept_rates`	A vector.
`target_accept_rate`	A double.
`tollerance`	A dobule.
`update_size`	A double.
`iterations`	An integer.
`metropolis_iterations`	An integer.
`total_number_of_tokens`	An integer.
`iterations_before_t_i_p_updates`	An integer.
`update_t_i_p_every_x_iterations`	An integer.
`perform_adaptive_metropolis`	A boolean.
`resample_word_types`	A boolean.
`slice_sample_alpha_m`	An integer (normally a boolean but you can set the value directly (normally 5, set to a negative number to turn off)),
`slice_sample_step_size`	An integer.
`parallel`	A boolean.
`use_cached_token_topic_distribution`	A boolean.
`cached_token_topic_distribution`	A vector with legth equal to the number of topics.
`u`	A numeric of length 1 between 0 and 1.
`num_documents`	An integer.
`words_per_doc`	An integer.
`num_topics`	An integer.
`num_terms`	An integer.
`num_actors`	An integer.
`num_ip`	An integer.
`num_ld`	An integer.
`GiR_samples`	An integer.
`forward_sample`	A logical. If TRUE, the forward GiR samples are generated. If FALSE, then backwards GiR samples are generated.
`use_collapsed_topic_sampling`	Defaults to FALSE. IF TRUE, then collapsed topic sampling is done. This only matters if you are using the Test_Sample_Token_Topics_From_Generative_Process option.
`verbose`	Defaults to TRUE, if FALSE, then no output is printed to the screen by the inference code.