Description Usage Arguments Value
View source: R/test_internal_functions.R
Allows the user to interface with the individual C++ functions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 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. |
Whatever is returned by the internal function being tested
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