## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE ) ## ----setup, echo = FALSE------------------------------------------------------ library(handwriter) ## ----eval=FALSE--------------------------------------------------------------- # template <- make_clustering_template( # main_dir = "path/to/main_dir", # template_docs = "path/to/main_dir/data/template_docs", # writer_indices = c(7,10), # centers_seed = 100, # K = 40, # num_dist_cores = 4, # max_iters = 25) ## ----------------------------------------------------------------------------- template <- example_cluster_template ## ----------------------------------------------------------------------------- template_data <- format_template_data(template = template) plot_cluster_fill_counts(template_data, facet = TRUE) ## ----eval = FALSE------------------------------------------------------------- # model <- fit_model(main_dir = "path/to/main_dir", # model_docs = "path/to/main_dir/data/model_docs", # num_iters = 4000, # num_chains = 1, # num_cores = 2, # writer_indices = c(7, 10), # doc_indices = c(11, 14)) ## ----------------------------------------------------------------------------- model <- example_model ## ----------------------------------------------------------------------------- plot_cluster_fill_counts(formatted_data=model, facet = TRUE) ## ----------------------------------------------------------------------------- names(as.data.frame(coda::as.mcmc(model$fitted_model[[1]]))) ## ----------------------------------------------------------------------------- about_variable(variable = "mu[1,1]", model = model) ## ----------------------------------------------------------------------------- plot_trace(variable = "mu[1,1]", model = model) ## ----------------------------------------------------------------------------- model <- drop_burnin(model, burn_in = 25) ## ----eval=FALSE--------------------------------------------------------------- # saveRDS(model, file='data/model.rds') ## ----eval = FALSE------------------------------------------------------------- # analysis <- analyze_questioned_documents( # main_dir = "path/to/main_dir", # questioned_docs = "path/to/main_dir/questioned_docs", # model = model, # writer_indices = c(8,11), # doc_indices = c(13,16), # num_cores = 2) ## ----------------------------------------------------------------------------- analysis <- example_analysis ## ----------------------------------------------------------------------------- plot_cluster_fill_counts(analysis, facet = TRUE) ## ----------------------------------------------------------------------------- analysis$posterior_probabilities ## ----------------------------------------------------------------------------- calculate_accuracy(analysis)