<jupyter_start><jupyter_text>figure 1<jupyter_code>fig_N100_M0 = ggplot_results(dir='../../../../github-bayestime-analysis', 
                              dat_name='N100_M0', idx_sim=31, 
                              title_mean='N = 100 & NA = 0%', fpc_range=c(-0.5, 2.75))
fig_N100_M80 = ggplot_results(dir='../../../../github-bayestime-analysis', 
                              dat_name='N100_M80', idx_sim=31, 
                              title_mean='N = 100 & NA = 80%', fpc_range=c(-0.5, 2.75))
plot_grid(fig_N100_M0$figure_mean, fig_N100_M80$figure_mean, 
          fig_N100_M0$figure_fpc, fig_N100_M80$figure_fpc,
          labels = c('A', 'B', 'C', 'D'), 
          hjust = 0, vjust = 1) 
ggsave('../figures/sim_fig1.png', width=7, height=7, dpi=300)<jupyter_output>Warning message:
“Removed 255 row(s) containing missing values (geom_path).”
Warning message:
“Removed 272 row(s) containing missing values (geom_path).”<jupyter_text>figure 2<jupyter_code>fig_N50_M80 = ggplot_results(dir='../../../../github-bayestime-analysis', 
                              dat_name='N50_M80', idx_sim=31, 
                              title_mean='N = 50 & NA = 80%', fpc_range=c(-0.5, 2.75))
fig_N25_M80 = ggplot_results(dir='../../../../github-bayestime-analysis', 
                              dat_name='N25_M80', idx_sim=15, 
                              title_mean='N = 25 & NA = 80%', fpc_range=c(-0.55, 2.75))
plot_grid(fig_N50_M80$figure_mean, fig_N25_M80$figure_mean, 
          fig_N50_M80$figure_fpc, fig_N25_M80$figure_fpc,
          labels = c('A', 'B', 'C', 'D'), 
          hjust = 0, vjust = 1) 
ggsave('../figures/sim_fig2.png', width=7, height=7, dpi=300)<jupyter_output>Warning message:
“Removed 200 row(s) containing missing values (geom_path).”
Warning message:
“Removed 242 row(s) containing missing values (geom_path).”<jupyter_text>figure 3<jupyter_code>fig_N10_M50 = ggplot_results(dir='../../../../github-bayestime-analysis', 
                              dat_name='N10_M50', idx_sim=31, 
                              title_mean='N = 10 & NA = 50%', fpc_range=c(-0.7, 2.75))
fig_N10_M80 = ggplot_results(dir='../../../../github-bayestime-analysis', 
                              dat_name='N10_M80', idx_sim=71, 
                              title_mean='N = 10 & NA = 80%', fpc_range=c(-0.7, 2.75))
plot_grid(fig_N10_M50$figure_mean, fig_N10_M80$figure_mean, 
          fig_N10_M50$figure_fpc, fig_N10_M80$figure_fpc,
          labels = c('A', 'B', 'C', 'D'), 
          hjust = 0, vjust = 1) 
ggsave('../figures/sim_fig3.png', width=7, height=7, dpi=300)<jupyter_output>Warning message:
“Removed 65 row(s) containing missing values (geom_path).”<jupyter_text>figure 0: model selection<jupyter_code>library(BayesTime)
dir='../../../../github-bayestime-analysis'
dat_name = 'N100_M80'
idx_sim = 2
load(paste0(dir, '/summary_results/bayesTime_modelSelection_', dat_name, '_nsim', idx_sim, '.RData'))
         elpd_diff   se_diff pc knot
model4    0.000000  0.000000  2    1
model7   -1.855321  2.207409  3    1
model6  -41.625613 13.827913  2    3
model5  -43.147370 14.599553  2    2
model8  -57.929983 17.267650  3    2
model9  -88.180462 22.751190  3    3
model2 -178.965177 16.206256  1    2
model3 -180.350760 16.097851  1    3
model1 -181.686704 16.261466  1    1

# from sim_sfpca.o1411763-3002
fig_loo = plot_k_diagnostic(prepared_data, optimal_model)$figure
fig_loo 
fig_posterior = plot_posterior_diagnostic(prepared_data, optimal_model)$figure
fig_posterior
plot_grid(fig_loo, fig_posterior, 
          labels = c('A', 'B'), 
          hjust = 0, vjust = 1) 
ggsave('../figures/sim_fig0.png', width=7, height=4, dpi=300)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15207373271889402, 'fraction_numerical': 0.11005692599620494, 'mean_word_length': 2.6643495531281034, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 14, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '109267', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1684, 'n_tokens_neox': 1406, 'n_words': 240, 'path': 'simulations/notebooks/1_visu_sim_results.ipynb', 'repo_name': 'serenejiang/BayesTime-analyses', 'size': '3689'}