diff --git a/code/analysis/analyze_NA_robustness.Rmd b/code/analysis/analyze_NA_robustness.Rmd
index cc40a60..b35c277 100644
--- a/code/analysis/analyze_NA_robustness.Rmd
+++ b/code/analysis/analyze_NA_robustness.Rmd
@@ -21,7 +21,7 @@ loadNamespace(package = "patchwork")
 ```{r data, echo = FALSE}
 #  Loading data
 data_folder <- file.path(here(), "code", "results", "simulations", "NA_robustness")
-files_list <- list.files(data_folder, pattern = "^NA_robustness_19-04-2024")
+files_list <- list.files(data_folder, pattern = "^NA_robustness_22-04-2024_17")
 ```
 ```{r df_func, echo = FALSE}
 prepare_dataframes_auc_ari <- function(data) {
@@ -40,8 +40,7 @@ prepare_dataframes_auc_ari <- function(data) {
     #  Preparing auc_data
     auc_data <- averaged_data %>%
         select(c(prop_NAs, model) | contains("auc_")) %>%
-        rename_with(~ gsub("auc_", "", .x, fixed = TRUE)) %>%
-        filter(prop_NAs != 0)
+        rename_with(~ gsub("auc_", "", .x, fixed = TRUE))
 
     auc_data_long <-
         bind_cols(
@@ -109,7 +108,7 @@ plot_auc_ari_data <- function(df_list) {
         geom_line(aes(color = method)) +
         geom_point(aes(color = method)) +
         geom_ribbon(aes(ymin = auc_mean - auc_sd, ymax = auc_mean + auc_sd, fill = method), alpha = 0.2) +
-        ylim(c(min(auc_data_long[["auc_mean"]]), max(auc_data_long[["auc_mean"]]))) +
+        ylim(c(0.5, 1)) +
         scale_x_continuous(breaks = scales::pretty_breaks(n = 10L)) +
         ylab(TeX("\\bar{AUC}")) +
         xlab("NA proportion") +
@@ -163,6 +162,32 @@ figcaps <- sapply(files_list, function(filename) {
 })
 ```
 
+Pour les deux figures, la collection est composée de deux réseaux ($M = 2$) dont 
+les proportions sont les suivantes :
+$$\pi = \rho = (0.5, 0.3, 0.2) $$
+
+Le premier réseau qui subit une perte d'informations est de petite taille, 40 
+individus en ligne et en colonne. Le second réseau contient 120 individus en 
+ligne et en colonne.
+
+La structure ayant abouti aux résulats de la figure \@ref(fig:plot-1) est la suivante :
+$$\alpha_{modular} = \begin{bmatrix}
+0.9 & 0.05 & 0.05 \\
+0.05 & 0.2 & 0.05 \\
+0.05 & 0.05 & 0.8
+\end{bmatrix}$$
+Il s'agit d'une structure modulaire répliquant un comportement communautaire.
+
+
+La structure ayant abouti aux résulats de la figure \@ref(fig:plot-2) est la suivante :
+$$\alpha_{nested} = \begin{bmatrix}
+0.9 & 0.25 & 0.1 \\
+0.3 & 0.15 & 0.05 \\
+0.1 & 0.05 & 0.05
+\end{bmatrix}$$
+Il s'agit d'une structure emboîtée répliquant une structure communément trouvée
+dans les réseaux plantes-pollinisateurs.
+
 ```{r plot, echo = FALSE, fig.cap=paste("Graph of metrics for NA robustness with a ", figcaps)}
 for (filename in files_list) {
     data <- readRDS(file.path(
@@ -172,12 +197,4 @@ for (filename in files_list) {
     df_list <- prepare_dataframes_auc_ari(data = data)
     plot(plot_auc_ari_data(df_list = df_list))
 }
-# filename <- files_list[1]
-# data <- readRDS(file.path(
-#     data_folder,
-#     filename
-# ))
-# print(here())
-# df_list <- prepare_dataframes_auc_ari(data = data)
-# knitr::knit_print(plot_auc_ari_data(df_list = df_list))
 ```
\ No newline at end of file
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