diff --git a/lbm_full_mat.R b/lbm_full_mat.R
new file mode 100644
index 0000000..1496433
--- /dev/null
+++ b/lbm_full_mat.R
@@ -0,0 +1,80 @@
+library(sbm)
+library(ggplot2)
+library(readr)
+
+source("load-full.R")
+
+sdCol <- function(matrix) {
+    sapply(seq_len(ncol(matrix)), function(col) sd(matrix[, col]))
+}
+
+net_table <- otu_table(cpd_phyloseq)
+rm(cpd_phyloseq)
+write.csv(net_table, "data/otu_matrix.csv")
+zip("data/otu_matrix.csv.zip", "data/otu_matrix.csv")
+unlink("data/otu_matrix.csv")
+# Ne tourne pas, probablement gros calculs matriciel
+# res <- estimateBipartiteSBM(netMat = net_table, dimLabels = c("OTU", "Sample"), model = "poisson")
+
+nb_inter <- sum(net_table > 0)
+all_possible_inter <- nrow(net_table) * ncol(net_table)
+(density <- nb_inter / all_possible_inter)
+
+t_net_table <- t(net_table)
+std_otu <- sdCol(t_net_table)
+mean_otu <- rowMeans(net_table)
+
+stats_df <- data.frame(otu = rownames(net_table), mean = mean_otu, std = std_otu, var = std_otu^2)
+ggplot(stats_df, aes(y = otu, x = mean)) +
+    geom_point() +
+    geom_errorbarh(aes(xmin = mean - std, xmax = mean + std), orientation = "y") +
+    theme(
+        axis.title.y = element_blank(),
+        axis.text.y = element_blank(),
+        axis.ticks.y = element_blank()
+    )
+
+ggplot(stats_df, aes(x = mean, y = var)) +
+    geom_line() +
+    geom_point() +
+    geom_abline(slope = 1, color = "red") +
+    scale_y_log10() +
+    scale_x_log10() +
+    ggtitle("OTU for log(mean) = log(var)")
+
+ggplot(stats_df, aes(x = mean, y = var / mean)) +
+    geom_line() +
+    geom_point() +
+    geom_hline(yintercept = 1, color = "red") +
+    ggtitle("OTU for f(mean) = var/mean = 1")
+
+ggplot(stats_df, aes(x = 1 / mean, y = var / mean^2)) +
+    geom_line() +
+    geom_point() +
+    geom_abline(slope = 1, color = "red") +
+    scale_y_log10() +
+    scale_x_log10() +
+    ggtitle("OTU for log(1/mean) = log(var/mean^2)")
+
+lambdas <- seq(1, 100)
+realization_pois <- sapply(lambdas, rpois, n = 1000)
+r_pois_df <- data.frame(mean = colMeans(realization_pois), var = sdCol(realization_pois)^2)
+
+ggplot(r_pois_df, aes(x = mean, y = var)) +
+    geom_line() +
+    geom_point() +
+    geom_abline(slope = 1, color = "red") +
+    ggtitle("Poisson for mean = var")
+
+ggplot(r_pois_df, aes(x = mean, y = var / mean)) +
+    geom_line() +
+    geom_point() +
+    geom_hline(yintercept = 1, color = "red") +
+    ggtitle("Poisson for f(mean) = var/mean = 1")
+
+
+ggplot(r_pois_df, aes(x = 1 / mean, y = var / mean^2)) +
+    geom_line() +
+    geom_point() +
+    geom_abline(slope = 1, color = "red") +
+    ggtitle("Poisson for 1/lambda = var/mean^2")