Changes

simulations/functions-anova.R

@@ -60,7 +60,12 @@ simulate_ANOVAs <- function(
 
     ## ANOVAs
     fit_ANOVA <- lm(y ~ groups)
-    fitphy_ANOVA <- phylolm(y ~ groups, phy = tree, model = stoch_process)
+    if (stoch_process == "OU"){
+        model = "OUfixedRoot"
+    } else {
+        model = "BM"
+    }
+    fitphy_ANOVA <- phylolm(y ~ groups, phy = tree, model = model)
 
     ## DONE refaire avec ces modalités et évaluer les erreurs de type 1 et erreurs de type 2
     ## faire scénario H_0: mu egaux -> ANOVA se plante car dep entre les indivs

simulations/test-anova.phylo_MB.R

@@ -79,9 +79,9 @@ plot_data <- data.frame(
 ggplot(plot_data, aes(x = tested_method, y = puissance, fill = interaction(group_type, mu_type))) +
     geom_bar(stat = "identity", position = "dodge") +
     labs(
-        title = "Puissance vs Tested Method",
+        title = "Proportions correctes vs Tested Method (BM)",
         x = "Tested Method",
-        y = "Puissance"
+        y = "Proportions correctes"
     ) +
     theme_minimal()
 # TODO : Regarder la notice de lmertest pour l'implémentation de Satterthwaite

simulations/test-anova.phylo_OU.R

@@ -0,0 +1,89 @@
+library(phylolm)
+library(phylotools)
+library(phytools)
+library(ape)
+library(ggplot2)
+set.seed(1234)
+
+N <- 100 # Number of different simulations
+n <- 100
+
+# Arbre
+tree <- rphylo(n, 0.1, 0)
+
+## Groupes
+K <- 3
+get_group <- function(tip) {
+    if (tip %in% getDescendants(tree, 105)) {
+        return(2)
+    }
+    if (tip %in% getDescendants(tree, 110)) {
+        return(3)
+    }
+    return(1)
+}
+
+source("./simulations/functions-anova.R")
+
+# Computing groups
+phylo_groups <- as.factor(sapply(1:n, get_group))
+non_phylo_groups <- as.factor(sample(c(1, 2, 3), n, replace = TRUE))
+
+calcul_puissance <- function(data, test_method) {
+    mean(data[which(data$tested_method == test_method), ]$has_selected_correctly)
+}
+
+# Mu tous différents
+mu_vect <- c(1, 5, 10)
+# N répétitions pour les 2 groupes générés
+mu_diff_phylo_groups_results <- do.call("rbind", lapply(1:N, function(id) simulate_ANOVAs(sim_id = id, groups = phylo_groups, tree = tree, mu_vect = mu_vect, stoch_process = "OU")))
+mu_diff_non_phylo_groups_results <- do.call("rbind", lapply(1:N, function(id) simulate_ANOVAs(sim_id = id, groups = non_phylo_groups, tree = tree, mu_vect = mu_vect, stoch_process = "OU")))
+
+puissance_mu_diff_phylo_for_phylo_groups <- calcul_puissance(mu_diff_phylo_groups_results, "ANOVA-Phylo")
+puissance_mu_diff_classic_for_phylo_groups <- calcul_puissance(mu_diff_phylo_groups_results, "ANOVA")
+
+puissance_mu_diff_phylo_for_non_phylo_groups <- calcul_puissance(mu_diff_non_phylo_groups_results, "ANOVA-Phylo")
+puissance_mu_diff_classic_for_non_phylo_groups <- calcul_puissance(mu_diff_non_phylo_groups_results, "ANOVA")
+
+# Mu égaux
+mu_vect <- rep(1, K)
+# N répétitions pour les 2 groupes générés
+mu_equals_phylo_groups_results <- do.call("rbind", lapply(1:N, function(id) simulate_ANOVAs(sim_id = id, groups = phylo_groups, tree = tree, mu_vect = mu_vect)))
+mu_equals_non_phylo_groups_results <- do.call("rbind", lapply(1:N, function(id) simulate_ANOVAs(sim_id = id, groups = non_phylo_groups, tree = tree, mu_vect = mu_vect)))
+
+# Calcul de puissance
+puissance_mu_equals_phylo_for_phylo_groups <- calcul_puissance(mu_equals_phylo_groups_results, "ANOVA-Phylo")
+puissance_mu_equals_classic_for_phylo_groups <- calcul_puissance(mu_equals_phylo_groups_results, "ANOVA")
+
+puissance_mu_equals_phylo_for_non_phylo_groups <- calcul_puissance(mu_equals_non_phylo_groups_results, "ANOVA-Phylo")
+puissance_mu_equals_classic_for_non_phylo_groups <- calcul_puissance(mu_equals_non_phylo_groups_results, "ANOVA")
+
+# Graphiques
+puissances <- c(
+    puissance_mu_diff_phylo_for_phylo_groups,
+    puissance_mu_diff_classic_for_phylo_groups,
+    puissance_mu_equals_phylo_for_phylo_groups,
+    puissance_mu_equals_classic_for_phylo_groups,
+    puissance_mu_diff_phylo_for_non_phylo_groups,
+    puissance_mu_diff_classic_for_non_phylo_groups,
+    puissance_mu_equals_phylo_for_non_phylo_groups,
+    puissance_mu_equals_classic_for_non_phylo_groups
+)
+plot_data <- data.frame(
+    puissance = puissances,
+    tested_method = rep(c("ANOVA-Phylo", "ANOVA"), 4),
+    group_type = rep(c("phylo", "non_phylo"), each = 4),
+    mu_type = rep(rep(c("different", "equals"), each = 2), 2)
+)
+
+ggplot(plot_data, aes(x = tested_method, y = puissance, fill = interaction(group_type, mu_type))) +
+    geom_bar(stat = "identity", position = "dodge") +
+    labs(
+        title = "Proportions correctes vs Tested Method (OU)",
+        x = "Tested Method",
+        y = "Proportions correctes"
+    ) +
+    theme_minimal()
+# TODO : Regarder la notice de lmertest pour l'implémentation de Satterthwaite
+# TODO : En utilisant l'arbre étoile, on obtient un modele mixte classique donc on peut appliquer lmerTest
+

sources/test_satterthwaite_star_tree.R

@@ -1,7 +1,7 @@
 library(lmerTest)
 library(phylolm)
 
-source("test_satterthwaite_utils.R")
+source("./sources/test_satterthwaite_utils.R")
 
 set.seed(12891289)