mirror of
https://github.com/Polarolouis/anova-phylogenetique-projet-msv.git
synced 2026-06-17 10:15:25 +02:00
89 lines
3.6 KiB
R
89 lines
3.6 KiB
R
library(phylolm)
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library(phylotools)
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library(phytools)
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library(ape)
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library(ggplot2)
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set.seed(1234)
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N <- 100 # Number of different simulations
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n <- 100
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# Arbre
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tree <- rphylo(n, 0.1, 0)
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## Groupes
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K <- 3
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get_group <- function(tip) {
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if (tip %in% getDescendants(tree, 105)) {
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return(2)
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}
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if (tip %in% getDescendants(tree, 110)) {
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return(3)
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}
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return(1)
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}
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source("./simulations/functions-anova.R")
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# Computing groups
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phylo_groups <- as.factor(sapply(1:n, get_group))
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non_phylo_groups <- as.factor(sample(c(1, 2, 3), n, replace = TRUE))
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calcul_puissance <- function(data, test_method) {
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mean(data[which(data$tested_method == test_method), ]$has_selected_correctly)
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}
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# Mu tous différents
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mu_vect <- c(1, 5, 10)
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# N répétitions pour les 2 groupes générés
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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")))
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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")))
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puissance_mu_diff_phylo_for_phylo_groups <- calcul_puissance(mu_diff_phylo_groups_results, "ANOVA-Phylo")
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puissance_mu_diff_classic_for_phylo_groups <- calcul_puissance(mu_diff_phylo_groups_results, "ANOVA")
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puissance_mu_diff_phylo_for_non_phylo_groups <- calcul_puissance(mu_diff_non_phylo_groups_results, "ANOVA-Phylo")
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puissance_mu_diff_classic_for_non_phylo_groups <- calcul_puissance(mu_diff_non_phylo_groups_results, "ANOVA")
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# Mu égaux
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mu_vect <- rep(1, K)
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# N répétitions pour les 2 groupes générés
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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)))
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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)))
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# Calcul de puissance
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puissance_mu_equals_phylo_for_phylo_groups <- calcul_puissance(mu_equals_phylo_groups_results, "ANOVA-Phylo")
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puissance_mu_equals_classic_for_phylo_groups <- calcul_puissance(mu_equals_phylo_groups_results, "ANOVA")
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puissance_mu_equals_phylo_for_non_phylo_groups <- calcul_puissance(mu_equals_non_phylo_groups_results, "ANOVA-Phylo")
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puissance_mu_equals_classic_for_non_phylo_groups <- calcul_puissance(mu_equals_non_phylo_groups_results, "ANOVA")
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# Graphiques
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puissances <- c(
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puissance_mu_diff_phylo_for_phylo_groups,
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puissance_mu_diff_classic_for_phylo_groups,
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puissance_mu_equals_phylo_for_phylo_groups,
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puissance_mu_equals_classic_for_phylo_groups,
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puissance_mu_diff_phylo_for_non_phylo_groups,
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puissance_mu_diff_classic_for_non_phylo_groups,
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puissance_mu_equals_phylo_for_non_phylo_groups,
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puissance_mu_equals_classic_for_non_phylo_groups
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)
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plot_data <- data.frame(
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puissance = puissances,
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tested_method = rep(c("ANOVA-Phylo", "ANOVA"), 4),
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group_type = rep(c("phylo", "non_phylo"), each = 4),
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mu_type = rep(rep(c("different", "equals"), each = 2), 2)
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)
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ggplot(plot_data, aes(x = tested_method, y = puissance, fill = interaction(group_type, mu_type))) +
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geom_bar(stat = "identity", position = "dodge") +
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labs(
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title = "Proportions correctes vs Tested Method (OU)",
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x = "Tested Method",
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y = "Proportions correctes"
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) +
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theme_minimal()
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# TODO : Regarder la notice de lmertest pour l'implémentation de Satterthwaite
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# TODO : En utilisant l'arbre étoile, on obtient un modele mixte classique donc on peut appliquer lmerTest
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