polarolouis/anova-phylogenetique-projet-msv
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Try standardized parameters

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This commit is contained in:
Paul Bastide 2024-01-16 14:39:10 +01:00
parent fe02088233
commit 008f2cce96
1 changed files with 17 additions and 13 deletions
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30
simulations/test-anova.phylo_MB.R
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@ -57,7 +57,9 @@ calcul_proportion_bonne_selection <- function(data, test_method) {
mean(data[which(data$tested_method == test_method), ]$has_selected_correctly) mean(data[which(data$tested_method == test_method), ]$has_selected_correctly)
} }
plot_different_sigmas <- function(sigma2_measure_err, sigma2_intra_species, mu_vect_different = c(1,4)) { plot_different_sigmas <- function(sigma2_measure_err,
sigma2_intra_species, # PB : this is not a very good name, as the phylo variance of the BM is inter-species typically. Maybe just "sigma2_phylo" ?
mu_vect_different = c(0, 1,-1)) {
# Mu tous différents # Mu tous différents
mu_vect <- mu_vect_different mu_vect <- mu_vect_different
# N répétitions pour les 2 groupes générés # N répétitions pour les 2 groupes générés
@ -87,7 +89,7 @@ plot_different_sigmas <- function(sigma2_measure_err, sigma2_intra_species, mu_v
puissance_mu_diff_classic_for_non_phylo_groups <- calcul_proportion_bonne_selection(mu_diff_non_phylo_groups_results, "ANOVA") puissance_mu_diff_classic_for_non_phylo_groups <- calcul_proportion_bonne_selection(mu_diff_non_phylo_groups_results, "ANOVA")
# Mu égaux # Mu égaux
mu_vect <- rep(1, K) mu_vect <- rep(0, K)
# N répétitions pour les 2 groupes générés # N répétitions pour les 2 groupes générés
mu_equals_phylo_groups_results <- do.call( mu_equals_phylo_groups_results <- do.call(
"rbind", "rbind",
@ -166,15 +168,17 @@ plot_different_sigmas <- function(sigma2_measure_err, sigma2_intra_species, mu_v
# TODO : En utilisant l'arbre étoile, on obtient un modele mixte classique donc on peut appliquer lmerTest # TODO : En utilisant l'arbre étoile, on obtient un modele mixte classique donc on peut appliquer lmerTest
ggsave <- function(..., bg = "white") ggplot2::ggsave(..., bg = bg) ggsave <- function(..., bg = "white") ggplot2::ggsave(..., bg = bg)
# Seulement l'information phylo ## Standardized parameters
only_phylo_data <- plot_different_sigmas(0, 1, mu_vect_different = c(1,5,10)) total_variance <- 1.0 # sigma2_phylo + sigma2_error, fixed [as tree_height = 1]
plot_only_phylo <- only_phylo_data$plot heri <- c(0.0, 0.5, 1.0) # heritability her = sigma2_phylo / total_variance. 0 means only noise. 1 means only phylo.
plot_only_phylo snr <- 1 # signal to noise ratio snr = size_effect / total_variance
# 2 erreurs
both_errors_identical_data <- plot_different_sigmas(1, 1, mu_vect_different = c(1, 5, 10))
plot_both_errors_identical <- both_errors_identical_data$plot
plot_both_errors_identical
ggsave("img/simulation_power_pure_BM.png", plot = plot_only_phylo) # BM pur ## Try several parameter values
ggsave("img/simulation_power_pure_both_error.png", plot = plot_both_errors_identical) # Both error same for (her in heri) {
# ggsave("img/simulation_power_pure_both_error_more_phylo.png", plot = plot_different_sigmas(1, 2)) # Both error same res_sim <- plot_different_sigmas(sigma2_measure_err = (1 - her) * total_variance,
sigma2_intra_species = her * total_variance,
mu_vect_different = c(0, snr * total_variance, -snr * total_variance))
res_sim_plot <- res_sim$plot
res_sim_plot
ggsave(paste0("img/simulation_power_BM_her_", her, ".png"), plot = res_sim_plot)
}