polarolouis/anova-phylogenetique-projet-msv
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✨ Beginning to add degrees of freedom to dataframe and adding TODOs objectives

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Louis Lacoste 2024-01-23 18:54:45 +01:00
parent f8e38ed979
commit bf8cfce2ea
1 changed files with 19 additions and 7 deletions
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26
R/anovaComparison.R
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@ -108,7 +108,8 @@ phyloanova_anova_pvalues <- function(
if (test_method == "satterthwaite") {
# For satterthwaite ddf computation
df2 <- ddf_satterthwaite_sum(phyloanova_res, tree)$ddf
df2 <- ddf_satterthwaite_sum(phyloanova_res, tree, REML = TRUE)$ddf
print(paste0("Satterthwaite ddf :", df2))
}
phyloanova_p_value <- pvalue_F_test(phyloanova_F_stat, df1 = df1, df2 = df2)
@ -132,7 +133,9 @@ phyloanova_anova_pvalues <- function(
list(
phyloanova_p_value = phyloanova_p_value,
anova_p_value = anova_p_value
anova_p_value = anova_p_value,
anova_df2 = nb_species - K,
phylo_df2 = df2
)
}
@ -152,12 +155,14 @@ simulate_matching_and_random <- function(
sigma2_phylo = sigma2_phylo, sigma2_measure = sigma2_measure,
stoch_process = stoch_process
)
matching_pvalues <- phyloanova_anova_pvalues(
matching_pval_df <- phyloanova_anova_pvalues(
traits = matching_phylo_traits,
groups = phylo_matching_groups, tree, stoch_process = stoch_process,
test_method = test_method, measurement_error = TRUE
)
matching_pvalues <- matching_pval_df[c(1, 2)]
matching_df2 <- matching_pval_df[c(3, 4)]
random_groups_traits <- compute_trait_values(
groups = random_groups,
@ -166,12 +171,14 @@ simulate_matching_and_random <- function(
stoch_process = stoch_process
)
random_groups_pvalues <- phyloanova_anova_pvalues(
random_groups_pval_df2 <- phyloanova_anova_pvalues(
traits = random_groups_traits,
groups = random_groups, tree, stoch_process = stoch_process,
test_method = test_method, measurement_error = TRUE
)
random_groups_pvalues <- random_groups_pval_df2[c(1,2)]
random_groups_df2 <- random_groups_pval_df2[c(3,4)]
# Concatenate pvalues
pvalues <- c(unlist(matching_pvalues), unlist(random_groups_pvalues))
@ -187,7 +194,8 @@ simulate_matching_and_random <- function(
anova_model = rep(c("phylo-anova", "anova"), 2),
group_type = rep(c("matching", "random"), each = 2),
pvalues = pvalues,
correctly_selected = correctly_selected
correctly_selected = correctly_selected,
df2 = unlist(c(matching_df2, random_groups_df2))
)
)
}
@ -233,6 +241,8 @@ compare_methods <- function(
stop("Unknown method to test.")
}
#  Generating data for each method
# TODO Séparer les deux fonctions de simulation et d'inférence
# TODO Utiliser les mêmes données pour les méthodes
##  To compute power
full_power_data <-
do.call("rbind", lapply(methods_to_test, function(method) {
@ -417,7 +427,7 @@ plot_comparison <- function(data, sim_parameters) {
#  TODO Adapt to the current code
## Standardized parameters
total_variance <- 1.0 # sigma2_phylo + sigma2_error, fixed [as tree_height = 1]
heri <- c(0.0, 0.5, 1.0) # heritability her = sigma2_phylo / total_variance. 0 means only noise. 1 means only phylo.
heri <- c(0.0, 0.25, 0.5, 1.0) # heritability her = sigma2_phylo / total_variance. 0 means only noise. 1 means only phylo.
snr <- 1 # signal to noise ratio snr = size_effect / total_variance
## Try several parameter values
@ -432,3 +442,5 @@ for (her in heri) {
res_sim_plot
ggsave(paste0("img/simulation_BM_her_", her, ".png"), plot = res_sim_plot)
}