149 lines
4.4 KiB
R
149 lines
4.4 KiB
R
library(SuppDists)
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library(dplyr)
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library(tidyr)
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setwd("/home/ladmin/Documents/DND/EOHI/eohi3")
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between_vars <- c("perspective", "temporalDO")
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within_vars_MEAN <- c(
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"past_pref_MEAN", "past_pers_MEAN", "past_val_MEAN",
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"fut_pref_MEAN", "fut_pers_MEAN", "fut_val_MEAN"
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)
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within_vars_DGEN <- c(
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"past_pref_DGEN", "past_pers_DGEN", "past_val_DGEN",
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"fut_pref_DGEN", "fut_pers_DGEN", "fut_val_DGEN"
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)
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df <- read.csv("eohi3.csv", stringsAsFactors = FALSE, check.names = FALSE, na.strings = "NA")
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anova_data_MEAN <- df %>%
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select(pID, all_of(between_vars), all_of(within_vars_MEAN)) %>%
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filter(!is.na(perspective), perspective != "",
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!is.na(temporalDO), temporalDO != "")
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long_data_MEAN <- anova_data_MEAN %>%
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pivot_longer(
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cols = all_of(within_vars_MEAN),
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names_to = "variable",
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values_to = "MEAN_SCORE"
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) %>%
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mutate(
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time = ifelse(grepl("^past_", variable), "past", "fut"),
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domain = case_when(
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grepl("_pref_MEAN$", variable) ~ "pref",
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grepl("_pers_MEAN$", variable) ~ "pers",
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grepl("_val_MEAN$", variable) ~ "val",
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TRUE ~ NA_character_
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)
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) %>%
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mutate(
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TIME = factor(time, levels = c("past", "fut")),
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DOMAIN = factor(domain, levels = c("pref", "pers", "val")),
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perspective = factor(perspective),
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temporalDO = factor(temporalDO)
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) %>%
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select(pID, perspective, temporalDO, TIME, DOMAIN, MEAN_SCORE) %>%
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filter(!is.na(MEAN_SCORE))
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cell_vars_MEAN <- long_data_MEAN %>%
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group_by(perspective, temporalDO, TIME, DOMAIN) %>%
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summarise(
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n = n(),
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variance = var(MEAN_SCORE, na.rm = TRUE),
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.groups = "drop"
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)
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fmax_by_cell_MEAN <- cell_vars_MEAN %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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Fmax_observed = max(variance, na.rm = TRUE) / min(variance, na.rm = TRUE),
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df_min = min(n) - 1L,
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.groups = "drop"
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)
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k <- 4
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fmax_table_MEAN <- fmax_by_cell_MEAN %>%
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rowwise() %>%
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mutate(
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alpha_0.05 = SuppDists::qmaxFratio(0.95, df = df_min, k = k),
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alpha_0.01 = SuppDists::qmaxFratio(0.99, df = df_min, k = k)
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) %>%
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ungroup() %>%
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mutate(
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Fmax_observed = round(Fmax_observed, 4),
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alpha_0.05 = round(alpha_0.05, 4),
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alpha_0.01 = round(alpha_0.01, 4)
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) %>%
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select(TIME, DOMAIN, Fmax_observed, alpha_0.05, alpha_0.01)
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# ---- MEAN: Print observed Hartley ratios ----
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cat("\n--- Hartley ratios (MEAN) ---\n")
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fmax_table_MEAN %>%
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mutate(across(where(is.numeric), ~ format(round(., 4), nsmall = 4))) %>%
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print()
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# ---- DGEN: Observed Hartley ratios ----
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anova_data_DGEN <- df %>%
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select(pID, all_of(between_vars), all_of(within_vars_DGEN)) %>%
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filter(!is.na(perspective), perspective != "",
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!is.na(temporalDO), temporalDO != "")
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long_data_DGEN <- anova_data_DGEN %>%
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pivot_longer(
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cols = all_of(within_vars_DGEN),
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names_to = "variable",
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values_to = "DGEN_SCORE"
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) %>%
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mutate(
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time = ifelse(grepl("^past_", variable), "past", "fut"),
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domain = case_when(
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grepl("_pref_DGEN$", variable) ~ "pref",
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grepl("_pers_DGEN$", variable) ~ "pers",
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grepl("_val_DGEN$", variable) ~ "val",
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TRUE ~ NA_character_
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)
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) %>%
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mutate(
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TIME = factor(time, levels = c("past", "fut")),
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DOMAIN = factor(domain, levels = c("pref", "pers", "val")),
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perspective = factor(perspective),
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temporalDO = factor(temporalDO)
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) %>%
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select(pID, perspective, temporalDO, TIME, DOMAIN, DGEN_SCORE) %>%
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filter(!is.na(DGEN_SCORE))
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cell_vars_DGEN <- long_data_DGEN %>%
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group_by(perspective, temporalDO, TIME, DOMAIN) %>%
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summarise(
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n = n(),
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variance = var(DGEN_SCORE, na.rm = TRUE),
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.groups = "drop"
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)
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fmax_by_cell_DGEN <- cell_vars_DGEN %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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Fmax_observed = max(variance, na.rm = TRUE) / min(variance, na.rm = TRUE),
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df_min = min(n) - 1L,
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.groups = "drop"
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)
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fmax_table_DGEN <- fmax_by_cell_DGEN %>%
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rowwise() %>%
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mutate(
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alpha_0.05 = SuppDists::qmaxFratio(0.95, df = df_min, k = k),
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alpha_0.01 = SuppDists::qmaxFratio(0.99, df = df_min, k = k)
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) %>%
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ungroup() %>%
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mutate(
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Fmax_observed = round(Fmax_observed, 4),
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alpha_0.05 = round(alpha_0.05, 4),
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alpha_0.01 = round(alpha_0.01, 4)
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) %>%
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select(TIME, DOMAIN, Fmax_observed, alpha_0.05, alpha_0.01)
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cat("\n--- Hartley ratios (DGEN) ---\n")
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fmax_table_DGEN %>%
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mutate(across(where(is.numeric), ~ format(round(., 4), nsmall = 4))) %>%
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print() |