Example of critical p-values for three basic multiple testing adjustments.

multiple testing adjustment

library(tidyverse, jmv, ggplot2)
library(ggthemes)
data <- read_csv(file = "https://raw.githubusercontent.com/methodenlehre/data/master/alkohol-aggression.csv") %>%
    mutate(alkoholbedingung = factor(alkoholbedingung,
                                     levels = c("kein_alkohol", "placebo", "anti_placebo", "alkohol")))


# 2. ANOVA inkl. Post-hoc Tests und Helmert-Kontrasten mit jamovi-Syntax

jmv_res <- jmv::ANOVA(
    formula = aggressivitaet ~ alkoholbedingung,
    data = data,
    effectSize = c("eta", "omega"),
    homo = TRUE,
    contrasts = list(
        list(
            var="alkoholbedingung",
            type="helmert")),
    postHoc = ~ alkoholbedingung,
    postHocCorr = c("tukey", "none", "bonf", "holm"),
    emMeans = ~ alkoholbedingung,
    emmPlotData = TRUE,
    emmPlotError = "se",
    emmTables = TRUE,
    emmWeights = FALSE)



# Tabelle extrahieren
res_table <- as_data_frame(jmv_res$postHoc[[1]]$asDF)
alpha <- 0.05

res_tab <- res_table %>% arrange(pnone) %>% # Tabelle nach p-Werten sortieren
    mutate(order = 1:n(),
           revorder = n():1,
           pkrit_holm = alpha / revorder,
           pkrit_bon = alpha / max(revorder),
           pkrit_sidak = 1 - (1 - alpha)^(1/max(revorder))) %>%
    select(order, pnone, starts_with("pkrit")) %>%
    pivot_longer(cols = pkrit_holm:pkrit_sidak, names_to = "pkrit") # wide to long

# plotten
ggplot(data = res_tab, aes(x = order, y = pnone, col = pkrit)) +
    facet_grid(~pkrit) +
    geom_point() +
    geom_line(aes(y = value)) +
    theme_tufte()