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介绍

我不久前遵循了本指南并返回以进行我自己的重复测量方差分析： https ://www.datanovia.com/en/lessons/repeated-measures-anova-in-r/

脚本

到目前为止，我有这个脚本并且没有任何直接的问题：

#### Local R data ####
hwk1 <- structure(list(group = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 
                                           1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), format.spss = "F8.2", class = c("haven_labelled", 
                                                                                                                         "vctrs_vctr", "double"), labels = c(`stress management` = 1, 
                                                                                                                                                             control = 2)), time1 = structure(c(39, 46, 32, 29, 24, 35, 42, 
                                                                                                                                                                                                51, 44, 32, 31, 48, 46, 47, 39, 30, 35, 40, 46, 58, 47, 39, 36, 
                                                                                                                                                                                                54), format.spss = "F8.2"), time2 = structure(c(37, 42, 32, 33, 
                                                                                                                                                                                                                                                22, 32, 33, 47, 41, 37, 38, 44, 48, 45, 35, 31, 33, 38, 46, 52, 
                                                                                                                                                                                                                                                44, 41, 39, 55), format.spss = "F8.2"), time3 = structure(c(36, 
                                                                                                                                                                                                                                                                                                            38, 34, 31, 18, 29, 43, 50, 36, 38, 39, 39, 47, 41, 38, 33, 37, 
                                                                                                                                                                                                                                                                                                            38, 51, 55, 46, 43, 41, 48), format.spss = "F8.2")), class = c("tbl_df", 
                                                                                                                                                                                                                                                                                                                                                                           "tbl", "data.frame"), row.names = c(NA, -24L))

#### Load libraries ####
library(tidyverse)
library(correlation)
library(rstatix)
library(ggpubr)

#### Pivot data ####
pivo_hwk1 <- hwk1 %>% 
  gather(key = "time",
         value = "stress",
         time1,
         time2,
         time3) %>% 
  convert_as_factor(group,
                    time)

#### Descriptives ####
pivo_hwk1 %>% 
  group_by(group,
           time) %>% 
  get_summary_stats(stress,
                    type = "mean_sd")

#### Simple boxplot of means ####
bxp <- ggboxplot(pivo_hwk1,
                 x="time",
                 y="stress",
                 color = "group",
                 add = "point",
                 palette = "lancet")
bxp

#### Check outliers ####
pivo_hwk1 %>% 
  group_by(group,
           time) %>% 
  identify_outliers()

#### Normality: Shapiro ####
pivo_hwk1 %>% 
  group_by(group,
           time) %>% 
  shapiro_test(stress)

#### Normality: QQ ####
ggqqplot(pivo_hwk1,
         x="stress",
         color = "group",
         palette = "lancet",
         title = "QQ Plot of Group x Time",
         subtitle = "Group 1 = Treatment, Group 2 = Control")+
  facet_wrap(~time)+
  theme_bw()+
  theme(plot.title = element_text(face = "bold"),
        plot.background = element_rect(fill = "steelblue"),
        axis.text = element_text(color = "black"))

问题

好吧，我尝试运行实际的重复测量方差分析，但我对如何将其输入 anova_test 命令感到困惑：

#### RMANOVA ####
res.aov <- anova_test(
  data = pivo_hwk1,
  dv = stress, 
  wid = group,
  within = c(treatment, 
             time))
get_anova_table(res.aov)

该res.aov位不起作用，很明显原因是wid部分。但是，我不确定我应该怎么做才能修改它，因为我从数据集中尝试的变量的每个组合似乎都不起作用。任何建议都会很棒！

编辑：

看起来我能够使用此代码运行 ANOVA：

# Read the data:
hwk1_raw <- read_csv("C:/Users/DELL/Dropbox/My PC (DESKTOP-SUOCLVS)/Desktop/RM II ASSIGN I/hwk1_raw.csv")

# Create ID values:
hwk1_raw$id <- c(1,2,3,4,5,6,7,8,9,10,
                 11,12,13,14,15,16,17,
                 18,19,20,21,22,23,24)

# Add ID value as character:
hwk1_raw$id <- as.character(hwk1_raw$id)

# Pivot data:
pivo_raw <- hwk1_raw %>% 
  pivot_longer(
    cols = 2:4,
    names_to = "time",
    values_to = "stress"
  )

# Running ANOVA:
res.aov <- anova_test(
  data = pivo_raw,
  wid = id,
  dv = stress,
  within = time,
  between = group
)
res.aov

但是，我认为我仍然无法确定如何进行成对比较。据我所知，应该只有一对，所以应该是这样的：

pivo_raw %>%  
  pairwise_t_test(
  data = pivo_raw,
    formula = stress ~ group, 
    paired = T,
    p.adjust.method = "bonferroni")

但它只是吐出这个：

Error in t.test.default(x = numeric(0), y = numeric(0), paired = TRUE) : not enough 'x' observations

有人可以建议从这里做什么吗？

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