添加一个在参与者内恒定但在参与者之间不同的数字（lv.2变量）[循环r，MLM]

Question

我想在多级模型中计算调节分析。我有 20 个任务块（1 级），供 33 名参与者（2 级）使用。对于我的相关 IV 和 DV，我已经编写了一个有效的循环：

for(i in 1:length(participants)) {
  participant.sub <- df[df$participant == participants[i],]
  participant.sub <- participant.sub[!is.na(participant.sub$participant),]
  for (j in 1:20) {
    block.sub <- participant.sub[participant.sub$block == j,]
    data.row <- c(unique(block.sub$participant),
                  j,
                  block.sub$slider_mentaleffort.response[!is.na(block.sub$slider_mentaleffort.response)],
                  block.sub$goal[!is.na(block.sub$goal)],
                  block.sub$sum_correct[!is.na(block.sub$sum_correct)])
    data.row <- data.row[2:6]
    dummy.df <- rbind(dummy.df, data.row) 
  }
}

现在我想把主持人也包括进来。调节变量是在实验开始前得到回答的李克特量表。每个人在每个尺度上都有一个确切的值。如何将这些值添加到当前循环/数据帧，以便我可以继续 lmer？

我试过这个

for(i in 1:length(participants)) {
  participant.sub <- df[df$participant == participants[i],]
  participant.sub <- participant.sub[!is.na(participant.sub$participant),]
  for (j in 1:20) {
    block.sub <- participant.sub[participant.sub$block == j,]
    data.row <- c(unique(block.sub$participant),
                  j,
                  block.sub$slider_mentaleffort.response[!is.na(block.sub$slider_mentaleffort.response)],
                  block.sub$goal[!is.na(block.sub$goal)],
                  block.sub$sum_correct[!is.na(block.sub$sum_correct)],
                  block.sub$slider_effort.response[!is.na(block.sub$slider_effort.response)],
                  block.sub$slider_talent.response[!is.na(block.sub$slider_talent.response)],
                  block.sub$slider_luck.response[!is.na(block.sub$slider_luck.response)],
                  block.sub$slider_taskdiff.response[!is.na(block.sub$slider_taskdiff.response)])
    data.row <- data.row[2:9] 
    dummy.df <- rbind(dummy.df, data.row) 
  }
}

和

for(i in 1:length(participants)) {
  participant.sub <- df[df$participant == participants[i],]
  participant.sub <- participant.sub[!is.na(participant.sub$participant),]
  for (j in 1:20) {
    block.sub <- participant.sub[participant.sub$block == j,]
    data.row <- c(unique(block.sub$participant),
                  j,
                  block.sub$slider_mentaleffort.response[!is.na(block.sub$slider_mentaleffort.response)],
                  block.sub$goal[!is.na(block.sub$goal)],
                  block.sub$sum_correct[!is.na(block.sub$sum_correct)],
                  block.sub$slider_effort.response,
                  block.sub$slider_talent.response,
                  block.sub$slider_luck.response,
                  block.sub$slider_taskdiff.response)
    data.row <- data.row[2:9] 
    dummy.df <- rbind(dummy.df, data.row) 
  }
}

但是没有效果。

Answer 1

最好使用 dpylr 并将数据切片到我想要的行中，然后将它们合并到一帧中，这样就可以排除 na 的

df_attitude <- df %>%
  filter(!is.na(slider_talent.response)) %>%
  select(participant, slider_talent.response, slider_luck.response, slider_effort.response, slider_taskdiff.response)
data <- data %>%
  merge(., df_attitude, by = "participant")

df_effort <- df %>%
  filter(!is.na(slider_mentaleffort.response)) %>%
  select(participant, block, slider_mentaleffort.response, sum_correct)
df_goal <- df %>%
  filter(!is.na(goal)) %>%
  select(participant, block, goal)

这是比循环更优雅、更灵活、更短的方式。