使用ggeffects添加事后检验字母时位置异常且报错，求解决方案

错误原因 #

1. add.data = TRUE兼容性问题：你使用的是零膨胀Gamma模型（ziGamma），ggeffects的plot()函数自带的add.data = TRUE参数在这类模型下无法正确关联原始数据，触发"Raw data not available"警告，进而引发后续逻辑判断的缺失值错误。
2. 硬编码列索引不可靠：用lt[,7]或lt[,8]提取显著性字母的方式不严谨，不同cld()结果的列数会变化，容易导致索引错位。
3. 位置变量不匹配：原代码中计算的x_1基于generation，但绘制单因素（如temp）图时，该变量与x轴分组不对应，导致字母位置错误。

修正方案 #

放弃ggeffects::plot()的add.data参数，手动用ggplot2添加原始数据；用列名提取显著性字母，确保与预测数据分组匹配；调整字母位置与x轴分组对齐。

修正后的完整代码

# 加载包
library(ggeffects)
library(dplyr)
library(glmmTMB)
library(multcomp)
library(lsmeans)
library(ggplot2)

# 读取数据并转换因子类型
ds <- read.csv("https://raw.githubusercontent.com/Leprechault/trash/main/temp_ger_ds.csv")
ds$temp <- as.factor(ds$temp)
ds$generation <- as.factor(ds$generation)

# 拟合模型
mTCFd <- glmmTMB(development ~ temp * generation, data = ds,
               family = ziGamma(link = "log")) 

1. 绘制temp的效应图并添加显著性字母

# 计算边际均值与显著性分组
lsm.mTCFd.temp <- lsmeans(mTCFd, "temp")
lt <- cld(lsm.mTCFd.temp, Letters = letters, decreasing = TRUE) %>%
  mutate(temp = as.character(temp))

# 获取预测数据并匹配显著性字母
df_gg <- ggpredict(mTCFd, terms = "temp") %>%
  mutate(temp = as.character(x)) %>%
  left_join(lt %>% select(temp, .group), by = "temp")

# 绘图
ggplot(df_gg, aes(x = x, y = predicted)) +
  geom_point(data = ds, aes(x = temp, y = development), alpha = 0.3, position = position_jitter(width = 0.1)) +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) +
  geom_point(size = 2) +
  geom_text(aes(label = .group), vjust = -1, size = 4) +
  theme_bw()

2. 绘制generation的效应图并添加显著性字母

# 计算边际均值与显著性分组
lsm.mTCFd.gera <- lsmeans(mTCFd, "generation")
lt <- cld(lsm.mTCFd.gera, Letters = letters, decreasing = TRUE) %>%
  mutate(generation = as.character(generation))

# 获取预测数据并匹配显著性字母
df_gg <- ggpredict(mTCFd, terms = "generation") %>%
  mutate(generation = as.character(x)) %>%
  left_join(lt %>% select(generation, .group), by = "generation")

# 绘图
ggplot(df_gg, aes(x = x, y = predicted)) +
  geom_point(data = ds, aes(x = generation, y = development), alpha = 0.3, position = position_jitter(width = 0.1)) +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) +
  geom_point(size = 2) +
  geom_text(aes(label = .group), vjust = -1, size = 4) +
  theme_bw()

3. 绘制temp与generation的交互效应图并添加显著性字母

# 计算边际均值与显著性分组
lsm.mTCFd.temp.gera <- lsmeans(mTCFd, c("temp", "generation"))
lt <- cld(lsm.mTCFd.temp.gera, Letters = letters, decreasing = TRUE) %>%
  mutate(temp = as.character(temp), generation = as.character(generation))

# 获取预测数据并匹配显著性字母
df_gg <- ggpredict(mTCFd, terms = c("temp", "generation")) %>%
  mutate(temp = as.character(x), generation = as.character(group)) %>%
  left_join(lt %>% select(temp, generation, .group), by = c("temp", "generation"))

# 绘图
ggplot(df_gg, aes(x = x, y = predicted, color = group, group = group)) +
  geom_point(data = ds, aes(x = temp, y = development, color = generation), alpha = 0.3, position = position_jitter(width = 0.1)) +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2, position = position_dodge(0.5)) +
  geom_line() +
  geom_point(size = 2, position = position_dodge(0.5)) +
  geom_text(aes(label = .group), vjust = -1, size = 4, position = position_dodge(0.5)) +
  theme_bw()

内容的提问来源于stack exchange，提问作者Leprechault