如何使用sjPlot将两个模型的估计值合并绘制到同一图表中

要实现将两个zeroinfl模型的估计结果合并到同一图表，且指定模型1的估计值在上、模型2在下，你可以直接给plot_model()传入模型列表，并通过绘图参数调整位置和样式。以下是完整的实现步骤和代码：

关键步骤说明 #

• 避免变量覆盖：分别拟合并保存两个目标模型（不要用同一个变量名覆盖）
• 传入模型列表：将两个模型放进一个列表，作为plot_model()的model参数
• 控制位置：通过position参数调整两个模型的估计值上下分布，同时设置统一的变量筛选（terms参数）确保两个模型的变量对齐

完整可运行代码 #

# 加载所需包
library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(ggplot2)
library(MASS)
library(pscl)
library(boot)

# 加载数据集
caterpillor=structure(list(id = 1:100,
               age = structure(c(1L, 1L, 2L, 1L, 
                                 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
                                 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 
                                 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 
                                 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 
                                 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 
                                 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 3L),
                               .Label = c("a", "b", "c"), class = "factor"),
               sex = structure(c(2L, 
                                 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
                                 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
                                 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 
                                 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
                                 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 
                                 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 
                                 2L, 1L, 1L),
                               .Label = c("F", "M"), class = "factor"),
               country = structure(c(1L, 
                                     1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
                                     2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 
                                     1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 
                                     2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 
                                     3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 
                                     1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
                                     2L, 2L, 2L),
                                   .Label = c("eng", "scot", "wale"), class = "factor"), 
               edu = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
                                 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 
                                 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
                                 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 
                                 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
                                 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 
                                 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L),
                               .Label = c("x", "y", "z"), class = "factor"),
               lungfunction = c(45L, 
                                23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L, 
                                70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L, 
                                50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L, 
                                23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L, 
                                70L, 69L, 90L, 50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L, 
                                50L, 62L, 45L, 23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 45L, 
                                23L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 25L, 45L, 70L, 69L, 
                                90L, 50L, 62L, 25L, 45L, 70L, 69L, 90L, 50L, 62L, 25L, 45L, 
                                70L, 69L, 90L),
               ivdays = c(15L, 26L, 36L, 34L, 2L, 4L, 5L, 
                          8L, 9L, 15L, 26L, 36L, 34L, 2L, 4L, 5L, 8L, 9L, 15L, 26L, 
                          0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
                          0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
                          0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
                          0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
                          0L, 0L, 0L, 0L, 0L, 5L, 8L, 9L, 36L, 34L, 2L, 4L, 5L, 8L, 
                          9L, 36L, 34L, 2L, 4L, 5L),
               no2_quintile = structure(c(1L, 
                                          1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                          1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                          2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                          3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                          3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
                                          4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 
                                          5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L),
                                        .Label = c("q1", "q2", 
                                                   "q3", "q4", "q5"), class = "factor")),
          class = "data.frame", row.names = c(NA, 
                                              -100L))

# 拟合两个目标模型（避免变量覆盖）
# 模型1：全数据集的完整模型
model_full <- zeroinfl(ivdays~age+sex+edu,
                 link="logit",
                 dist = "negbin",
                 data=caterpillor)

# 模型2：英格兰子集的完整模型
model_eng <- zeroinfl(ivdays~age+sex+edu,
                 link="logit",
                 dist = "negbin",
                 data=subset(caterpillor, country=="eng"))

# 合并绘制估计图，指定模型顺序和位置
plot_model(
  model = list(model_full, model_eng),  # 传入模型列表，顺序对应上下
  type = "est",
  terms = c("count_ageb", "count_agec", "zero_ageb", "zero_agec"),  # 统一筛选变量
  colors = c("#2E86AB", "#F24C4C"),  # 自定义模型颜色
  position = position_dodge2(width = 0.6, padding = 0.2),  # 调整位置，让模型1在上、模型2在下
  legend.title = "模型",
  axis.labels = c("计数部分: age=b", "计数部分: age=c", "零膨胀部分: age=b", "零膨胀部分: age=c")
) +
  theme(axis.text.y = element_text(size = 10))  # 可选：调整轴标签大小

效果说明 #

• 列表中第一个模型（model_full）的估计值会显示在上方，第二个模型（model_eng）在下方
• position_dodge2参数控制两个模型的估计值不重叠，同时保持在同一变量的垂直对齐线上
• 通过terms参数确保两个模型只展示指定的变量，避免因变量不一致导致的绘图错位

内容的提问来源于stack exchange，提问作者skpak