潜增长曲线模型路径图自定义：移除截距与添加置信区间

问题说明 #

已用lavaan构建潜增长曲线模型（LGCM）并生成路径图，需完成两项调整：

• 移除预测变量x1，以及观测变量T0（基线）、T1（第1年）、T2（第2年）、T3（第3年）的截距节点
• 在路径图中显示带置信区间的参数估计值

原代码如下：

# Load relevant packages
library(lavaan) #for SEM LGCM
library(semTools) # for fit indices 
library(semPlot) # for path diagrams
library(semptools) # for path diagrams
library(tidyverse) # pipelines, exploring and visualizing data

# model
model_fit <- ' 
# intercept with fixed coefficients
  i =~ 1*T0 + 1*T1 + 1*T2 + 1*T3
# slope with fixed coefficients
  s =~ 0*T0 + 1*T1 + 2*T2 + 3*T3

# time invariant covaraites
 # regression of time-invariant covariates on intercept and slope factors
  i ~  x1 + covar1 + covar2 + covar3
  s ~ x1 + covar1 + covar2 + covar3
'

fit <- growth(model_fit, data=LGCM_data, missing="fiml", estimator = "MLR")
summary(fit,fit.measures=TRUE, ci=TRUE)


# Plot   
lgcm_no_covs <- semptools::drop_nodes(
                 object = semPlotModel(fit),
                 nodes = c("covar1", "covar2", "covar3")) 

lgcm_no_covs <- semPaths(lgcm_no_covs, whatLabels = "est",
                    label.norm = "PAL \nBaseline",
                    edge.label.cex = 1.0, label.cex = 2.0,
                    edge.color = "black", edge.label.color = "black",
                    height = 20, width = 12,
                    residuals = FALSE,
                    DoNotPlot= TRUE)  

my_label_list <- list(list(node = "i", to = "Intercept"),
                     list(node = "s", to = "Slope"),
                     list(node = "x1", to = "Predictor"),
                     list(node = "T0", to = "Baseline"),
                     list(node = "T1", to = "Year 1"),
                     list(node = "T2", to = "Year 2"),
                     list(node = "T3", to = "Year 3")
                     )

lgcm_no_covs <- change_node_label(lgcm_no_covs, my_label_list)

plot(lgcm_no_covs)

当前路径图效果：

解决方案 #

1. 移除指定节点（x1 + 观测变量截距） #

• 移除x1：在drop_nodes的nodes参数中加入"x1"，直接删除该节点
• 移除观测变量截距：在semPaths中添加intercepts = FALSE参数，关闭观测变量截距节点的显示

2. 显示带置信区间的估计值 #

semPaths不直接支持显示置信区间，需手动提取参数估计与CI，替换路径标签：

• 用parameterEstimates(fit, ci=TRUE)提取所有参数的估计值、95%置信区间上下限
• 匹配semPlotModel中每条边的参数，生成估计值 [下限, 上限]格式的标签
• 替换原路径标签

修改后完整代码 #

# Load relevant packages
library(lavaan)
library(semTools)
library(semPlot)
library(semptools)
library(tidyverse)

# 模型拟合
model_fit <- ' 
# 截距因子（固定载荷）
  i =~ 1*T0 + 1*T1 + 1*T2 + 1*T3
# 斜率因子（固定载荷）
  s =~ 0*T0 + 1*T1 + 2*T2 + 3*T3

# 时不变协变量回归
  i ~  x1 + covar1 + covar2 + covar3
  s ~ x1 + covar1 + covar2 + covar3
'

fit <- growth(model_fit, data=LGCM_data, missing="fiml", estimator = "MLR")
summary(fit, fit.measures=TRUE, ci=TRUE)

# 步骤1：移除不需要的节点（covar1-3 + x1）
lgcm_no_covs <- semptools::drop_nodes(
  object = semPlotModel(fit),
  nodes = c("covar1", "covar2", "covar3", "x1")
)

# 步骤2：生成基础路径图框架（关闭截距显示）
lgcm_no_covs <- semPaths(lgcm_no_covs, 
                        whatLabels = "est",
                        intercepts = FALSE,  # 关闭观测变量截距节点
                        label.norm = "PAL \nBaseline",
                        edge.label.cex = 1.0, label.cex = 2.0,
                        edge.color = "black", edge.label.color = "black",
                        height = 20, width = 12,
                        residuals = FALSE,
                        DoNotPlot= TRUE)  

# 修改节点标签
my_label_list <- list(list(node = "i", to = "截距"),
                     list(node = "s", to = "斜率"),
                     list(node = "T0", to = "基线"),
                     list(node = "T1", to = "第1年"),
                     list(node = "T2", to = "第2年"),
                     list(node = "T3", to = "第3年")
                     )
lgcm_no_covs <- change_node_label(lgcm_no_covs, my_label_list)

# 步骤3：提取参数估计与置信区间，替换路径标签
params <- parameterEstimates(fit, ci=TRUE) %>%
  mutate(
    # 生成带CI的标签：保留两位小数
    ci_label = sprintf("%.2f [%.2f, %.2f]", est, ci.lower, ci.upper),
    # 为匹配semPlot的边标识，创建参数对（lhs -> rhs）
    param_pair = paste(lhs, rhs, sep = "->")
  )

# 遍历每条边，替换标签
for (i in seq_along(lgcm_no_covs$edges$label)) {
  # 获取当前边的参数对（注意semPlot的边方向：from -> to）
  edge_pair <- paste(lgcm_no_covs$edges$from, lgcm_no_covs$edges$to, sep = "->")
  # 匹配对应的CI标签
  matched_label <- params$ci_label[params$param_pair == edge_pair]
  # 若匹配到则替换，否则保留原标签（固定载荷等）
  if (length(matched_label) > 0) {
    lgcm_no_covs$edges$label[i] <- matched_label
  }
}

# 绘制最终路径图
plot(lgcm_no_covs)

关键改动说明 #

• 在drop_nodes中加入"x1"直接删除该预测变量节点
• 添加intercepts = FALSE关闭观测变量的截距节点显示
• 提取参数估计时开启ci=TRUE，生成带置信区间的格式化标签
• 通过匹配参数对（lhs->rhs）将带CI的标签替换到对应路径上

内容的提问来源于stack exchange，提问作者AEP