结构方程模型计算Fisher's C值

rm(list = ls())

# 安装并加载必要的包
library(piecewiseSEM)
library(lavaan)

# 读取数据
dat <- read.csv("~/Downloads/MYDATA(1).csv", header = T, row.names = 1)
all.scaled <- as.data.frame(scale(dat, center = FALSE))

# 转换为分段回归模型
# 将SEM模型转换为多个回归方程，分别创建每个模型
model1 <- lm(RR ~ ST + I + AB + TF + PS, data = all.scaled)
model2 <- lm(AB ~ ST + I, data = all.scaled)
model3 <- lm(APH ~ AB + TF + PS + ST + I, data = all.scaled)  # 注意这里包含了直接效应 I
model4 <- lm(TF ~ ST + PS, data = all.scaled)
model5 <- lm(PS ~ I, data = all.scaled)
model6 <- lm(I ~ ST, data = all.scaled)

# 创建piecewiseSEM对象
psem_model <- psem(model1, model2, model3, model4, model5, model6)

# 获取Fisher's C统计量
fisher_c_result <- fisherC(psem_model)
print(fisher_c_result)

# 获取模型汇总，包括Fisher's C值
summary(psem_model)