R - 如何在多个数据插补后合并 SEM 回归的结果

Question

我正在R中的SEM上进行多个数据插补。我有一个特定的数据插补算法，我正在测试，这就是为什么我"手动"而不是使用 MItools 进行池化的原因。

for (i in 1:m){ # m is number of imputation
# Imputation
df_imputed <-  myImputationAlgorithm(df)
# Fit model
fitted_model<- sem(model, data=df_imputed, se="bootstrap",bootstrap=100)     
# Save parameters for pooling
# https://rdrr.io/cran/lavaan/man/lavInspect.html
betas <- append(betas, lavInspect(fitted_model, "coef"))
vars <- append(vars,   lavInspect(fitted_model, "vcov"))
}
# Pooling
summary(MIcombine(betas, vars))

我正在尝试使用 MIcombine(( 组合结果。我认为它应该可以工作，但我缺少有关输入格式的详细信息。我收到一个错误：Error in cbar + results[[i]] : non-conformable arrays.

我错过了什么？

Answer 1

我认为我可以直接从结果的lavaan矩阵中提取内容。

m<- 10 # Number of imputation
estimates <- as.data.frame(matrix(NA, nrow=29, ncol = m))   # Estimates
standErr <- as.data.frame(matrix(NA, nrow=29, ncol = m))    # Standard deviations
zvalue <- as.data.frame(matrix(NA, nrow=29, ncol = m))      # Z-value
pvalue <- as.data.frame(matrix(NA, nrow=29, ncol = m))      # P-value
tli <- as.data.frame(matrix(NA, nrow=1, ncol = m))          # TL 
cfi <- as.data.frame(matrix(NA, nrow=1, ncol = m))          # CFI
rmsea <- as.data.frame(matrix(NA, nrow=1, ncol = m))        # RMSEA 
for (i in 1:m){
print(cat("Imputation #",i,"n", sep= ""))
df_imputed <-  myImputationAlgorithm() 
# Estimation
fitted_model<- sem(model, data=df_imputed, se="bootstrap",bootstrap=100)         
# Extrcat results
estimates[[i]] <- parameterEstimates(fitted_model)$est    # Estimate
standErr[[i]]  <- parameterEstimates(fitted_model)$se     # Standard Error
zvalue[[i]]    <- parameterEstimates(fitted_model)$z      # z-value  
pvalue[[i]]    <- parameterEstimates(fitted_model)$pvalue # p-value
tli[[i]]       <- inspect(fitted_model, "fit")["tli"]     # TLI
cfi[[i]]       <- inspect(fitted_model, "fit")["cfi"]     # CFI
rmsea[[i]]     <- inspect(fitted_model, "fit")["rmsea"]   # RMSEA

}

# Pooling
mean_estimates  <- rowMeans(estimates)
mean_standErr   <- rowMeans(standErr)
mean_zvalue     <- rowMeans(zvalue)
mean_pvalue     <- rowMeans(pvalue)
mean_tli        <- rowMeans(tli)
mean_cfi        <- rowMeans(cfi)
mean_rmsea      <- rowMeans(rmsea)