R 从Lavan中提取误差协方差矩阵_R_Extract_R Lavaan_Sem

R 从Lavan中提取误差协方差矩阵

R 从Lavan中提取误差协方差矩阵,r,extract,r-lavaan,sem,R,Extract,R Lavaan,Sem,我想从sem（）中提取误差协方差矩阵，但不知道是否正确。我使用了inspect（“cov.ov”），对于“cov.ov”，文档中说“模型隐含方差协方差矩阵”。这和误差协方差矩阵一样吗？代码如下： # convert vector of correlations into matrix wisc4.cor <- lav_matrix_lower2full(c(1,0.72,1,0.64,0.63,1,0.51,0.48,0.37,1,0.37,0.38,0.38,0.38,1)) #

我想从

sem（）

中提取误差协方差矩阵，但不知道是否正确。我使用了

inspect（“cov.ov”）

，对于

“cov.ov”

，文档中说“模型隐含方差协方差矩阵”。这和误差协方差矩阵一样吗？代码如下：

# convert vector of correlations into matrix
   wisc4.cor <- lav_matrix_lower2full(c(1,0.72,1,0.64,0.63,1,0.51,0.48,0.37,1,0.37,0.38,0.38,0.38,1))
# enter the SDs
   wisc4.sd <- c(3.01 , 3.03 , 2.99 , 2.89 , 2.98)
# name the variables
   colnames(wisc4.cor) <- rownames(wisc4.cor) <- c("Information", "Similarities", "Word.Reasoning", "Matrix.Reasoning", "Picture.Concepts")
   names(wisc4.sd) <-  c("Information", "Similarities", "Word.Reasoning", "Matrix.Reasoning", "Picture.Concepts")
# convert correlations and SDs to covarainces
   wisc4.cov <- cor2cov(wisc4.cor,wisc4.sd)
# specify single factor model
   wisc4.model<-'
   g =~ a*Information + b*Similarities + c*Word.Reasoning + d*Matrix.Reasoning + e*Picture.Concepts
'
# fit model
   wisc4.fit <- sem(model=wisc4.model, sample.cov=wisc4.cov, sample.nobs=550,  std.lv=FALSE)
#does this extract the error covariance matrix?
   inspect(wisc4.fit, "cov.ov")

#将相关向量转换为矩阵
wisc4.cor您需要残差的cov矩阵：
residuals(wisc4.fit)$cov