R 使用inspect（）报告的Lavan不可信矩阵

出于教学目的，我正在模拟与固定效应模型一致的数据，然后使用Lavan在SEM中运行该模型。我不明白Lavan使用

inspect（）

函数给我的底层矩阵是怎么回事

以下是用于再现性目的的协方差矩阵：

obs <- matrix( c( 2.1531960, 0.9219589, 1.1247727, 2.1399405, 1.6009631, 1.7877342,
                  0.9219589, 2.0183384, 1.1263892, 1.6586093, 2.0530063, 1.7664361,
                  1.1247727, 1.1263892, 1.9152931, 1.8183510, 1.7904083, 1.9676634,
                  2.1399405, 1.6586093, 1.8183510, 4.2225644, 3.0380404, 3.1018654,
                  1.6009631, 2.0530063, 1.7904083, 3.0380404, 4.0023862, 2.9819462,
                  1.7877342, 1.7664361, 1.9676634, 3.1018654, 2.9819462, 3.8469132), 
               nrow = 6, ncol = 6)

lambda矩阵是一个零矩阵：

## $lambda
##    n y1 y2 y3 x1 x2 x3
## y1 0  0  0  0  0  0  0
## y2 0  0  0  0  0  0  0
## y3 0  0  0  0  0  0  0
## x1 0  0  0  0  0  0  0
## x2 0  0  0  0  0  0  0
## x3 0  0  0  0  0  0  0

但我将潜在个体效应变量定义为

n=~1*y1+1*y2+1*y3

，据我所知，lambda矩阵应该如下所示：

## $lambda
##    n y1 y2 y3 x1 x2 x3
## y1 1  1  0  0  0  0  0
## y2 1  0  1  0  0  0  0
## y3 1  0  0  1  0  0  0
## x1 0  0  0  0  1  0  0
## x2 0  0  0  0  0  1  0
## x3 0  0  0  0  0  0  1

---

我已经亲自检查过了。将lambda作为一个由零组成的矩阵会把一切都搞糟。有人知道这里发生了什么吗？关于拉万使用的矩阵表示法，有什么我不明白的吗

请注意，

inspect

是一个较旧的函数，现在用作

lainspect

的快捷方式。进一步注意矩阵的名称

library(lavaan)

obs <- matrix( c( 2.1531960, 0.9219589, 1.1247727, 2.1399405, 1.6009631, 1.7877342,
                  0.9219589, 2.0183384, 1.1263892, 1.6586093, 2.0530063, 1.7664361,
                  1.1247727, 1.1263892, 1.9152931, 1.8183510, 1.7904083, 1.9676634,
                  2.1399405, 1.6586093, 1.8183510, 4.2225644, 3.0380404, 3.1018654,
                  1.6009631, 2.0530063, 1.7904083, 3.0380404, 4.0023862, 2.9819462,
                  1.7877342, 1.7664361, 1.9676634, 3.1018654, 2.9819462, 3.8469132), 
               nrow = 6, ncol = 6, dimnames = list(NULL, c(paste0(rep(c("x", "y"), each = 3), 1:3)))
               )

导致

##    n
## y1 0
## y2 0
## y3 0

您可以使用

what=“est”

访问估计参数

您可以使用

n=~y1+y2+y3

交叉检查该行为。最后，随着回归语句的引入，

y

变量成为内生变量，而不是测量负荷。也就是说，对于

n

上的“加载”，您需要查看

beta

矩阵

fe_sem <- "
# Define individual effects variable 
n =~ 1*y1 + 1*y2 + 1*y3 
# Regressions, constrain coefficient to be equal over time
y1 ~ b*x1
y2 ~ b*x2 
y3 ~ b*x3
# Allow unrestricted correlation between eta and covariates
n ~~ x1 + x2 + x3 
x1 ~~ x2 + x3
x2 ~~ x3 
# Constrain residual variances to be equal over time
y1 ~~ e*y1
y2 ~~ e*y2
y3 ~~ e*y3
"
fe_sem.fit <- sem( model = fe_sem, sample.cov = obs, sample.nobs = 300)
lavInspect(fe_sem.fit, what = "est")$lambda

及

给你

##    n y1 y2 y3    x1    x2    x3
## n  0  0  0  0 0.000 0.000 0.000
## y1 1  0  0  0 0.326 0.000 0.000
## y2 1  0  0  0 0.000 0.326 0.000
## y3 1  0  0  0 0.000 0.000 0.326
## x1 0  0  0  0 0.000 0.000 0.000
## x2 0  0  0  0 0.000 0.000 0.000
## x3 0  0  0  0 0.000 0.000 0.000

---

谢谢，这正是我想要的！总是一种乐趣，而不是一件家务

##    n
## y1 0
## y2 0
## y3 0

lavInspect(fe_sem.fit, what = "est")$lambda

##    n
## y1 1
## y2 1
## y3 1

fe_sem <- "
# Define individual effects variable 
n =~ 1*y1 + 1*y2 + 1*y3 
# Regressions, constrain coefficient to be equal over time
y1 ~ b*x1
y2 ~ b*x2 
y3 ~ b*x3
# Allow unrestricted correlation between eta and covariates
n ~~ x1 + x2 + x3 
x1 ~~ x2 + x3
x2 ~~ x3 
# Constrain residual variances to be equal over time
y1 ~~ e*y1
y2 ~~ e*y2
y3 ~~ e*y3
"
fe_sem.fit <- sem( model = fe_sem, sample.cov = obs, sample.nobs = 300)
lavInspect(fe_sem.fit, what = "est")$lambda

##    n y1 y2 y3 x1 x2 x3
## y1 0  1  0  0  0  0  0
## y2 0  0  1  0  0  0  0
## y3 0  0  0  1  0  0  0
## x1 0  0  0  0  1  0  0
## x2 0  0  0  0  0  1  0
## x3 0  0  0  0  0  0  1

lavInspect(fe_sem.fit, what = "est")$beta

##    n y1 y2 y3    x1    x2    x3
## n  0  0  0  0 0.000 0.000 0.000
## y1 1  0  0  0 0.326 0.000 0.000
## y2 1  0  0  0 0.000 0.326 0.000
## y3 1  0  0  0 0.000 0.000 0.326
## x1 0  0  0  0 0.000 0.000 0.000
## x2 0  0  0  0 0.000 0.000 0.000
## x3 0  0  0  0 0.000 0.000 0.000