我使用lavaan
和semplot
库进行SEM,
我用包裹lavaan的例子(惠顿的异化稳定性)
lower <-
'
11.834,
6.947, 9.364,
6.819, 5.091, 12.532,
4.783, 5.028, 7.495, 9.986,
-3.839, -3.889, -3.841, -3.625, 9.610,
-21.899, -18.831, -21.748, -18.775, 35.522, 450.288
'
# convert to a full symmetric covariance matrix with names
wheaton.cov <- getCov(lower, names=c("anomia67","powerless67", "anomia71",
"powerless71","education","sei"))
# the model
wheaton.model <-
'
# measurement model
ses =~ education + sei
alien67 =~ anomia67 + powerless67
alien71 =~ anomia71 + powerless71
# equations
alien71 ~ alien67 + ses
alien67 ~ ses
# correlated residuals
anomia67 ~~ anomia71
powerless67 ~~ powerless71'
fit <- sem(wheaton.model, sample.cov=wheaton.cov, sample.nobs=932)
semPaths(fit,whatLables="par",layout = "spring")
我无法理解清楚和潜在变量的准确代表是什么。