Dear all,
I've been studying SEM, and I am trying to run the following model.
dataUse <- read.csv("SRI.cov.csv" )
covUse <- data.matrix(dataUse, rownames.force = NA)
variable_names <- paste0("Y",1:32)
Nobs <- 939
model_stepA <- '
F1 =~ Y2 + Y3 + start(-3000)*Y4
F2 =~ Y9 + Y11 + Y13
F3 =~ Y14 + Y16 + Y17 + Y18 + Y19
F4 =~ Y20 + Y21
F5 =~ Y22 + Y23 + Y24 + Y25 + Y26
F6 =~ Y28 + Y29
'
fit <- sem(model_stepA,
sample.cov = covUse,
sample.nobs = Nobs,
)
The model has no issue in Mplus with a starting value of -3000 for Y4.
The problem is that it does not run in lavaan with the following error:
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
Interestingly, the estimates and fit indices are identical for Mplus and lavaan.
The only thing different is standard errors, because the information matrix isn't inverted in lavaan.
I'd appreciate any suggestions how to make lavaan compute standard errors.
Best,
Lee.
I've attached the output of Mplus for comparison.