How to go past a model implied cov not positive definite error?

I am trying to specify the CLPM in the attached figure (It's an extension of Zyphur's 2020 general CLPM, but with PGSs). I specified both using lavaan syntax in umx and matrix algebra (first time, hope it is correct, but looks like so). It is identified, the model runs ok. But when I try to get power or the ncp statistic it fails, as the model's implied cov is not positive definite.

- What can I do to avoid this? lbounds and ubounds will help me there?

Lavaan code:


model = "
# random intercepts
xir =~ 1*X1 + 1*X2 + 1*X3 +1*X4 + 1*X5 + 1*X6
yir =~ 1*Y1 + 1*Y2 + 1*Y3 + 1*Y4 + 1*Y5 + 1*Y6
# crosslaged
Y2 ~ X1
X2 ~ Y1
Y3 ~ X2
X3 ~ Y2
Y4 ~ X3
X4 ~ Y3
Y5 ~ X4
X5 ~ Y4
Y6 ~ X5
X6 ~ Y5
# instrument
X1 + X2 + X3 + X4 + X5 + X6 ~ PGSx
Y1 + Y2 + Y3 + Y4 + Y5 + Y6 ~ PGSy
# innovations
innoX1 =~ 1*X1
innoX2 =~ 1*X2
innoX3 =~ 1*X3
innoX4 =~1*X4
innoX5 =~1*X5
innoX6 =~1*X6
innoY1 =~1*Y1
innoY2 =~1*Y2
innoY3 =~1*Y3
innoY4 =~1*Y4
innoY5 =~1*Y5
innoY6 =~1*Y6
X2 ~ innoX1
X3 ~ innoX2
X4 ~ innoX3
X5 ~ innoX4
X6 ~ innoX5
Y2 ~ innoY1
Y3 ~ innoY2
Y4 ~ innoY3
Y5 ~ innoY4
Y6 ~ innoY5
# # correlations
xir ~~ yir
#means
X1 ~ 1
X2 ~ 1
X3 ~ 1
X4 ~ 1
X5 ~ 1
Y1 ~ 1
Y2 ~ 1
Y3 ~ 1
Y4 ~ 1
Y5 ~ 1
# variances
innoX1 ~~ 1*innoX1
innoX2 ~~ 1*innoX2
innoX3 ~~ 1*innoX3
innoX4 ~~ 1*innoX4
innoX5 ~~ 1*innoX5
innoX6 ~~ 1*innoX6
innoY1 ~~ 1*innoY1
innoY2 ~~ 1*innoY2
innoY3 ~~ 1*innoY3
innoY4 ~~ 1*innoY4
innoY5 ~~ 1*innoY5
innoY6 ~~ 1*innoY6
"

id <- mxCheckIdentification(m1)
# Identified!


Now where it errs:


umxPower(m1, update = "Y2_to_X3", sig.level = 0.05, explore = T)


gives me:


Error incurred trying to run model: model = mxTryHard(model) might help?
The job for model 'drop_Y2_to_X3' exited abnormally with the error message: fit is not finite (The continuous part of the mode
l implied covariance (loc2) is not positive definite in data 'drop_Y2_to_X3.data' row 488. Detail:
covariance = matrix(c( # 16x16


and,


mxCompare(m1, mxTryHard(omxSetParameters(m1, labels=c('Y2_to_X3','X2_to_Y3'),
values= 0,
free= F),2))


Gives me:


All fit attempts resulted in errors - check starting values or model specification

Error in if (rfu == "r'Wr") { : argument is of length zero


P.S.:Is it possible to inline the image in this quesiton instead of using the attachment function?