Hi,
I created the model below, which returns me the following warning message:
In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names =
vars, :
Could not compute QR decomposition of Hessian.
Optimization probably did not converge.
######### Model ########
mDPDF =
data.frame(mj1,mj2,mj3,mj4,mj5,eL1,eL2,eL3,eL4,eL5,aC1,aC2,aC3,aC4,disR1,disR2,disR3,disR4,disR5,
difR1,difR2,difR3,difR4,difR5,difR6,dC1,dC2,dC3,dC4,aB1,aB2,aB3,aB4,aB5,deh1,deh2,deh3,deh4)
mydata.cov <- cov(mDPDF)
model.mydata <- specify.model()
MJ -> mj1, NA, 1
MJ -> mj2, lam2, NA
MJ -> mj3, lam3, NA
MJ -> mj4, lam4, NA
MJ -> mj5, lam5, NA
EL -> eL1, NA, 1
EL -> eL2, lam7, NA
EL -> eL3, lam8, NA
EL -> eL4, lam9, NA
EL -> eL5, lam10, NA
AC -> aC1, NA, 1
AC -> aC2, lam12, NA
AC -> aC3, lam13, NA
AC -> aC4, lam14, NA
DISR -> disR1, NA, 1
DISR -> disR2, lam16, NA
DISR -> disR3, lam17, NA
DISR -> disR4, lam18, NA
DISR -> disR5, lam19, NA
DIFR -> difR1, NA, 1
DIFR -> difR2, lam21, NA
DIFR -> difR3, lam22, NA
DIFR -> difR4, lam23, NA
DIFR -> difR5, lam24, NA
DIFR -> difR6, lam25, NA
DC -> dC1, NA, 1
DC -> dC2, lam27, NA
DC -> dC3, lam28, NA
DC -> dC4, lam29, NA
AB -> aB1, NA, 1
AB -> aB2, lam31, NA
AB -> aB3, lam32, NA
AB -> aB4, lam33, NA
AB -> aB5, lam34, NA
DEH -> deh1, NA, 1
DEH -> deh2, lam36, NA
DEH -> deh3, lam37, NA
DEH -> deh4, lam38, NA
mj1 <-> mj1, e1, NA
mj2 <-> mj2, e2, NA
mj3 <-> mj3, e3, NA
mj4 <-> mj4, e4, NA
mj5 <-> mj5, e5, NA
eL1 <-> eL1, e6, NA
eL2 <-> eL2, e7, NA
eL3 <-> eL3, e8, NA
eL4 <-> eL4, e9, NA
eL5 <-> eL5, e10, NA
aC1 <-> aC1, e11, NA
aC2 <-> aC2, e12, NA
aC3 <-> aC3, e13, NA
aC4 <-> aC4, e14, NA
disR1 <-> disR1, e15, NA
disR2 <-> disR2, e16, NA
disR3 <-> disR3, e17, NA
disR4 <-> disR4, e18, NA
disR5 <-> disR5, e19, NA
difR1 <-> difR1, e20, NA
difR2 <-> difR2, e21, NA
difR3 <-> difR3, e22, NA
difR4 <-> difR4, e23, NA
difR5 <-> difR5, e24, NA
difR6 <-> difR6, e25, NA
dC1 <-> dC1, e26, NA
dC2 <-> dC2, e27, NA
dC3 <-> dC3, e28, NA
dC4 <-> dC4, e29, NA
aB1 <-> aB1, e30, NA
aB2 <-> aB2, e31, NA
aB3 <-> aB3, e32, NA
aB4 <-> aB4, e33, NA
aB5 <-> aB5, e34, NA
deh1 <-> deh1, e35, NA
deh2 <-> deh2, e36, NA
deh3 <-> deh3, e37, NA
deh4 <-> deh4, e38, NA
MJ <-> MJ, NA, 1
EL <-> EL, NA, 1
AC <-> AC, NA, 1
DISR <-> DISR, NA, 1
DIFR <-> DIFR, NA, 1
DC <-> DC, NA, 1
AB <-> AB, NA, 1
DEH <-> DEH, NA, 1
MJ <-> EL, MJEL, NA
MJ <-> AC, MJAC, NA
MJ <-> DISR, MJDISR , NA
MJ <-> DIFR, MJDIFR , NA
MJ <-> DC, MJDC , NA
MJ <-> AB, MJAB , NA
MJ <-> DEH, MJDEH , NA
EL <-> AC, ELAC , NA
EL <-> DISR, ELDISR , NA
EL <-> DIFR, ELDIFR , NA
EL <-> DC, ELDC , NA
EL <-> AB, ELAB , NA
EL <-> DEH, ELDEH , NA
AC <-> DISR, ACDISR , NA
AC <-> DIFR, ACDIFR , NA
AC <-> DC, ACDC , NA
AC <-> AB, ACAB , NA
AC <-> DEH, ACDEH , NA
DISR <-> DISR, DISRDISR , NA
DISR <-> DC, DISRDC , NA
DISR <-> AB, DISRAB , NA
DISR <-> DEH, DISRDEH , NA
DIFR <-> DC, DIFRDC , NA
DIFR <-> AB, DIFRAB , NA
DIFR <-> DEH, DIFRDEH , NA
DC <-> AB, DCAB , NA
DC <-> DEH, DCDEH , NA
AB <-> DEH, ABDEH , NA
mydata.sem <- sem(model.mydata, mydata.cov, nrow(mDPDF))
######### Model ########
In addition to this model, I created a number of subset models (i.e.,
models with fewer latent variables, but otherwise everything the same) some
of which do not give me the warning message as well as a model where none
of the exogenous variables' variances are fixed, but I still obtained the
same message.
I'm wondering if there is anything I can do to address this problem.
Best,
~Kino
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