In specifying a CFA model using the sem package, I got 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.

 

This is the complete input (including data import):

 

----------------------------------------------------------------------------
-

DS =
read.table("http://www.beltz.de/fileadmin/beltz/downloads/OnlinematerialienP
VU/Statistik_und_Forschungsmethoden/Daten_kap23.dat", 

   sep="", header = F)

attach(DS)

 

mycov = cov(DS)

 

my.model <- specify.model()

   eta1 -> V1, NA,    1

   eta1 -> V2, lam12, NA

   eta1 -> V3, lam13, NA

   eta1 -> V4, lam14, NA

   eta1 -> V5, lam15, NA

   eta1 -> V6, lam16, NA

   eta2 -> V4, NA,    1

   eta2 -> V5, lam52, NA

   eta2 -> V6, lam62, NA

   V1  <-> V1, e1,   NA

   V2  <-> V2, e2,   NA

   V3  <-> V3, e3,   NA

   V4  <-> V4, e4,   NA

   V5  <-> V5, e5,   NA

   V6  <-> V6, e6,   NA

   eta1 <-> eta1, var.eta1, NA

   eta2 <-> eta2, var.eta2, NA

 

my.sem <- sem(my.model, mycov, nrow(DS), debug=TRUE) 

 

----------------------------------------------------------------------------
-

 

The problem should converge easily and does so in Mplus. Also, it converges
if the correlation matrix instead of the variance-covariance matrix is used
and gives the correct standardized coefficients. Any suggestions why it does
not work with the covariance matrix in my example?

 

 

 


	[[alternative HTML version deleted]]

