[R] SEM model fit

[Cougar]

 I wonder if someone could explain why, when I perform confirmatory
factor-analysis model using polychoric correlations why I do not get an
estimated confidence interval for the RMSEA.  My experience with these type
models is that I would obtain a confidence interval estimate.  I did not get
any warning messages with the output.

RESULTS:

Model Chisquare =  1374   Df =  185 Pr(>Chisq) = 0
 Chisquare (null model) =  12284   Df =  210
 Goodness-of-fit index =  0.903
 Adjusted goodness-of-fit index =  0.88
 RMSEA index =  0.0711   90% CI: (NA, NA)
 Bentler-Bonnett NFI =  0.888
 Tucker-Lewis NNFI =  0.888
 Bentler CFI =  0.902
 SRMR =  0.0682
 BIC =  51.4 


SYNTAX

rm(sem.enf.rq)
mdl.rq <- specify.model()
enf                   -> law2,      NA,       1
enf                   -> law3,      lam2,     1
enf                   -> law4,      lam3,     1
enf                   <-> enf,      psi1,     0.6
law2                  <-> law2,     theta1,   0.3
law3                  <-> law3,     theta2,   0.3
law4                  <-> law4,     theta3,   0.5
gender                -> enf,       a1,       0.2
incomex               -> enf,       a2,       0.2
oftdrnkr              -> enf,       a3,       0.2
attn                  -> nvatt,     NA,       1
attn                  -> crimatt,   lam4,     1.3
attn                  -> asltatt,   lam5,     1.2
attn                  <-> attn,     psi2,     0.5
nvatt                 <-> nvatt,    theta4,   0.5
crimatt               <-> crimatt,  theta5,   0.1
asltatt               <-> asltatt,  theta6,   0.2
gender                -> attn,      a4,       0.2
acon                   -> acon1,    NA,       1
acon                   -> acon2,    lam4,     1.5
acon                   <-> acon,    psi2,     0.1
mcon                   -> mvcon1,   NA,       1
mcon                   -> mvcon2,   lam5,     1
mcon                   <-> mcon,    psi3,     0.3
ocon                   -> oicon1,   NA,       1
ocon                   -> oicon2,   lam6,     1
ocon                   <-> ocon,    psi4,     0.2
con                    -> acon,     NA,       1
con                    -> mcon,     lam7,     0.8
con                    -> ocon,     lam8,     0.9
con                   <-> con,     psi5,     0.3
acon1                 <-> acon1,   theta7,   0.4
acon2                 <-> acon2,   theta8,   0.2
mvcon1                <-> mvcon1,  theta9,   0.2
mvcon2                <-> mvcon2,  theta10,   0.3
oicon1                <-> oicon1,  theta11,   0.2
oicon2                <-> oicon2,  theta12,   0.3
gender                -> con,      a5,       0.1
incomex               -> con,      a6,       -0.1
oftdrnkr              -> con,      a7,       -0.2
attn                  -> con,      gam1,     0.2
sev                   -> aophys,   NA,        1
sev                   -> mvphys,   NA,        1
sev                   -> oiphys,   NA,        1
sev                   <-> sev,     psi6,      0.5
aophys                <-> aophys,  theta13,    0.5
mvphys                <-> mvphys,  theta14,    0.5
oiphys                <-> oiphys,  theta14,    0.5
con                   -> sev,      gam3,       0.8
prev                  -> mvpct,    NA,        1
prev                  -> oipct,    NA,        1
prev                  -> alcpct,   NA,        1
prev                  <-> prev,    psi8,      0.4
mvpct                 <-> mvpct,   theta15,    0.5
oipct                 <-> oipct,   theta15,    0.5
alcpct                <-> alcpct,  theta15,    0.5
con                   -> prev,     gam5,       0.8 
prev                  -> enf,      gam6,       0.4

sem.enf.rq <- sem(ram = mdl.rq, S = hcor(dx),  N = nrow(dx), obs.v =
names(dx), raw = F, fixed = names(dx)[4:6], par.size = 's', maxiter = 1e3,
analytic = F, gradtol = 1e-10)  ##set raw to False
summary(obj = sem.enf.rq, dig = 3, conf = 0.9) 

Respectfully,

Frank Lawrence