[R] SEM model fit

From: Cougar <Cougar_711_at_msn.com>
Date: Wed, 27 Jun 2007 16:38:31 -0400

 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



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