[R] Results of CFA with Lavaan
John Fox
jfox at mcmaster.ca
Thu Jun 9 00:45:00 CEST 2011
Dear Sam,
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On Behalf Of R Help
> Sent: June-08-11 5:57 PM
> To: John Fox
> Cc: r-help
> Subject: Re: [R] Results of CFA with Lavaan
>
> Yes, that is the difference. For the last SEM I built I fixed the
> factor variances to 1, and I think that's what I want to do for the CFA
> I'm doing now. Does that make sense for a CFA?
Sure -- then the factor covariances are correlations. The point is that you
have to do something to fix the metrics of the factors and identify the
model.
>
> I'll try figuring out how to do that with lavaan later, but my model
> takes so long to fit that I can't try it right now.
Maybe that should tell you something about the conditioning of the problem.
Best,
John
>
> Thanks,
> Sam
>
> On Wed, Jun 8, 2011 at 5:58 PM, John Fox <jfox at mcmaster.ca> wrote:
> > Dear Sam,
> >
> > In each case, the first observed variable is treated as a "reference
> > indicator" with its coefficient fixed to 1 to establish the metric of
> > the corresponding factor and therefore to identify the model. If you
> > didn't do the same thing (or something equivalent, such as fixing the
> > factor variances to 1) in specifying the model to sem::sem(), that
> > might account for the problems you encountered.
> >
> > Best,
> > John
> >
> > --------------------------------
> > John Fox
> > Senator William McMaster
> > Professor of Social Statistics
> > Department of Sociology
> > McMaster University
> > Hamilton, Ontario, Canada
> > http://socserv.mcmaster.ca/jfox
> >
> >
> >
> >> -----Original Message-----
> >> From: r-help-bounces at r-project.org
> >> [mailto:r-help-bounces at r-project.org]
> >> On Behalf Of R Help
> >> Sent: June-08-11 4:15 PM
> >> To: r-help
> >> Subject: [R] Results of CFA with Lavaan
> >>
> >> I've just found the lavaan package, and I really appreciate it, as it
> >> seems to succeed with models that were failing in sem::sem. I need
> >> some clarification, however, in the output, and I was hoping the list
> >> could help me.
> >>
> >> I'll go with the standard example from the help documentation, as my
> >> problem is much larger but no more complicated than that.
> >>
> >> My question is, why is there one latent estimate that is set to 1
> >> with no SD for each factor? Is that normal? When I've managed to
> >> get sem::sem to fit a model this has not been the case.
> >>
> >> Thanks,
> >> Sam Stewart
> >>
> >> HS.model <- ' visual =~ x1 + x2 + x3
> >> textual =~ x4 + x5 + x6
> >> speed =~ x7 + x8 + x9 '
> >> fit <- sem(HS.model, data=HolzingerSwineford1939) summary(fit,
> >> fit.measures=TRUE) Lavaan (0.4-8) converged normally after 35
> >> iterations
> >>
> >> Number of observations 301
> >>
> >> Estimator ML
> >> Minimum Function Chi-square 85.306
> >> Degrees of freedom 24
> >> P-value 0.000
> >>
> >> Chi-square test baseline model:
> >>
> >> Minimum Function Chi-square 918.852
> >> Degrees of freedom 36
> >> P-value 0.000
> >>
> >> Full model versus baseline model:
> >>
> >> Comparative Fit Index (CFI) 0.931
> >> Tucker-Lewis Index (TLI) 0.896
> >>
> >> Loglikelihood and Information Criteria:
> >>
> >> Loglikelihood user model (H0) -3737.745
> >> Loglikelihood unrestricted model (H1) -3695.092
> >>
> >> Number of free parameters 21
> >> Akaike (AIC) 7517.490
> >> Bayesian (BIC) 7595.339
> >> Sample-size adjusted Bayesian (BIC) 7528.739
> >>
> >> Root Mean Square Error of Approximation:
> >>
> >> RMSEA 0.092
> >> 90 Percent Confidence Interval 0.071 0.114
> >> P-value RMSEA <= 0.05 0.001
> >>
> >> Standardized Root Mean Square Residual:
> >>
> >> SRMR 0.065
> >>
> >> Parameter estimates:
> >>
> >> Information Expected
> >> Standard Errors Standard
> >>
> >>
> >> Estimate Std.err Z-value P(>|z|) Latent
> variables:
> >> visual =~
> >> x1 1.000
> >> x2 0.554 0.100 5.554 0.000
> >> x3 0.729 0.109 6.685 0.000
> >> textual =~
> >> x4 1.000
> >> x5 1.113 0.065 17.014 0.000
> >> x6 0.926 0.055 16.703 0.000
> >> speed =~
> >> x7 1.000
> >> x8 1.180 0.165 7.152 0.000
> >> x9 1.082 0.151 7.155 0.000
> >>
> >> Covariances:
> >> visual ~~
> >> textual 0.408 0.074 5.552 0.000
> >> speed 0.262 0.056 4.660 0.000
> >> textual ~~
> >> speed 0.173 0.049 3.518 0.000
> >>
> >> Variances:
> >> x1 0.549 0.114 4.833 0.000
> >> x2 1.134 0.102 11.146 0.000
> >> x3 0.844 0.091 9.317 0.000
> >> x4 0.371 0.048 7.778 0.000
> >> x5 0.446 0.058 7.642 0.000
> >> x6 0.356 0.043 8.277 0.000
> >> x7 0.799 0.081 9.823 0.000
> >> x8 0.488 0.074 6.573 0.000
> >> x9 0.566 0.071 8.003 0.000
> >> visual 0.809 0.145 5.564 0.000
> >> textual 0.979 0.112 8.737 0.000
> >> speed 0.384 0.086 4.451 0.000
> >>
> >> ______________________________________________
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> >
> >
>
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