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
What do you mean by latent estimate? The table of variances has variances for each factors. Is there something different in the sem output that you don't see here? Yes, this looks normal. Jeremy On 8 June 2011 13:14, R Help <rhelp.stats at gmail.com> wrote:> 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 > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
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 > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code.