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-boun...@r-project.org [mailto:r-help-boun...@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@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. ______________________________________________ R-help@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.
