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
Results of CFA with Lavaan
11 messages · R Help, Jeremy Miles, John Fox +1 more
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.
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? 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. 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
______________________________________________ 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,
-----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
______________________________________________ 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.
______________________________________________ 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.
On 06/08/2011 11:56 PM, R Help wrote:
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?
If you have a latent variable in your model (like a factor in CFA), you need to define its metric/scale. There are typically two ways to do this: 1) fix the variance of the latent variable to a constant (typically 1.0), or 2) fix the factor loading of one of the indicators of the factor (again to 1.0). For CFA with a single group, it should not matter which method you choose. The fit measures will be identical. Lavaan by default uses the second option. If you prefer the first (fixing the variances), you can simply add the 'std.lv=TRUE' option to the cfa() call, and lavaan will take care of the rest.
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.
You can use the 'verbose=TRUE' argument to monitor progress. You may also use the options se="none" (no standard errors) and test="none" (no test statistic) to speed things up, if you are still constructing your model. Or the model does not convergence, but I should see both the model and the data to determine the possible cause. Hope this helps, Yves Rosseel http://lavaan.org
Thanks for the help, the std.lv=TRUE command is exactly what I was looking for. As you stated, it doesn't matter in terms of overall model fit, but my client is more interested in the loadings than the factor variances. In terms of speed, it's just a very large model (7 factors, 90 observations, only ~560 subjects) with missing values, so I don't expect much in terms of speed. I think the overall conclusion for the project is that the model is poorly specified, but whether that's the model itself or the lack of samples is difficult to determine at this point. Thanks for your help, and I'll certainly be using lavaan in the future, Sam
On Thu, Jun 9, 2011 at 6:19 AM, yrosseel <yrosseel at gmail.com> wrote:
On 06/08/2011 11:56 PM, R Help wrote:
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?
If you have a latent variable in your model (like a factor in CFA), you need to define its metric/scale. There are typically two ways to do this: 1) fix the variance of the latent variable to a constant (typically 1.0), or 2) fix the factor loading of one of the indicators of the factor (again to 1.0). For CFA with a single group, it should not matter which method you choose. The fit measures will be identical. Lavaan by default uses the second option. If you prefer the first (fixing the variances), you can simply add the 'std.lv=TRUE' option to the cfa() call, and lavaan will take care of the rest.
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.
You can use the 'verbose=TRUE' argument to monitor progress. You may also use the options se="none" (no standard errors) and test="none" (no test statistic) to speed things up, if you are still constructing your model. Or the model does not convergence, but I should see both the model and the data to determine the possible cause. Hope this helps, Yves Rosseel http://lavaan.org
______________________________________________ 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.
Ok, I think this is the last question I have. My model is producing an estimate of intercepts for my variables along with my loadings.
From the documentation it appears that this is controlled by the
meanstructure option in cfa. It says that setting it to TRUE includes the intercepts, and setting it to "default" means thatthe value is set based on the user-specified model, and/or the values of other arguments. I've included my model specification below, and I would prefer not to fit intercepts, but setting it to FALSE does not seem to achieve this. Thanks, Sam F1 =~ reFDE + ReFUIDGreg + reFDRwithDDRV + reparD + reparDR + reparRisk + reWDD + reWDH + reWSP + reWDIS + reWCell + reWFAT + reAanx + reDanx + reDstress + reAstress F2 =~ reSI1 + reSI2 + reSI3 + reSI4 + reSimDE + reSimDD + reSimDrug + reSimDRD F3 =~ RENOINTEND + RETRYNOTD + RENOSTARTD + REUSEDD + REWILLD1 + REDU1 + REDA1 + RERIDE1 + REAFTER1 + REUSEC1 + REUSESP1 + REUM1 + REABUSE1 + RESB1 + REMIGHT1 F4 =~ retrydrink + RetryDope + reNoD + reLeaveD + reDeDR + reDopeNo + reDopeleave + reDopeDD + reP3D F5 =~ reP3DA + reP3DD + reP3DRD + reP3Equip + reP3UC + reP3SP + reP3UM + reP3Abuse + reP3SB + reP3helmet + reP1DADR + reP1DRUG + reP1SP F6 =~ reinjwhileDU + reinjwhileWDUDRV + reinjwhileDA + reinjwhileDRafterD + reinjwhileUcrack + reinjwhileUM + reinjwhileabusePRDG + reinjwhilenoSB + reinjwhilenohelmet F7 =~ relikeDR + relikeSP + relikeDIS + relikeCELL + relikeDROW + relikeDRUG + restupid + reimmature + takerisksFthinkcool + takeriskFthinkIMP + takeriskFthinkbrave + takeriskFthinkexciting + reSELF + reNORISK + reNOPERSON + reNOCONSE + reWRONG + reGEAR + reCONSEQ + reSUCES
On Thu, Jun 9, 2011 at 6:19 AM, yrosseel <yrosseel at gmail.com> wrote:
On 06/08/2011 11:56 PM, R Help wrote:
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?
If you have a latent variable in your model (like a factor in CFA), you need to define its metric/scale. There are typically two ways to do this: 1) fix the variance of the latent variable to a constant (typically 1.0), or 2) fix the factor loading of one of the indicators of the factor (again to 1.0). For CFA with a single group, it should not matter which method you choose. The fit measures will be identical. Lavaan by default uses the second option. If you prefer the first (fixing the variances), you can simply add the 'std.lv=TRUE' option to the cfa() call, and lavaan will take care of the rest.
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.
You can use the 'verbose=TRUE' argument to monitor progress. You may also use the options se="none" (no standard errors) and test="none" (no test statistic) to speed things up, if you are still constructing your model. Or the model does not convergence, but I should see both the model and the data to determine the possible cause. Hope this helps, Yves Rosseel http://lavaan.org
______________________________________________ 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.
On 06/09/2011 05:21 PM, R Help wrote:
Ok, I think this is the last question I have. My model is producing an estimate of intercepts for my variables along with my loadings. From the documentation it appears that this is controlled by the meanstructure option in cfa. It says that setting it to TRUE includes the intercepts, and setting it to "default" means thatthe value is set based on the user-specified model, and/or the values of other arguments. I've included my model specification below, and I would prefer not to fit intercepts, but setting it to FALSE does not seem to achieve this.
Several arguments of the cfa() function force meanstructure=TRUE (and indeed, silently overriding the meanstructure=FALSE option if specified by the user; perhaps, lavaan should spit out a warning if this happens). The following argument choices force meanstructure to be TRUE (if there is only a single group): - estimator = "mlm" or "mlf" or "mlr" - missing = "ml" or "fiml" Did you use any one of those arguments? But why would you prefer not to fit the intercepts? If there are no restrictions on the intercepts/means, fitting them should have no effect on your model fit whatsoever. Yves Rosseel http://lavaan.org
I am using missing = 'fiml', which would require estimating intercepts. I figured they would effect my overall model fit, but can I still estimate my loading coefficients the same way? The warning would be helpful, but if I had looked closer into the 'fiml' option I might have been able to figure it out myself. Thanks, Sam
On Thu, Jun 9, 2011 at 1:02 PM, yrosseel <yrosseel at gmail.com> wrote:
On 06/09/2011 05:21 PM, R Help wrote:
Ok, I think this is the last question I have. ?My model is producing an estimate of intercepts for my variables along with my loadings. ?From the documentation it appears that this is controlled by the meanstructure ? option in cfa. ?It says that setting it to TRUE includes the intercepts, and setting it to "default" means thatthe value is set based on the user-specified model, and/or the values of other arguments. ?I've included my model specification below, and I would prefer not to fit intercepts, but setting it to FALSE does not seem to achieve this.
Several arguments of the cfa() function force meanstructure=TRUE (and indeed, silently overriding the meanstructure=FALSE option if specified by the user; perhaps, lavaan should spit out a warning if this happens). The following argument choices force meanstructure to be TRUE (if there is only a single group): - estimator = "mlm" or "mlf" or "mlr" - missing = "ml" or "fiml" Did you use any one of those arguments? But why would you prefer not to fit the intercepts? If there are no restrictions on the intercepts/means, fitting them should have no effect on your model fit whatsoever. Yves Rosseel http://lavaan.org
On 06/09/2011 06:06 PM, R Help wrote:
I am using missing = 'fiml', which would require estimating intercepts. I figured they would effect my overall model fit, but can I still estimate my loading coefficients the same way?
Yes, no problem. Yves Rosseel http://lavaan.org