search for: rmsea

...4,NA xi1 <-> xi1,NA,1 xi2 <-> xi2,NA,1 xi3 <-> xi3,NA,1 xi4 <-> xi4,NA,1 xi1 <-> xi2 ,PH12,NA xi1 <-> xi3 ,PH13,NA xi2 <-> xi3 ,PH23,NA xi4 <-> xi1,PH41,NA xi4 <-> xi2,PH42,NA xi4 <-> xi3,PH43,NA summary(sem(mod3.1_9,cor18,500))$RMSEA; summary(sem(mod3.1_6AND10_12,cor18,500))$RMSEA; summary(sem(mod3.1_3AND7_9AND10_12,cor18,500))$RMSEA; summary(sem(mod3.4_6AND7_9AND10_12,cor18,500))$RMSEA; summary(sem(mod4,cor18,500))$RMSEA;##fail [[alternative HTML version deleted]]

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...

...tricted 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...

Hello useRs, I`m running structural equation models with R, but for one of my models the below error message apears. I`m trying to change startvalues but without success. The manual for sem package did not help me. Does anyone knows how to change startvalues for iteration in sem package? Or it can be another problem with the model? Error in startvalues(S, ram, debug = debug, tol = start.tol)

Hi, I'm trying to perform a hierachical, second order CFA. That's the thing that I need to leave AMOS. I found some sim.hierarchical and omega stuff, but nothing clear enough. Can anyone help me? I just need a simple and clear manual. Best, Pablo. [[alternative HTML version deleted]]

...using SEM package in R, which was fit to a raw moment matrix, and includes an intercept term. The only goodness of fit statistics that are output are Model Chisquare, AIC, AICc, BIC, CAIC, and normalized residuals. How can I get the other goodness of fit statistics, like adjusted goodness of fit, RMSEA, and R-squared? And how can I get the final value of the log-likelihood of the model? Thanks, Maya [[alternative HTML version deleted]]

...is the code that I have, but it is giving me an error message "Warning message: In eval(expr, envir, enclos) : Negative parameter variances. Model may be underidentified." and a further error "Error in summary.objectiveML(cfa, fit.indices = c("NNFI", "CFI", "RMSEA")) : coefficient covariances cannot be computed". I have run CFA before with no issues. This is the first time I am running a nested model. Any help will be greatly appreciated. Regards, Mat cov.matrix<-cov(na.omit(df)) cfa.model<-specifyModel() F1->i2,a1 F1->i3,a2 F1->i4,a...

...sq) dfM1 <- as.numeric(myModlChiM1$df) chiM0 <- as.numeric(myModlChiM1$chisqNull) dfM0 <- as.numeric(myModlChiM1$dfNull) GFIM1 <- as.numeric(myModlChiM1$GFI) AGFIM1 <- as.numeric(myModlChiM1$AGFI) RMSEAM1 <- as.numeric(myModlChiM1$RMSEA[1]) CFIM1 <- as.numeric(myModlChiM1$CFI) BICM1 <- as.numeric(myModlChiM1$BIC) SRMRM1 <- as.numeric(myModlChiM1$SRMR) iterM1 <- as.numeric(myModlChiM1$iterations) resultsM1...

...1 x2 -> y1, x2-y1 summary(sem(path.model, cov.matrix, N = 422)) and I get following results; Model Chisquare = 12.524 Df = 1 Pr(>Chisq) = 0.00040179 Chisquare (null model) = 812.69 Df = 3 Goodness-of-fit index = 0.98083 Adjusted goodness-of-fit index = 0.885 RMSEA index = 0.16545 90% CI: (0.09231, 0.25264) Bentler-Bonnett NFI = 0.98459 Tucker-Lewis NNFI = 0.9573 Bentler CFI = 0.98577 SRMR = 0.027022 BIC = 6.4789 Parameter Estimates Estimate Std Error z value Pr(>|z|) x1-y1 -0.67833 0.033967 -19.970 0 y1 <--- x1 x2-x1...

...EMC5 EMC6 EMC7 EMC8 0.3622224 0.2350041 0.2488009 0.2901653 0.3195399 0.3107343 0.3436622 0.2345912 EMC9 EMC10 EMC11 EMC12 EMC13 EMC14 EMC15 EMC16 0.2621680 0.3230400 0.4039245 0.3803105 0.2773370 0.4348342 0.2757216 0.3405252 The fit indices of RMSEA and GFI are good, so I think the problem might be solve by another way for computing the Chi-square of null model. I'm not well trained in maths, so I come for help. Any advise is appreciated. Best wishes, Wei-Wei

...RAM specification(i.e., delete "LOCUS10 <-> FARSCH, x2x1, NA"), I DO get all the usual statistics: Model Chisquare = 1303.7 Df = 1 Pr(>Chisq) = 0 Chisquare (null model) = 8526.8 Df = 6 Goodness-of-fit index = 0.95864 Adjusted goodness-of-fit index = 0.58639 RMSEA index = 0.30029 90% CI: (NA, NA) Bentler-Bonnett NFI = 0.84711 Tucker-Lewis NNFI = 0.082726 Bentler CFI = 0.84712 BIC = 1294.1 My understanding is the you should always put in the correlation between exogenous predictors, but when I do this I don't get fit statistics. Can an...

Hello all! I want to estimate parameters in a MIMIC model. I have one latent variable (ksi), four reflexive indicators (y1, y2, y3 and y4) and four formative indicators (x1, x2, x3, x4). Is there a way to do it in R? I know there is the SEM library, but it seems not to be possible to specify formative indicators, that is, observed exogenous variables which causes the latent variable. Thanks,

...sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Obtained summary: summary(model.3eq.33.estim) Model fit to raw moment matrix. Model Chisquare = 398.81 Df = 24 Pr(>Chisq) = 0 Goodness-of-fit index = 0.944 Adjusted goodness-of-fit index = 0.60099 RMSEA index = 0.15923 90 % CI: (0.14569, 0.17316) BIC = 175.29 Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -0.9120 0.0000 0.0000 0.0268 0.0000 1.5600 Parameter Estimate Std Error z value Pr(>|z|) beta13...

...-square statistic for the model. However, when I use the summary() function, the model does not provide indices of fit; I need these as part of my analysis output. In particular, I am looking for the Tucker Lewis Index (TLI), Comparative Fit Index (CFI), & the Root Mean Square of Approximation (RMSEA). I have checked the documentation and cannot seem to find any reason for this; none of the arguments listed with the sem command indicate that I have to specify these as part of the output. In addition, the analysis example from the video includes these indices as part of the output, but my analys...

...X4 <-> X4, NA, 1 X5 <-> X5, NA, 1 X6 <-> X6, NA, 1 This at least converges: > summary(fit.sem) Model Chisquare = 2147 Df = 10 Pr(>Chisq) = 0 Chisquare (null model) = 2934 Df = 15 Goodness-of-fit index = 0.4822 Adjusted goodness-of-fit index = -0.087387 RMSEA index = 0.66107 90 % CI: (NA, NA) Bentler-Bonnett NFI = 0.26823 Tucker-Lewis NNFI = -0.098156 Bentler CFI = 0.26790 BIC = 2085.1 Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -5.990 -0.618 0.192 0.165 1.700 3.950 Parameter Estimates Estimate Std...

...w). Does anybody know how to standardized the residual variance ? Sincerely yours, > summary(semModif.tmp, digits=2) Model Chisquare = 601 Df = 229 Pr(>Chisq) = 0 Chisquare (null model) = 2936 Df = 253 Goodness-of-fit index = 0.81 Adjusted goodness-of-fit index = 0.78 RMSEA index = 0.08 90% CI: (0.072, 0.088) Bentler-Bonnett NFI = 0.8 Tucker-Lewis NNFI = 0.85 Bentler CFI = 0.86 BIC = -667 Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -2.6300 -0.5640 -0.0728 -0.0067 0.5530 3.5500 Parameter Estimates Estimate Std E...

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Hi. I have tried to use Package "SEM". As a learning, I try to convert a program running well of EQS which is as follows to SEM: ### EQS ### /SPECIFICATION CAS=100; VAR=5 MAT=COR; ANA=COR; /EQUATIONS V1=*F1+E1; V2=*F1+E2; V3=*F1+*F2+E3; V4=**F1+*F2*E4; V5=*F2+E5; /VAR E1 TO E5=*; F1*1.0; F2=1.0; /COV E1,E2=*; F1,F2=*: /PRINT FIT ALL; /MATRIX ...... /END This is the converted SEM

Hello All, Recently, I was asked to help out with an SEM cross-validation analysis. Initially, the project was based on "sample-splitting" where half of cases were randomly assigned to a training sample and half to a testing sample. Attempts to replicate a model developed in the training sample using the testing sample were not entirely successful. A number of parameter estimates were

...4,NA xi1 <-> xi1,NA,1 xi2 <-> xi2,NA,1 xi3 <-> xi3,NA,1 xi4 <-> xi4,NA,1 xi1 <-> xi2 ,PH12,NA xi1 <-> xi3 ,PH13,NA xi2 <-> xi3 ,PH23,NA xi4 <-> xi1,PH41,NA xi4 <-> xi2,PH42,NA xi4 <-> xi3,PH43,NA summary(sem(mod3.1_9,cor18,500))$RMSEA; summary(sem(mod3.1_6AND10_12,cor18,500))$RMSEA; summary(sem(mod3.1_3AND7_9AND10_12,cor18,500))$RMSEA; summary(sem(mod3.4_6AND7_9AND10_12,cor18,500))$RMSEA; summary(sem(mod4,cor18,500))$RMSEA;##fail [[alternative HTML version deleted]] ------------------------------ Message: 30 Date: Mon, 22...