search for: bentler

...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 3.88384 0.293743 13.222 0 x1 <--> x2 x2...

...10 <-> 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 anyone help me understand what is happening here...

...th by using bootstrap estimations. But, I was wondering how the fit indexes in the SEM package were estimated when the observed variables are categorical (ordinal or/and binary) and when one is using polychoric correlation (hetcor in polycor package) in combination with ML. Does SEM use the Satorra-Bentler rescaled Chi-square for instance? I look in previous posts but couldn't find what i am looking for (hope i looked well enough!). Cheers, Dorothee -- View this message in context: http://www.nabble.com/Fit-indexes-in-SEM-with-categorical-data-%2B-ML-estimation-tp21782611p21782611.html Sent f...

...ce 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 goodness-of-fit index = 0.88 RMSEA index = 0.0711 90% CI: (NA, NA) Bentler-Bonnett NFI = 0.888 Tucker-Lewis NNFI = 0.888 Bentler CFI = 0.902 SRMR = 0.0682 BIC = 51.4 SYNTAX rm(sem.enf.rq) mdl.rq <- specify.model() enf -> law2, NA, 1 enf -> law3, lam2, 1 enf -> law4, lam...

...y specified target rotation pstQ oblique partially specified target rotation oblimax oblique entropy orthogonal minimum entropy quartimax orthogonal varimax orthogonal simplimax oblique bentlerT orthogonal Bentler's invariant pattern simplicity criterion bentlerQ oblique Bentler's invariant pattern simplicity criterion tandemI orthogonal Tandem Criterion tandemII orthogonal Tandem Criterion geominT orthogonal geo...

...y specified target rotation pstQ oblique partially specified target rotation oblimax oblique entropy orthogonal minimum entropy quartimax orthogonal varimax orthogonal simplimax oblique bentlerT orthogonal Bentler's invariant pattern simplicity criterion bentlerQ oblique Bentler's invariant pattern simplicity criterion tandemI orthogonal Tandem Criterion tandemII orthogonal Tandem Criterion geominT orthogonal geo...

...the multinormal distribution. Some researchers (e.g., Yuan et al., 2000) have proposed a multivariate transformation of the variables. Can you tell me, if and how such a transformation can be handeled in R? Thanks in advance. With best regards Holger --------------- Yuan, K.-H., Chan, W., & Bentler, P. M. (2000). Robust transformation with applications to structural equation modeling. British Journal of Mathematical and Statistical Psychology, 53, 31?50. -- View this message in context: http://www.nabble.com/Multivariate-Transformations-tp23739013p23739013.html Sent from the R help mailing...

...A, 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 Error z value Pr(>|z|) l11 -0.245981 0.218...

...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 Error z value Pr(>|z|) param1 0.78 0.073...

Hi list, I'm performing a series of confirmatory factor analysis on different groupings of items from data collected with questionnaires. There are some missing values. For those sets with no missing values I call factanal(datamatrix,factors=n) where datamatrix is a table of all observations for the items under investigation. This call fails when there are missing values. help(factanal)

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

...their models in a compact, elegant and useR-friendly way - lavaan is robust and reliable: there are no convergence problems and numerical results are very close (if not identical) to the commercial package Mplus - many different estimators are available: ML, GLS, WLS, robust ML using Satorra-Bentler corrections, and FIML for data with missing values - full support for meanstructures and multiple groups - user friendly output including standardized solutions, fit measures, modification indices and more To get a first impression of how the 'lavaan model syntax' looks like, below is...

...their models in a compact, elegant and useR-friendly way - lavaan is robust and reliable: there are no convergence problems and numerical results are very close (if not identical) to the commercial package Mplus - many different estimators are available: ML, GLS, WLS, robust ML using Satorra-Bentler corrections, and FIML for data with missing values - full support for meanstructures and multiple groups - user friendly output including standardized solutions, fit measures, modification indices and more To get a first impression of how the 'lavaan model syntax' looks like, below is...

#### I checked through every 3 factor * 3 loading case. #### While, 4 factor * 3 loading failed. #### the data is 6 factor * 3 loading require(sem); cor18<-read.moments(); 1 .68 1 .60 .58 1 .01 .10 .07 1 .12 .04 .06 .29 1 .06 .06 .01 .35 .24 1 .09 .13 .10 .05 .03 .07 1 .04 .08 .16 .10 .12 .06 .25 1 .06 .09 .02 .02 .09 .16 .29 .36 1 .23 .26 .19 .05 .04 .04 .08 .09 .09 1 .11 .13 .12 .03 .05 .03

I am a new user of the function sem in package sem and lavaan for structural equation modeling 1. I don?t know what is the difference between this function and CFA function, I know that cfa for confirmatory analysis but I don?t know what is the difference between confirmatory analysis and structural equation modeling in the package lavaan. 2. I have data that I want to analyse but I have some

...t and useR-friendly way > > - lavaan is robust and reliable: there are no convergence problems and > numerical results are very close (if not identical) to the commercial > package Mplus > > - many different estimators are available: ML, GLS, WLS, robust ML using > Satorra-Bentler corrections, and FIML for data with missing values > > - full support for meanstructures and multiple groups > > - user friendly output including standardized solutions, fit measures, > modification indices and more > > To get a first impression of how the 'lavaan model...

Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

...the multinormal distribution. Some researchers (e.g., Yuan et al., 2000) have proposed a multivariate transformation of the variables. Can you tell me, if and how such a transformation can be handeled in R? Thanks in advance. With best regards Holger --------------- Yuan, K.-H., Chan, W., & Bentler, P. M. (2000). Robust transformation with applications to structural equation modeling. British Journal of Mathematical and Statistical Psychology, 53, 31?50. -- View this message in context: http://www.nabble.com/Multivariate-Transformations-tp23739013p23739013.html Sent from the R help mailing...

...35: Read 34 records > summary(sem(mod4, cor18, 500)) Model Chisquare = 80.675 Df = 48 Pr(>Chisq) = 0.0021920 Chisquare (null model) = 1106.4 Df = 66 Goodness-of-fit index = 0.9747 Adjusted goodness-of-fit index = 0.95888 RMSEA index = 0.036935 90% CI: (0.022163, 0.050657) Bentler-Bonnett NFI = 0.92708 Tucker-Lewis NNFI = 0.95682 Bentler CFI = 0.9686 SRMR = 0.032512 BIC = -217.63 Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -1.71000 -0.23300 -0.00337 0.08850 0.26700 2.13000 Parameter Estimates Estimate Std Error z value Pr(&...