similar to: confirmatory factor analysis in R

Does someone have confirmatory factor analysis program in R,which includes factor loading and some tests?thank you! -- View this message in context: http://r.789695.n4.nabble.com/confirmatory-factor-analysis-program-in-R-tp3386133p3386133.html Sent from the R help mailing list archive at Nabble.com.

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]]

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

Hi Has anyone used R to conduct confirmatory factor analysis? This email pertains to use of SEM. For context consider an example: the basic idea is that there are a bunch of observables variables (say study habbits, amount of time reading in the bus, doing homework, helping other do homework, doing follow-up on errors etc.) and one believes that all these variables maybe measured by two or

Hi: I am doing a confirmatory factor analysis now. In the analysis, I have null hypothesis test which specify some special structure for the loading matrix. And the alternative is there is no such special structure. Then the log likelihood ratio test can be used. The problem I have is my data comes from a questionnaire and all the variables are discrete from 1 to 7, which makes the normality

Hello all, I'm trying to replicate a confirmatory factor analysis done in Amos. The idea is to compare a one-factor and a two-factor model. I get the following warning message when I run either model: "Could not compute QR decomposition of Hessian. Optimization probably did not converge." I have no idea what to do here. I believe posters reported the same problem. It seems

Is there a way to use full information maximum likelihood (FIML) to estimate missing data in the sem package? For example, suppose I have a dataset with complete information on X1-X3, but missing data (MAR) on X4. Is there a way to use FIML in this case? I know lavaan and openmx allow you to do it, but I couldn't find anything in the documentation for the sem package. Thanks! -- Dustin Fife

Dear R-List, (I am not sure whether this list is the right place for my question...) I have a dataframe df.cfa

Hello, I'm trying to fit a pretty simple confirmatory factor analysis using the sem package. There's a CFA example in the examples, which is helpful, but the output for my (failing) model is hard to understand. I'd be interested in any other ways to do a CFA in R, if this proves troublesome. The CFA is replicating a 5 uncorrelated-factor structure (for those interested, it is a

Hi. which package i need to install to be able to run "Path analysis" using r? many thanks, Guy -- Guy Rotem Department of Life Sciences The Spatial Ecology Lab Ben Gurion University of the Negev P.O.B. 653 Beer-Sheva 84105 ISRAEL +972-52-3354485 (mobile) +972-8-6461350 (lab) [[alternative HTML version deleted]]

Hi, I recently tried to estimate a linear unconditional latent growth curve on 7 repeated measures using lavaan (most recent version): modspec=' alpha =~ 1*read_g0 + 1*read_g1 + 1*read_g2 + 1*read_g3 + 1*read_g4 + 1*read_g5 + 1*read_g6 beta =~ 0*read_g0 + 1*read_g1 + 2*read_g2 + 3*read_g3 + 4*read_g4 + 5*read_g5 + 6*read_g6 ' gmod=lavaan(modspec, data=math, meanstructure=T,

Hi, I am wondering if anyone knows of an easy way to fit a saturated model using the sem package on raw data? Say the data were: mtcars[, c("mpg", "hp", "wt")] The model would estimate the three means (intercepts) of c("mpg", "hp", "wt"). The variances of c("mpg", "hp", "wt"). The covariance of mpg with

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

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 Yves lavaan looks like a very nice package. From the tutorial introduction I see you create path diagrams for some of the models you describe. How did you do this? I don't see a function for this in the package. I know there is a path.diagram function in the sem package that uses dot to draw the diagram, but I've always found the layouts from dot somewhat strange for path diagrams

I have friendship data which is strong skewed. So it doesn't make sense to use maximum likelihood methods for fa and cfa. But I couldn't find any function for asymptotically distributed data for doing a factor analysis. Only: apca() but there is no possibility to allow for factor correlations. The same problem is with sem() I couldn't get any solutions for my model because of the

I want estimate MIMIC latent variable with R in a Monte Carlo simulation. The packages plspm and semPLS don't permit to introduce MIMIC variable but only reflexives or formatives variables. The only one program which permits to use MIMIC latent variable with PLSPM seems to be XLSTAT, which can not be used to simulate a lot of data bases. It is a real challenge to develop a package with

I am the guy who compiles the OpenMx binaries. We would be delighted to place our package on CRAN, once the project is stable enough so that we are comfortable releasing it to the larger public. Let's try to track down where I made a mistake. Our Makevars.in file contains the line: PKG_LIBS=$(FLIBS) $(BLAS_LIBS) $(LAPACK_LIBS) In addition, on the build machine I noticed that a copy of

Full_Name: Timothy Brick Version: 2.10 OS: Mac OS X (seen on both 10.6 and 10.5) Submission from: (NULL) (63.255.24.5) When using install.packages in R on Mac OS X, packages that require gfortran throws an error (Example below from installation of OpenMx package): Loading required package: OpenMx Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library

I'm contemplating doing Bayesian estimation in R but am wondering how Bayesian estimation on R stacks up against BUGS. Is R reasonably complete, more difficult to use,...? Anyone care to share their experience? Also, am I right to conclude that covariance structure analysis & confirmatory factor analysis are not available in R? (I can't find them with help) Peter