similar to: error in model specification for cfa with lavaan-package

Hi Sorry for the rather long message. I am trying to use the cfa command in the lavaan package to run a CFA however I am unsure over a couple of issues. I have @25 dichotomous variables, 300 observations and an EFA on a training dataset suggests a 3 factor model. After defining the model I use the command fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",

Hello, I am trying to do a multiple groups CFA in lavaan and I get the following error message: Error in cov(data.obs, use = "pairwise") : 'x' is empty I'm not sure what this message is referring to, can anyone help me? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Multiple-Groups-CFA-in-Lavaan-tp3457971p3457971.html Sent from the R help mailing

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

Dear all, I am trying to learn about R. As a Phd student, I would like to use R for Krippendorff s alpha and later for a series of CFA. Is there any good introduction to these kind of analyses, or could you recommend me some tutorials on youtube, for example? I am new to R, so I need something basic , starting from opening the R environment?. Thank you . Martina Hulesova Odesl?no z m?ho Windows

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

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 don't know the right computer science words for this question, I'm afraid. Apology in advance. How do you find your way around in somebody else's code? If the user runs a specific command, and wants to know how the data is managed until the result is returned, what to do ? I've tried this manually with tools like mtrace and browser. This is a bit frustrating because the

Hello, I am trying to create parcels for a CFA model. I am trying to average 6 sets of 3 variables each into parcels. I don't understand why I am getting an error message as follows: Thanks for your help, Catherine atds1par <- rowMeans(semHW1dat1[, c("atds1", "atds2", "atds3")], na.rm=TRUE) atds2par <- rowMeans(semHW1dat1[, c("atds4",

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 All I am having 2 very long character strings (550chars) and I want to put them as expressions together with c(). The problem is that I also get these double-quotes, as seen below in 'fct'. How can I remove these double-quotes? I tried as.name() but it did not work (because of size?). These are creating trouble with subsequent programs, which I tested with strings that for some

I performed a CFA using the lavaan package require('lavaan'); HS.model <- 'external_regulation_soc =~ JOBMOTIVATIE_extsoc1 + JOBMOTIVATIE_extsoc2 + JOBMOTIVATIE_extsoc3 external_regulation_mat =~ JOBMOTIVATIE_extmat1 + JOBMOTIVATIE_extmat2 + JOBMOTIVATIE_extmat3 introjected_regulation =~ JOBMOTIVATIE_introj1 +

Hi All, I'm attempting to use eps-regression or nu-regression SVM to compute probabilities but the predict function applied to an svm model object returns values outside [0, 1]: Variable Data looks like: Present X02 X03 X05 X06 X07 X13 X14 X15 X18 1 0 1634 48 2245.469 -1122.0750 3367.544 11105.013 2017.306 40 23227 2 0 1402 40 2611.519 -811.2500 3422.769 10499.425 1800.475 40 13822 3 0 1379

Dear all, I have a data frame in which the columns need to be ordered. The first column X is at the right position, but the remaining columns X1-Xn should be ordered like this: X1, X2, X3 etc instead of like below. > colnames(pos1) [1] "X" "X1" "X10" "X11" "X12" "X13" "X14" "X15" "X16"

Hello! I am trying to reproduce (for a publication) analyses that I ran several months ago using lavaan, I'm not sure which version, probably 0.4-12. A sample model is given below: pathmod='mh30days.log.w2 ~ mh30days.log + joingroup + leavegroup + alwaysgroup + grp.partic.w2 + black + age + bivoc + moved.conf + local.noretired + retired + ds + ministrytime + hrswork + nomoralescore.c +

Bill, > The reason for it: when going to calculate the CFA in LLVM, the > original code was adding an offset of type i32 to a pointer. This is > fine if pointers are 32-bits, but we get an assert if the types are > different. By converting the cfa_offset to a pointer, we get it to be > that size. Please ignore my prev. e-mail. The code breaks at least 32-bit stuff. The arg of

Hi, May be this helps (Assuming that there are only '0's and '1's in the dataset) dat1<-read.table(text=" ??????? ID X0 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 1?? 5184??? 0??? 0??? 0??? 0??? 0?? 0?? 0??? 0??? 0??? 1???? 0????? 0????? 0????? 0????? 0????? 0 2?? 6884??? 0??? 0??? 1??? 0??? 0?? 1?? 0??? 0??? 0??? 0???? 0????? 0????? 0????? 0????? 0????? 0 3?

Hello R Community, I am using the Lavaan package in R 2.15.0 to analyze data collected from 1200 lakes across North America. My dataset includes 3 continuous independent variables (LOG_NTL, LOG_PTL, and LOG_SR_A_D) and 1 continuous dependent variable (BIOVOL) . I have successfully constructed structural equation models using the Lavaan package (example included below with code), but I have not

I have been trying to use lavaan (version 0.4-7) for a simple path model, but the program seems to be computing far less degrees of freedom for my model then it should have. I have 7 variables, which should give (7)(8)/2 = 28 covariances, and hence 28 DF. The model seems to only think I have 13 DF. The code to reproduce the problem is below. Have I done something wrong, or is this something I

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'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]]