similar to: CFA with lavaan or with SEM

I am currently working with R's polycor package and I have encountered a problem. I tried to follow the steps as outlined in the sem.pdf file where a CFA model is run using polychoric correlations. Every time I run the command sem(model, data, N=.), I get the following warning message: Warning message: In if (orthogonal) { : the condition has length > 1 and only the first element will be

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

Hi there, Am I correct to believe that tetrachoric correlation is a special case of polychoric correlation when there are only two levels to the ordered factor? Thus it should be okay to use hetcor from the polycor package to build a matrix of correlations for binary variables? If this is true, how can one estimate 95% confidence intervals for the correlations? My guess would be mat =

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

About two years ago there was a thread about this which suggested that at that time nobody had these coefficients ready to go. (a) has anyone in the meanwhile programmed them? (b) I think I can see how to do the tetrachoric one with mvtnorm on similar lines to an example on the help page so will try that if nobody else already has (c) looking at the polychoric one makes me realise yet again

Hello R Users, While I have attempted to dig into the R help files and I have not identified the answer to these questions, I apologize in advance if my questions were answered in the past. I also recognize that one of my questions unfortunately verges on statistical rather than code territory. I have two rather unrelated questions about using the sem and polycor packages for a relatively

Hello, I'd like to know if there is a way to fit a Structural equation model with dichotomous indicators (ex: problem with a phone solved/ or not) having effects on a ordinal variable. How I do that using R? Do you have an example with the code in R that you can send to me? Thanks a lot! Renata Estrella UFRJ, Brasil, Rio de Janeiro Renata Leite Estrella Assistente de

Hello all I'm trying to run some path analysis in either sem or lavaan (preferably lavaan because I find its interface easier to use). Most of my variables are continuously distributed and fairly well-behaved but I have a single exogenous variable (sex) which is not continuously distributed. Preliminary model fitting suggests that there aren't any sex by (anything else) interactions. The

Hi I am looking for a structural equation modeling package in R which can be used for categorical data. Is anyone aware of the existence of such a package? Would appreciate any help on this. Thank you Upasna -- --------------------------------------------------------------------- Upasna Sharma Research Scholar Shailesh J. Mehta School of Management, Indian Institute of Technology, Bombay

I am trying to run a confirmatory factor analysis using the SEM package. My data are ordinal. I have read http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf. When I apply the hetcor function, I receive the following error: Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of 'lower' is larger than 'upper' Example:

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 +

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

Hello, It has been found that SEM analysis using polychoric correlations + maximum likelihood estimator produces incorrect test statistics and standard errors (e.g., Flora, D. B., & Curran, P. J. (2004). An Empirical Evaluation of Alternative Methods of Estimation for Con?rmatory Factor Analysis With Ordinal Data. Psychological Methods, 9(4), 466-491). Standard errors can be dealt with by

I hope someone here knows the answer to this since it will save me from delving deep into documentation. Based on 22 pairs of vectors, I have noticed that tetrachoric correlation coefficients in stata are almost uniformly higher than those in R, sometimes dramatically so (TCC=.61 in stata, .51 in R; .51 in stata, .39 in R). Stata's estimate is higher than R's in 20 out of 22

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->

Hi - I am trying to fit sem model with intercepts. Here is what I have in my model. Exogeneous vars: x1 (continous), x2 (ordinal), x3 (ordinal), x4(continuous) Endogeneous vars: y1 (continuous), y2 (ordinal), y3 (ordinal) SEM model: x1 -> eta1; x2 -> eta1; x3 -> eta2; x4 -> eta2; eta1 -> y1, eta1 -> y2, eta2 -> y2, eta2 -> y3 However, in these arrow models, I

Dear All, I am new for R and SEM. I try to fit the model with Y (ordinal outcome), X (4 categorical data), M1-M3 (continuous), and 2 covariates (Age&sex) as a diagram. library(polycor) model.ly <-specify.model() 1: x -> m1, gam11, NA 2: x -> m2, gam12, NA 3: x -> m3, gam13, NA 4: age -> m1, gam14, NA 5: age -> m2, gam15, NA 6: age -> m3, gam16, NA 7: sex -> m1,

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for