similar to: Incorrect degrees of freedom in SEM model using lavaan

What objective function is maximized by survreg with the default Weibull model? I'm getting finite parameters in a case that has the likelihood maximzed at Infinite, so it can't be a simple maximum likelihood. Consider the following: ############################# > set.seed(3) > Stress <- rep(1:3, each=3) > ch.life <- exp(9-3*Stress) > simLife <- rexp(9,

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,

Dear R users, could you please help me figure out why I am getting an error? Initially my data looks like this: > attributes(compl)$names [1] "UserID" "compl_bin" "Sex.x" "PHQ_base" "PHQ_Surv1" "PHQ_Surv2" "PHQ_Surv3" [8] "PHQ_Surv4" "EFE"

Hi, I am looking for the best multidimensional configuration for my data (47*47 distance matrix). I ve tried classical metric (cmdscale) and non metric MDS (isoMDS, nmds) but it is now difficult to choose the best solution because of the uncertainties in the definitions of the "stress" function. So, same problem, several questions : 1. Statistical consideration : With

why does teh following code not work? a<-rnorm(50) dim(a)<-c(10,5) mycov<-function(mat,i,j){ sum((mat[,i]-mean(mat[,i]))*(mat[,j]-mean(mat[,j]))) } covmat<-function(X){ outer(1:dim(X)[2],1:dim(X)[2],function(v1,v2){mycov(X,v1,v2)})} from what i know covmat(a) should give the covariance matrix of a, but it gives a matrix with constant

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

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

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 +

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

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

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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 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

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

Dear R-users, Does somebody know what does the "Estimate" reported by the Lavaan model tell us? I assume this tells the relative strength of the dyadic relations. Thank you for your help! Regards, Sylvain -- View this message in context: http://r.789695.n4.nabble.com/Lavaan-model-tp4648004.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I am using package lavaan and have created a structural equation model with two exogenous and seven endogenous variables with the following relationships #specify the model m1 = ' # regressions D ~ ma + hs + b4 + b5 + b15 + b16 ma ~ hs + b4 + b5 + b15 + b16 hs ~ b4 + b5 + b15 + b16 b4 ~ el + la b5 ~ el + la

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

Dear R-users, A new package called `lavaan' (for latent variable analysis) has been uploaded to CRAN. The current version of lavaan (0.3-1) can be used for path analysis, confirmatory factor analysis, structural equation modeling, and growth curve modeling. More information can be found on the website: http://lavaan.org Some notable features of lavaan: - the 'lavaan model

Dear R-users, A new package called `lavaan' (for latent variable analysis) has been uploaded to CRAN. The current version of lavaan (0.3-1) can be used for path analysis, confirmatory factor analysis, structural equation modeling, and growth curve modeling. More information can be found on the website: http://lavaan.org Some notable features of lavaan: - the 'lavaan model