similar to: new version 2.0-0 of the sem package

Dear all, I am trying to estimate simultaneous equation model concerning growth in russian regions. I run the analysis by means of FIML in R sem package. I am not familiar with SEM yet, but I've just got several suitable estimated specifications. Nevertheless, sometimes R gives the following warning message: Warning message: Negative parameter variances. Model is probably underidentified.

Dear R users, Version 3.0-0 of the sem package is now on CRAN. From the package NEWS file: o Compiled code for optimization. o Added multi-group models. o Modification indices for equality-constrained parameters. o weights argument added to tsls(). o raw argument added to cfa(). Of these changes, the first two are the most significant, and the first -- the use of compiled code to

Hello, I use the function sem the following way sem.mod <- sem(model, mod.cov, N=109) where the variables are modelled: Z -> M Z -> I Z -> R M <-> M I <-> I R <-> R Z <-> Z The output is ... Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -7.3300 -0.2750 -0.2670 -0.1290 -0.0369 9.0300 Parameter Estimates Estimate Std Error z value Pr(>|z|)

Hello, I have conducted an SEM in which the resultant standardized path coefficients are much higher than would be expected from the raw correlation matrix. To explore further, I stripped the model down to a simple bivariate relationship between two variables (NDVI, and species richness), where it's my understanding that the SEM's standardized path coefficient should equal the correlation

Hello. I am getting results from sem that are not correct (that's assuming that the results from my AMOS 4.0 software are correct). sem does not vary some of the parameters substantially from their starting values, and the final estimates of those parameters as well as the model chisquare value are incorrect. I've attached some code that replicates the problem. The parameters in

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

Sorry if this question has been asked previously, I searched but found little. There also doesn't seem to be a dedicated SEM list-serv so hopefully this will find its way to the appropriate audience. In discussing SEM with a colleague I mentioned that a model they were fitting in AMOS was equivalent to a linear regression and that the coefficients would be the same. This of course was the

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

Greetings, I am working in the sem package on a model with 3 exogenous variables (2 are nominal-categorical), and 4 endogenous, continuous variables. To use sem with the nominal variables, I created dummy variables. Now, in my sem output I have estimates for path coefficients for the relationship between each level of the nominal variables and the endogenous variables they are associated

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact

salutations! i am doing some longitudinal modeling with sem and thought calculating some "simple variables" would make my model more readable. this is the smallest subset of my model that illustrates the resulting problem. i have 2 observed exogenous variables (c1, d2) and 4 observed endogenous variables (dc1, dd1, dc2, dd2). c1 is the observed state at time 1, dc1 is the change in c

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

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

Dear R list members, Since today seems to be the day for optimization questions, I have one that has been puzzling me: I've been doing some work on sem, my structural-equation modelling package. The models that the sem function in this package fits are essentially parametrizations of the multinormal distribution. The function uses optim and nlm sequentially to maximize a multinormal

I'm having the most curious error while using the sem package. For the model I'm working with, I keep getting the following error: Error in J[cbind(1:n, observed)] <- 1 : subscript out of bounds I''ve used debug=TRUE with sem, and there don't appear to be any problems with model - there are no latent variables in this model. The variables in the covariance matrix

Hi, New to both R and SEM, so this may be a very simple question. I am trying to run a very simple path analysis using the sem package. There are 2 exogenous (FARSCH, LOCUS10) and 2 endogenous (T_ATTENT, RMTEST) observed variables in the model. The idea is that T_ATTENT mediates the effect of FARSCH and LOCUS10 on RMTEST. The RAM specification I used is FARSCH -> T_ATTENT, y1x1, NA

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:

Dear R users/developers I requested help to solve the problem of formulating Multivariate Sample selection model by using Full Information Maximum Likelihood (FIML)estimation method. I could not get any response. I formulated the following code of FIML to analyse univariate sample selection problem. Would you please advise me where is my problem library (sem) library(nrmlepln) Selection

I'm uncertain how helpful it will be to give example code, but last week, this model gave an error message to the tune of "failed to converge" after about 5 minutes of run-time : library(nlme) model.A<- lme (fixed = avbranch~ wk*trt*pop , random = ~wk|ID/fam/pop, data=branch) It seemed that failure to converge made sense, since there were many weeks (wk) and values for