similar to: sem - problem

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, is there a similar package like SEM. In fact, I am looking for a package where I as input several given vector variables which are part of an autoregressive model. Then I would like to obtan parameters for so called latent variables. Thank you, Luba [[alternative HTML version deleted]]

Hello, I've been unsuccessfully trying to reproduce a sem from Grace et al. (2010) published in Ecological Monographs: http://www.esajournals.org/doi/pdf/10.1890/09-0464.1 The model in question is presented in Figure 8, page 81. The errors that I've been getting are: 1. Using a correlation matrix: res.grace <- sem(grace.model, S = grace, N = 190) Warning message: In sem.default(ram

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",

Hi R-users: When I run the R script (as the following), I got the error message: Error in solve.default(C[ind, ind]) : Lapack routine dgesv: system is exactly singular. Any help is appreciated. Ying library(sem) R.pw <- matrix(c( 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2137356, 0.2137356, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

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

Hello, I tried to build an own R package on Windows XP but get an error which I can't solve. I called my package "pack". It works to creat the file "pack" with package.skeleton(). But when I try to compile it with Rcmd build --binary pack I obtain the following error: IO::Seekable::seek missing at C:/R/R-2.9.0/share/perl/FileHandle.pm line 59. Compilation failed in

hi, I plot path diagrams with the path.diagram() function of the sem package in combination with the graphviz application. Now I want the graphviz code for a path-plot with the actual standardized coefficients on the arrows (not the names). I tried to add edge.labels="values" as an argument to path.diagram() but it's just ignored. can anyone help me on that? p.s.;

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

What??s happening with this following code: require(sem) Celpe.Mod.RAM <- matrix(c( # path parametro Inicio "Produ????o -> T1", "gamma.11", NA, "Produ????o -> T2", "gamma.12", NA,

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

Dear R-users, I am running a large number of simulations and estimating a structural equation model for each one using the SEM package. Each run of my program has around 8000 simulations. Most of the time the program completes all of them correctly but sometimes I get a segmentation fault in the sem routine and my program stops with the following error message: > *** caught

Hi, Excuse me for asking this silly question. But I really couldn't understand why cov() and ccov() don't work for my calculation of covariance matrix. a <- matrix(1:8, 2, 4) a [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8 > ccov(a) Error in solve.default(cov, ...) : Lapack routine dgesv: system is exactly singular I also tried colume bind, but it

Dear users, i'm using the sem package in R, because i need to improve a confermative factor analisys. I have so many questions in my survey, and i suppose, for example, that Question 1 (Q1) Q2 and Q3 explain the same thing (factor F1), Q4,Q5 and Q6 explain F2 and Q7 and Q8 explain F3... For check that what i supposed is true, i run this code to see if the values of loadings are big or not.

I am trying to work my way through the book "Singer, JD and Willett, JB, Applied Longitudinal Data Analysis. Oxford University Press, 2003" using R. I have the SAS code and S-Plus code from the UCLA site (doesn't include chapter 8 or later problems). In chapter 8, there is a structural equation/path model which can be specified for the sem package as follows S <- cov(al2)

Hi, I am doing a hierarchical CFA using the sem package. I have 20 items, and I have 2 factors (F3 and F4), and also F1 and F2 are nested within F3. Here is the code that I have, but it is giving me an error message "Warning message: In eval(expr, envir, enclos) : Negative parameter variances. Model may be underidentified." and a further error "Error in summary.objectiveML(cfa,

Hi. I have tried to use Package "SEM". As a learning, I try to convert a program running well of EQS which is as follows to SEM: ### EQS ### /SPECIFICATION CAS=100; VAR=5 MAT=COR; ANA=COR; /EQUATIONS V1=*F1+E1; V2=*F1+E2; V3=*F1+*F2+E3; V4=**F1+*F2*E4; V5=*F2+E5; /VAR E1 TO E5=*; F1*1.0; F2=1.0; /COV E1,E2=*; F1,F2=*: /PRINT FIT ALL; /MATRIX ...... /END This is the converted SEM

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

I try to load the package lattice by library(lattice). Although I have installed it through CRAN by internet as well as from a local directory using the zip-file, it can't be loaded into the editor. Can anyone give advice, please. I am using R 2.9.1 on Widows XP. Thanks a lot, Luba [[alternative HTML version deleted]]

Dear all, I met a problem while doing SEM by sem package. I got a negative chi-square of null model. Because the theoretical value of chi-square cannot be negative, I checked the source code of sem.R in sem package and I found the Chi-square of null model was computed by the following expression: result$chisqNull <- (N - 1) * (sum(diag(S %*% diag(1/diag(S)))) + log(prod(diag(S)))) I think