similar to: sem: standardized covariance estimates

Dear list members, In sem, std.coef() will give me standardized coefficients from a sem model. But is there a trick so that path.diagram can use these coefficients rather than unstandardized ones? Thanks Steve Powell From: John Fox <jfox_at_mcmaster.ca> Date: Wed 28 Feb 2007 - 14:37:22 GMT Dear Tim, See ?standardized.coefficients (after loading the sem package). Regards, John John

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 started using the sem package in R and after a lot of searching and trying things I am still having difficulty. I get the following error message when I use the sem() function: Warning message: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I started with a

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 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.

Hi, I would like to print elements from sem (structural equation modeling) function (e.g., model-reproduced covariance matrix (C); estimated asymptotic covariance matrix of parameter estimates (cov)). How can I do this? Thanks, Andr?? -- Andr?? Tavares Corr??a Dias Laborat??rio de Ecologia Vegetal Universidade Federal do Rio de Janeiro CCS-IB-Departamento de Ecologia Caixa Postal 68020

Hello - Does anybody know how to get the SEM package in R to return standardized path coefficients instead of unstandardized ones? Does this involve changing the covariance matrix, or is there an argument in the SEM itself that can be changed? Thank you, Tim [[alternative HTML version deleted]]

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 am currently working with the sem package in R, to create pathway diagrams. Id like to use the standardized path coeffcients. To get these, I use std.coef. However, using this yields only the standardized coefficients, but does not give me the standard error. Does someone know how to get std.coef to show the standard error of the standardized path coefficients as well? Thanks, Bastiaan

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 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

Forgive me if I'm posting to the wrong place....It's my first time posting. Here's the situation: I'm using the sem package and making path diagrams using path.diagrams. Suppose I have the following code: #install.packages("ggm") require(ggm) cor = rcorr(7) nm = c("SOF", "IWF", "PWF", "FSC", "FSF", "EF",

Hello, I'm trying to fit a pretty simple confirmatory factor analysis using the sem package. There's a CFA example in the examples, which is helpful, but the output for my (failing) model is hard to understand. I'd be interested in any other ways to do a CFA in R, if this proves troublesome. The CFA is replicating a 5 uncorrelated-factor structure (for those interested, it is a

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

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

Hello I am using sem to look at the direct effect of one variable on another but i am uncertain if i am progressing correctly. An example: covar1<-? matrix(c(0.4,-0.2,3,-0.2 , 0.3,-2 , 3 ,-2 , 60), nrow=3,byrow=T) rownames(covar1)<-colnames(covar1)<-c("endo","exo","med") path1<-matrix(c(? ? "exo -> endo",? "g1", NA,

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 keep getting this error when I try to use the sem package. I and another person who has successfully used the sem package for similar analysis (fMRI effective connectivity) cannot figure out what is wrong with my code. I would appreciate any suggestions. The error message: Error in data.frame(object$coeff, se, z, 2 * (1 - pnorm(abs(z))), par.code) : arguments imply differing

Hi all, This is probably going to come off as unnecessary (and show my ignorance) but I am trying to understand the parameter estimates I am getting from R when doing an ANCOVA. Basically, I am accustomed to the estimate for the categorical variable being equivalent to the respective cell means minus the grand mean. I know is the case in JMP - all other estimates from these data match the

I feel really dumb for having to ask this, but here I go anyway... I perform structural equation modeling on a survey, using about 25 variables that create a total of 5 latent variables (factors). Applying sem (using the sem package) was a piece of cake, even for an (SEM) layman, thanks for THE excellent work here. I have all the variable/path coefficients, A- and P-matrix etc. But now I am