similar to: path analysis (misspecification?)

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.;

I am trying to simulate a dataset using Parallel Latent CTT model and this is what i have done so far: (START) #Importing psych library for all the simulation related functions library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #Using the function to generate the data GenData <- congeneric.sim(N=500, loads =

Hi All, I am using fleiss kappa for inter rater agreement. Are there any know issues with Fleiss kappa calculation in R? Even when I supply mock data with total agreement among the raters I do not get a kappa value of 1. instead I am getting negative values. I am using the irr package version 0.70 Any help is much appreciated. Thanks and Regards M [[alternative HTML version deleted]]

Hi! I am trying to analyse with R a database that I have previously analysed with SPSS. Steps with SPSS: Factorial analysis Extraction options : I select = Principal component analysis Rotation: varimax Steps with R: I have tried it with varimax function with factanal or with princomp...and the results are different of what I have with SPSS. I think that varimax function is incorporated in

hi, How can I compute a reliability score of a scale consisting only of dichotomous items? thanks for any help!

Hello,   In SPSS, a multiple regression can be conducted by inputting the means, standard deviations, sample size, and correlation matrix without actually using the raw dataset. Is it possible to do the same in R?   Thanks in advance for your assistance.   Linda [[alternative HTML version deleted]]

Hello, I was wondering if someone could tell me how to output, to file, the residuals from a REML model-fit. The type of residuals I am interested in are the simple "original raw values - model fit" type. Thanks in advance, Josh B. [[alternative HTML version deleted]]

Dear List, I use Rgraphviz for display of graphs in some packages. Since Rgraphviz is no longer on CRAN it needs to be installed from Bioconductor and that is fine, but I have trouble figureing out the following: I create a plot method which - if Rgraphviz is installed - uses Rgraphviz for displaying and otherwise does nothing. This is implemented as: if (!("package:Rgraphviz" %in%

Hi R, Let, x=1:80 I want to sum up first 8 elements of x, then again next 8 elements of x, then again another 8 elements..... So, my new vector should look like: c(36,100,164,228,292,356,420,484,548,612) I used: aggregate(x,list(rep(1:10,each=8)),sum)[-1] or rowsum(x,group=rep(1:10,each=8)) But without grouping, can I achieve the required? Any other ways of doing

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little

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

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 wonder if someone could explain why, when I perform confirmatory factor-analysis model using polychoric correlations why I do not get an estimated confidence interval for the RMSEA. My experience with these type models is that I would obtain a confidence interval estimate. I did not get any warning messages with the output. RESULTS: Model Chisquare = 1374 Df = 185 Pr(>Chisq) = 0

Hello folks, many multivariate anayses (e.g., structural equation modeling) require multivariate normal distributions. Real data, however, most often significantly depart from the multinormal distribution. Some researchers (e.g., Yuan et al., 2000) have proposed a multivariate transformation of the variables. Can you tell me, if and how such a transformation can be handeled in R? Thanks in

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20

Hello, I'm using the "sem" package to do a confirmatory factor analysis on data collected with a questionnaire. In the model, there is a unique factor G and 23 items. I would like to calculate the standardized residual variance of the observed variables. "Sem" only gives the residual variance with the "summary" function, or the standardized loadings with the

Hi list, I'm performing a series of confirmatory factor analysis on different groupings of items from data collected with questionnaires. There are some missing values. For those sets with no missing values I call factanal(datamatrix,factors=n) where datamatrix is a table of all observations for the items under investigation. This call fails when there are missing values. help(factanal)

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

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