similar to: sem package, suppress warnings

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

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",

Let me start with an example: par(mfrow=c(2,3)) for (i in 1:5){ x = rnorm(100) y = .5*x + rnorm(100, 0, sqrt(1-.5^2)) plot(x,y) } Note that there's five plots and six spaces for those plots via mfrow, leaving one row empty. Is there a way to have the bottom two plots centered? I think it looks weird to have them left-justified. Thanks in advance for the help! -- Dustin Fife PhD

Forgive me if this is a trivial question, but I couldn't find it an answer in former forums. I'm trying to reproduce some SAS results where they set two parameters equal. For example: y = b1X1 + b2X2 + b1X3 Notice that the variables X1 and X3 both have the same slope and the intercept has been removed. How do I get an estimate of this regression model? I know how to remove the intercept

Hi, I'm working on a project that will generate RAM matrices at random. What I want to do is to be able to automatically identify if the model is non-recursive. For example, the following RAM matrix has a non-recursive loop (going from A to B to C to A): n.recursive <- data.frame(matrix(c("A", "B", 1, "B", "C", 1,

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

How can I stop the following warning from occuring? invalid factor level, NAs generated in: "[<-.factor"(`*tmp*`, iseq, value = structure(1, .Label = "12", class = "factor")) The Label messages are for "5", "8", "12" and "46". I want the NAs to be generated as needed. Is this causing R to slow down by generating the warning

Dear R users, I am trying to simulate some multitrait-multimethod models using the packages sem and psych but whatever I do to deal with models which do not converge I always get stuck and get error messages such as these: "Error in summary.sem(M1) : coefficient covariances cannot be computed" "Error in solve.default(res$hessian) : System ist f?r den Rechner singul?r: reziproke

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --1660387551-1458482416-1249639718=:2997 Content-Type: TEXT/PLAIN; CHARSET=US-ASCII; FORMAT=flowed Content-ID: <Pine.LNX.4.64.0908071039211.2997 at parser.ilovebacon.org> Hello, There appears to be a bug in the nlm function, which I

Hi all, hope someone can help me out with this. Background Introduction I have a data set consisting of data collected from a questionnaire that I wish to validate. I have chosen to use confirmatory factor analysis to analyse this data set. Instrument The instrument consists of 11 subscales. There is a total of 68 items in the 11 subscales. Each item is scored on an integer scale between 1 to 4.

I'm using the "Match" package to do propensity score matching. Here's some example code that shows the problem that I'm having (much of this code is taken from the Match package documentation): *data(lalonde) glm1 <- glm(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75,

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --1660387551-150661043-1249684349=:2997 Content-Type: TEXT/PLAIN; charset=iso-8859-1; format=flowed Content-Transfer-Encoding: QUOTED-PRINTABLE Hi Jeff, =09As mentioned in my message, I *did* replicate on another platform.=20 One platform was

Hello, I'm using the nls function and would like to increase the number of iterations. According to the documentation as well as other postings on R-help, I've tried to do this using the "control" argument: nls(y ~ SSfpl(x, A, B, xmid, scal), data=my.data, control=nls.control(maxiter=200)) but no matter how much I increase "maxiter", I get the following error

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

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

Dear colleagues, Has anybody any experience using the sem package to fit structural equation models using a fitting function other than ML? I have heard tell that OLS may provide better estimates when using standardized matrices generated from small sample sizes, so I was interested in comparing the two for a few models. However, ML appears to be hard-coded into the source for sem...but maybe

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)

Dears, I'm a college student and In doing my statistics homework. I use R with SEM package as my tool for sem analysis, but my teacher told me AMOS is more suitable for such analysis. Could someone help tell me whether it is true that some commercial software is better accepted in academic fields? Sorry if I should not post such topics here. -- Best Regards, Reeyarn T. Lee Accounting

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, 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|)