similar to: Sem and nlm and ols instead of ml

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

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

Adam, It seems that your attachment didn't make it through. That aside, my experience with strange errors like those (random type not implemented ones) has been that you may be looking at a memory problem on you machine. Given that you can't replicate on another platform (and the .csv file didn't come through), I would think it wise to start there. My 2c. And I love bacon too :)

Dear Colleagues, Occasionally I deal with computer-generated (i.e., websurvey) data files that haven't quite worked correctly. When I try to read the data into R, I get something like this: Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 26 did not have 648 elements ...is there any way to get R to tell me how many elements line 26 *did* have? That

Dear colleagues, I've been running some principal components analyses, which generate tables of loadings that I'm interested in looking at. print(f1$rot$load,cutoff=.4) is what I use, and it gives me what I want. However, I'm now interested in comparing these loadings across a few data sets. In other words, I would like R to match the loadings on rownames() and display them next

Dear Colleagues, I've been searching for a post or article or something which explains why having na.rm=FALSE or na.action=na.fail as the default is a better choice than TRUE or na.omit. I understand the basic argument: it does not make sense to average a nonexistance into an aggregate, and removing them implicitly leads to accidental pairwise deletion in some cases, and sum(x) /

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

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

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'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

Hello, I have searched and failed for a program or script or method to conduct a power analysis for a MANOVA. My interest is a fairly simple case of 5 dependent variables and a single two-level categorical predictor (though the categories aren't balanced). If anybody happens to know of a script that will do this in R, I'd love to know of it! Otherwise, I'll see about writing one

Hello, I'm having the following questionable behavior: > summary(m) Min. 1st Qu. Median Mean 3rd Qu. Max. 1 13000 26280 25890 38550 50910 > max(m) [1] 50912 > typeof(m) [1] "integer" > class(m) [1] "integer" ...it seems to me like max() and summary(m)[6] ought to return the same number. Am I doing something wrong? I'm

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

Dear colleagues, I run R on a few different machines, and view graphs and the like by tunnelling X through SSH to my local machine. This is useful for me because my local machine can't easily handle some of the data sets I work with. However, when an ssh connection dies, the tunnelled X session also dies, which breaks R's device connection, generating this error: > Error: X11

Dear R community I have stored the results of arrays in a list consist of J-components (say 200 components). Each component containing same no of columns but may be different no of rows. e.g [[1]] [,1] [,2] [,3] [,4] [,5] [1,] 4 0 0 0 0 [2,] 4 3 4 0 0 [3,] 4 3 4 0 0 [4,] 4 3 0 0 0 [[2]] [,1] [,2] [,3] [,4] [,5]

Hi, I'm using the sem package under conditions where I know beforehand that several models will be problematic. Because of that, I want to suppress warnings. I've used the following code sem(mod, S = as.matrix(dataset), N = 1000, maxiter = 10000, warn = FALSE) But I still get warnings. I've also tried adding "check.analytic = FALSE, debug = FALSE," and that

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)

Hello all, I'm trying to replicate a confirmatory factor analysis done in Amos. The idea is to compare a one-factor and a two-factor model. I get the following warning message when I run either model: "Could not compute QR decomposition of Hessian. Optimization probably did not converge." I have no idea what to do here. I believe posters reported the same problem. It seems

Dear R list members, I have a question about the proper use of the typsize and fscale arguments to nlm. I use nlm in my sem package to fit general structural-equation models, which entails maximizing a multinormal likelihood with respect to parameters that represent regression coefficients and covariances of variables. The magnitudes of these parameters can be very different. The

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