Dear Adam, I'm afraid that our emails have crossed. Please see my previous message.
John ------------------------------ John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: Adam D. I. Kramer [mailto:[EMAIL PROTECTED] > Sent: September-18-08 2:12 PM > To: John Fox > Cc: r-help@r-project.org > Subject: Re: [R] Difficulty understanding sem errors / failed confirmatory > factor analysis > > Dear John, > > On Thu, 18 Sep 2008, John Fox wrote: > > >> 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 structure of word usage patterns in weblogs) in a > >> special population. The model looks like model.txt (attached as many > >> people hate long emails); the correlation matrix cors.txt as well. > > > > As far as I can see, the attachments aren't there. If you like, you can > > send them to me privately. Without the input covariance matrix and your > > model, it's very hard to tell what the source of the problem is, but one > > guess (assuming that you've specified the model correctly) is that the > > assumption of uncorrelated factors is too far off. Also see below. > > I have pasted the matrix into another email; apologies for failing to attach > them acceptably before. > > I also augmented the model to allow the factors to correlate, by adding > these lines to the model: > > Melancholy <-> Social, Soc.Mel, NA > Melancholy <-> Rant, Rant.Mel, NA > Melancholy <-> Work, Work.Mel, NA > Melancholy <-> Metaphysical, Meta.Mel, NA > Social <-> Rant, Soc.Rant, NA > Social <-> Work, Soc.Work, NA > Social <-> Metaphysical, Soc.Meta, NA > Rant <-> Work, Rant.Work, NA > Rant <-> Metaphysical, Rant.Meta, NA > Work <-> Metaphysical, Work.Meta, NA > > ...and obtain the same errors. > > >> > >> I'm setting no overlap between factors, no correlation between > >> factors, and estimating a separate variance for each observed variable > >> (which should be everything on the right-hand side of the -> arrows), but > >> setting the factor variances equal to 1...pretty standard. I've ensured > > that > >> everything is typed correctly to the best I am able. > >> > >> The problem: > >> > >> library(sem) > >> model.kr <- specify.model(file="model.txt") # printing it checks out ok > >> correl <- read.csv("cors.csv", header=TRUE) # printing it checks out ok > >> kr.sem <- sem(ram=model.kr,S=correl,N=3034) > >> ...about 10 seconds pass... > >> 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. > >> > >> (running qr on correl works fine; randomly-generated correl matrices fail > > in > >> the same way; I do not know how to further troubleshoot this) > > > > Doing a QR decomposition on the correlation matrix of the data is > > essentially irrelevant. The issue is the Hessian. (The scaled inverse > > Hessian is the covariance matrix of the parameter estimates, not of the > > data.) That you observe similar problems for randomly generated covariance > > matrices may or may not be troublesome, depending upon how you generated > > them. > > df <- as.data.frame(matrix(rnorm(3034*24),nrow=3034,ncol=24)) > df.cor <- cor(df) > rownames(df.cor) <- colnames(df.cor) <- colnames(correl) > sem.df <- sem(model.kr, df.cor, 3034) > > ...which now does not throw errors with the new model, even though that > syntax > was copied from my .Rhistory. I think I may have gotten unlucky with random > data the first time. > > Thanks for the info on what the error message means, though--I was largely > in the dark on that. > > >> ...and then the model itself (which is produced, as the above was just a > >> warning): > >> > >> summary(kr.sem) > >> Error in data.frame(object$coeff, se, z, 2 * (1 - pnorm(abs(z))), > > par.code) : > >> arguments imply differing number of rows: 47, 0 > > > > If the Hessian isn't positive-definite, it won't be possible to get > > estimated coefficient standard errors. I suspect that this is the source > > of this error message. If so, it would be better for summary.sem() to > > provide a more informative error message. > > This makes sense. It may also be useful for the sem() function to throw an > error rather than a warning if the Hessian matrix cannot be decomposed, > perhaps? How often is an SEM model without estimated coefficient standard > errors desirable? > > Thanks again for the assistance. I think the trouble may now be in my > correlation matrix; I will play around with my model and see whether > something else is more reasonable. > > --Adam ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
