Hi, I am wondering if anyone knows of an easy way to fit a saturated model using the sem package on raw data? Say the data were:
mtcars[, c("mpg", "hp", "wt")] The model would estimate the three means (intercepts) of c("mpg", "hp", "wt"). The variances of c("mpg", "hp", "wt"). The covariance of mpg with hp and wt and the covariance of hp with wt. I am interested in this because I want to obtain the MLE mean vector and covariance matrix when there is missing data (i.e., the sum of the case wise likelihoods or so-called full information maximum likelihood). Here is exemplary missing data: dat <- as.matrix(mtcars[, c("mpg", "hp", "wt")]) dat[sample(length(dat), length(dat) * .25)] <- NA dat <- as.data.frame(dat) It is not too difficult to write a wrapper that does this in the OpenMx package because you can easily define paths using vectors and get all pairwise combinations using: combn(c("mpg", "hp", "wt"), 2) but I would prefer to use the sem package, because OpenMx does not work on 64 bit versions of R for Windows x64 and is not available from CRAN presently. Obviously it is not difficult to write out the model, but I am hoping to bundle this in a function that for some arbitrary data, will return the FIML estimated covariance (and correlation matrix). Alternately, if there are any functions/packages that just return FIML estimates of a covariance matrix from raw data, that would be great (but googling and using findFn() from the sos package did not turn up good results). Thanks! Josh -- Joshua Wiley Ph.D. Student, Health Psychology Programmer Analyst II, Statistical Consulting Group University of California, Los Angeles https://joshuawiley.com/ ______________________________________________ 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.