OpenMx Developer Forums

topic for mxFactor

I have some questions about the correct approach to sort raw data. Imagine the following dataset:

The following proposal would change the behavior of mxMatrix() in some circumstances based on the 'nrow' and 'ncol' arguments. This proposed is based on the following bug report: http://openmx.psyc.virginia.edu/issue/2010/07/matrix-not-populated-expected-r-based-nrow-ncol

I'm trying to run a model where I need to round a value in an mxMatrix to an integer. I'm doing this in an mxAlgebra. In the help files for mxAlgebra, I noticed that there is no 'round' function listed. (There are lots of other functions: min, max, abs, sum, etc.) Is there a reason for its exclusion, or was it just forgotten?

In R, if you type ?Arith to get the help page for the S4 generic mathematical operators. An mxAlgebra should probably be able to handle all of these, and already does handle most of them.

The OpenMx developers team has started looking at the most effective optimizations we can implement prior to the version 1.0 release this summer. One large candidate is the sorting of raw data, and then using the information learned from the sorting process to speed up full information maximum likelihood (FIML) estimation. We should write a forum post devoted entirely to the sorting process, and the various transformation we can perform after we have sorted the data, but that forum post will appear in the future.

I was searching for multiple groupe examples [Google in side bar] and was directed to:

http://openmx.psyc.virginia.edu/openmx-features

The phrase "Multiple Groups" looks like it links to an example, but only returns to the same page. This is true for all of the listed features with the exception of "Integration with R". It would be helpful if the features were linked to examples, the on-line documentation, or a wiki page.

I wonder if others would like the confidence intervals report to include the initial value in the middle as a convenience
i.e.,
confidence intervals:

                lbound        estimate        ubound
top.a[1,1]   0.6275213     0.713             0.7931507

rather than
                lbound    ubound
top.a[1,1]   0.6275213  0.7931507

Hi all,

I'm writing a simple latent change score demo using mxPath, and found something interesting. I believe we've talked about default means models before, but I don't recall.

Currently the svn repository has an implementation of likelihood-based confidence intervals. For the moment, the interface looks like the following:

model <- mxModel(model, mxNormalInterval(c('A', 'C[,]', 'E[1,1]'), 0.95, 0.95))

The first argument to mxNormalInterval() is a vector of strings. These could be either matrix/algebra names or free parameter names. The second argument is the lower confidence level, and the third argument is the upper confidence level.

I would like to propose a unified method for users to obtain updates to OpenMx.

mxUpdate()

Without any arguments, this would allow people to load the most current version of OpenMx. If a version number was supplied it could load a specific version if they wanted to roll back an upgrade.

In its simplest form it would look something like

mxUpdate <- function(version=NA) {
    if(is.na(version)) source('http://openmx.psyc.virginia.edu/getOpenMx.R')
    else source(paste("http://openmx.psyc.virginia.edu/get", version, ".R", sep=""))
}