Hello,
I’m trying to implement a slightly changed Maximum Likelihood function using mxFitfunctionR and ran into some problems that I wasn’t able to solve. Here is a short version of my current attempt at implementing a covariance-based FML function without any changes:
simple_FML <- function(model, state) {
obsCov <- model$data$observed# observed covariance
nmanif <- ncol(obsCov) # number of observed variables
I would like to conduct BLRT test for 2 nested GMM model with definition variables. I referred to a previous post
https://openmx.ssri.psu.edu/node/4329 and it worked well on my computer. However, when I run the comparison of my model, it reported errors. I attached a screenshot of LRT without and with bootstrap in the attachment. And here is the information of my OpenMx Version:
I'm interested in using the new bootstrap function in mxCompare to evaluate nested growth mixture models (GMM) using the Bootstrap Likelihood Ratio Test (BLRT), but am a uncertain how to do so. For example, in the attached code building on the GMM example provided in the OpenMx documentation, say I wanted to compare a three-class model to a two-class model. How would I conduct the BLRT?
It seems that OpenMx uses the same formula of RMSEA for both single- and multiple-group analyses. According to Steiger (1998), the RMSEA should be adjusted by a factor of sqrt(K) where K is the no. of groups.
The attached PDF includes the test suggested by Steiger (1998, p. 417):
1. Construct two identical arbitrary data sets (random numbers will suffice).
2. Test one sample with a simple model, for example, a single factor model, and record the RMSEA value.
Wondering if Satorra / Satorra-Bentler scaled chi-square is available in OpenMx or not? i.e., ML with robust corrections to the test statistics and standard errors in the case of non-normal data. Ideally, I'd like to see if the difference test for model comparisons is available. Only thing I found in the forums was this post from quite a while ago: http://openmx.psyc.virginia.edu/thread/530
My attempts to specify a custom R objective function with mxFitFunctionR (using latest build from the git mirror) are not working, because it seems as though the model that gets passed into the fitfunction is with all the algebras unevaluated. Am I doing something wrong, or thinking about this completely wrong? Is there a way I can achieve similar with the algebras evaluated? Below is a minimal example modified from the mxFitFunctionR example. Thanks!
A <- mxMatrix(nrow = 4, ncol = 1, values = c(6:9), free = TRUE, name = 'A')