OpenMx General Help

Hi,

I am trying to detect and identify bivariate outliers in a dataset using OpenMx, in order to see whether specific outliers have significant contribution. Preferrably the output would be like that of %p in old Mx.
(i.e. 8 columns with:
1) -2lnL,
2) Mahalanobis,
3) estimated Z,
4) number of observations in data set,
5) number of data points in vector,
6) optimization details,
7) whether or not likelihood was calculable, and
8) model number if there are multiple models)

I have a data set with six binary variables, which I am trying to determine the temporal relationship. I was using lavaan R package, where they suggested to use dummy variable for endogenous variables (independent) and use ordered for exogenous (dependent variables). I was using the model as described in pdf file. I have gotten following results:

lavaan (0.5-16) converged normally after 31 iterations

Number of observations 51

Estimator DWLS Robust

Any chance of this being implemented at some point? Would seem to be consistent with the omxExponential implementation. Currently I optimize some free parameters over the range 0 to -inf, which is exponentially scaled, and I suspect this probably poses optimization difficulties....

Hi,

I am hoping to perform a multilevel SEM on complex-sampling survey data, with known weight and binary outcome and ordinal independent variables.

Similar posts on this forum don't seem to support that this is simple to do on OpenMx, but any advice would be much appreciated. (http://openmx.psyc.virginia.edu/thread/2348, http://openmx.psyc.virginia.edu/thread/861#comment-form)

Thank you.

Hi,

I am interested in obtaining both the number of major iterations and the overall number of objective function evaluations (how many times is the log likelihood and its gradient computed).
I found model@output$iterations and model@output$evaluations. model@output$iterations is the number of major iterations. model@output$evaluations contains a vector with two integer entries. Does somebody know what the entries are?

hi, I am a beginner using Mx open packages, so I do not quite understand. I have run my program. output is given as attached.
The value of RSMEA, CFI and TLI its very ugly because its value doesn't meet either criteria . What can I do in advance to improve the model?
thank you.

I'm trying to run a model in which I have several matrices that should contain the same parameter estimates (they overlap partly). I know I can use labels or mxConstraint to put equality constraints on parameter estimates, but I was wondering if it is also possible to refer directly to the parameter estimates of a matrix in other parts of the OpenMx environment. For example, I have tried to build an mxAlgebra that refers to the parameter estimates of an mxMatrix by using:

mxAlgebra(expression=cbind(Model.mxMatrix[,i],Model.mxMatrix[,j]), name="part_mxMatrix")

This may be an easy question, but I can't think of the answer. I've built a regression model (predicting Y) with two independent variables (X and Z). I want to compute R^2. Is that built into the model somehow? If not, any ideas on how to compute it? Here's the model I have:

multiRegModel <- mxModel("Multiple Regression, All Variables",
type="RAM",
manifestVars=c("x", "y", "z"),
# variance paths
mxPath(
from=c("x", "y", "z"),
arrows=2,
free=TRUE,
values = c(.5, .5, .5),
labels=c("varx", "residual", "varz")
),

I'm not sure the best way to get confidence intervals for my estimates. I'm doing a bifactor model with a large dataset (HRS) with lots of missing data, and the mxCI are taking impossibly long (days long).

Anyone have suggestions? Would bootstrapping be faster? If so, what would that script look like? (I've never bootstrapped before)