# OpenMx General Help

## matrix logarithm function for mxAlgebra

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## Groups analysis

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## Does OpenMx support multilevel, ordinal/binary outcomes/indicators with sampling weights?

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.

## Obtain number of Objective function evaluations

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?

## modified model

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.

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## Referring to an OpenMx matrix in the OpenMx environment

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

## Computing R squared with missing data

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")

),

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## Confidence Intervals

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

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## OpenMx in the Cloud

Would I need to run OpenMx inside a VM, or is there a server version? Can multiple instances of OpenMx run inside the same VM? or Would we need to run multiple VMs, each for an instant of OpenMx?

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## Genetic correlation equals to 1 or -1 in Bivariate ACE model

I had just done bivariate ACE model for two continuous variables (bivariate correlated factors model). The results turned out to be a little weird as follows:

A1 = 0.25, A2 = 0.09

C1 = 0.12, C2 = 0.08

E1 = 0.62, E2 = 0.82

Rph = -0.24

Ra = -1

Rc = 1

Re = -0.26

We observe Ra is -1, but we personally think it is not reasonable that the two variables are perfectly correlated. Is it possible that there are some problems with the program?

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