OpenMx General Help

How can one estimate the power for the detection of significant rG and rE in multivariate Cholesky models?

Specifically, the analysis employed a trivariate Cholesky (AE providing the best fit, with all significant rGs, and one significant rE). The CIs have been calculated for all estimates. The sample is on the small side: 200 same-sex pairs (half MZ, half DZ) and may have been underpowered for the other smaller rEs but I’m not sure what size effect I had enough power to detect.

Is there a function to put the computed value of all parameters determined by a bracket address (e.g. var[1,1] ) into the appropriate values cell(s) in the model?

Background and use-case: OpenMx allows us to use labels to set the value of a parameter (matrix cell). Here's a RAM model where I insert the value of covXY into the row 2, column1 of the symmetric path (S) matrix:


m1$S
$labels
x y
x "x_with_x" "covXY[1,1]"
y "covXY[1,1]" "y_with_y"



$values
x y
x 1.198632 0.000000
y 0.000000 2.018483


I am trying to parallelize OpenMx on a computing cluster at my university. I'm using Rmpi, and I keep getting the same error:

Error in { : task 18 failed - "job.num is at least 2."
Calls: %dopar% ->
Execution halted
--------------------------------------------------------------------------
mpirun has exited due to process rank 0 with PID 1077 on
node compute-0-11.local exiting improperly. There are two reasons this could occur:

1. this process did not call "init" before exiting, but others in
the job did. This can cause a job to hang indefinitely while it waits

When I run this code (a threshold model for ordinal data), the standard errors are incredibly large but the confidence intervals are relatively narrow. Are any of the results trustworthy? I've tried simplifying the thresholds (using labeling to reduce the number of parameters) but it doesn't help. I'm not sure why this data is so hard to model.

If I treat the ordinal data as if it were quantitative, I have no trouble fitting a common factor model.

This code used to work using "mxAlgebraObjective", but now says I have to use "mxFitFunctionAlgebra" instead of "mxAlgebraObjective".

###############
models_1_v4 <- mxModel("Models_1",
Model_1_t1_v4,
Model_1_t2_v4,
mxAlgebra(
Model_1_t1_v4.objective+
Model_1_t2_v4.objective,
name="multi"),

# mxAlgebraObjective("multi"),

mxFitFunctionAlgebra(algebra = "multi")
)

m1_fit_v4 <- mxRun(models_1_v4)
##################