OpenMx General Help
mxExpectationGREML error
> require(OpenMx)
> mxVersion()
OpenMx version: 2.0.1.4157
R version: R version 3.1.2 (2014-10-31)
Platform: x86_64-apple-darwin10.8.0
Default optimiser: NPSOL
> # the following are the first three commands from the Examples from
> # the documentation to mxExpectationGREML
> dat <- cbind(rnorm(100),rep(1,100))
> colnames(dat) <- c("y","x")
>
> ge <- mxExpectationGREML(V="V",yvars="y",Xvars=list("X"),addOnes=FALSE)
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speed issue with mxRun
I am writing on behalf of a user of our local cluster.
The OpenMX install is fairly recent (just with source('http://openmx.psyc.virginia.edu/getOpenMx.R' in R). The script in question is rather long and starts with loading OpenMX:
require(OpenMx)
and subsequently snowfall:
library(snowfall)
sfInit(parallel = T, cpus = 64)
sfLibrary(OpenMx)
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Specifying Unique mxFitFunction
I am new to 2.0 and am playing around with specifying a non-standard fit function, and have hit some road bumps in trying to start with specifying the ML fit through mxFitFunctionAlgebra. Attached is the reproducible script. Error message is:
Error: The job for model 'One Factor' exited abnormally with the error message: MxComputeGradientDescent: fitfunction One Factor.fitfunction is not finite ()
If mxFitFunctionAlgebra() is used, does it have to include a gradient specification?
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Restricted Model Cholesky; how to drop/fix parameters?
I'm a graduate student currently learning OpenMX, and I am unfortunately still a novice at OpenMX and genetic modeling in general. I've so far successfully run multivariate Cholesky analyses with OpenMX in R studio, and gotten interpretable data.
However, I am now trying to drop parameters on my current Cholesky model, to get a "best fit" reduced model for my data. I am having trouble understanding how I might do this; I understand it would involve fixing certain parameters at zero, but given my code, I am completely at a loss as to how I might go about this.
OpenMx performance on CentOS server
when I run the demo script AlternativeApproaches.R on my computer, the running cpu is around 100% and completed in 10 seconds.
input:
time Rscript AlternativeApproaches.R
output:
real 0m2.431s
user 0m3.752s
sys 0m0.093s
But when I run the script on my company CentOS server, the running cpu is above 500% and completed in more than 15 minutes.
input:
time Rscript AlternativeApproaches.R
output:
real 2m39.438s
user 15m6.753s
sys 0m2.392s
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SEM, Binary and Categorical Data in independent variables
The data set has (please see example diagram)
+ 3 continuous variables(X1, X2, X3) for a latent variable (intercept)
+ 3 mediators (one binary X8 and two categorical variables X6, X7)
+ 1 dependent variable (X4).
This coding was based on the post about Categorical Data in both independent and dependent variables http://openmx.psyc.virginia.edu/thread/3883
When I run SEM.R model I have following error message.
Error in mxRAMObjective(A = "A", S = "S", M = "M", thresholds = "Threshold") :
Access Kalman filter generated process state estimates online (mxExpectationStateSpace)
Background of data set in OpenMx
http://openmx.psyc.virginia.edu/docs/OpenMx/latest/_static/Rdoc/dzoData.html
Would anyone please provide more background about this data? The sample size seems to indicate that it is not simulated data, but no descriptions are given for the variables.
I would appreciate it. Thanks.
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Problem with definition variables with state space expectation?
data(demoOneFactor)
nvar <- ncol(demoOneFactor)
varnames <- colnames(demoOneFactor)
demoOneFactorInputs <- cbind(demoOneFactor, V1=rnorm(nrow(demoOneFactor)))
ssModel <- mxModel(model="State Space Inputs Manual Example",
mxMatrix("Full", 1, 1, TRUE, .3, name="A"),
mxMatrix("Full", 1, 1, TRUE, values=1, name="B"),
identifying bivariate outliers
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)
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