# Sample data
# yi <- c(-0.264,-0.230,0.166,0.173,0.225,0.291,0.309,0.435,0.476,0.617,0.651,0.718,0.740,0.745,0.758,0.922,0.938,0.962,1.522,1.844)
# vi <- c(0.086,0.106,0.055,0.084,0.071,0.078,0.051,0.093,0.149,0.095,0.110,0.054,0.081,0.084,0.087,0.103,0.113,0.083,0.100,0.141)

## library(metaSEM)
## metaSEM.model <- meta(y=yi, v=vi)$mx.model

###### mx model generated from metaSEM
# metaSEM.fit <- mxRun(metaSEM.model)
# summary(metaSEM.fit)


###### mx model specified in OpenMx
# OpenMx.model <- mxModel("test", type="default",
#	mxMatrix("Full", ncol=1, nrow=1, free=F, values=0,   labels="data.vi", name="V"),
#	mxMatrix("Full", ncol=1, nrow=1, free=T, values=0.1, lbound=0.0000001, name="Tau"),
#	mxMatrix("Full", ncol=1, nrow=1, free=T, values=0,   name="M"),
#	mxAlgebra(V+Tau, name="S"),
#	mxFitFunctionML(),mxExpectationNormal(covariance="S", means="M", dimnames=c("yi")),
#	mxData(observed=cbind(yi,vi), type="raw")
#)
# OpenMx.fit <- mxRun(OpenMx.model)
# summary(OpenMx.fit)


####### The above objects are stored in the test2.0.Rdata work space
library(OpenMx)

## Load the work space to get the objects
load("test2.0.txt")

## Run the analysis based on metaSEM
metaSEM.fit <- mxRun(metaSEM.model)
summary(metaSEM.fit)

## Run the analysis based on OpenMx
OpenMx.fit <- mxRun(OpenMx.model)
summary(OpenMx.fit)

## Check their detailed outputs
metaSEM.fit$output

OpenMx.fit$output