Sorry for posting this again.

I am trying to adapt the sample script on FIML estimation for missing data. In the script below (from OpenMx documentation) how do I change the component of the "firstHalfCalc" expression of the likelihood function (log2pi %*% 2) to represent the number of variables that has nonmissing data for that observation? Will the sample code work for all situations without modification?

Sorry if this is a silly question. I am quite new to OpenMx.

simulate data

require(MASS)
set.seed(200)
rs <- .5
xy <- mvrnorm (1000, c(0,0), matrix(c(1, rs, rs, 1), nrow=2, ncol=2))

create a data frame

testData <- as.data.frame(xy)
testVars <- c('X','Y')
names(testData) <- testVars
summary(testData)
cov(testData)

model specification

bivCorModel <- mxModel(name="FIML BivCor",
mxData(
observed=testData,
type="raw",
),
mxMatrix(
type="Full",
nrow=1,
ncol=2,
free=TRUE,
values=c(0,0),
name="expMean"
),
mxMatrix(
type="Lower",
nrow=2,
ncol=2,
free=TRUE,
values=.2,
name="Chol"
),
mxAlgebra(
expression=Chol %*% t(Chol),
name="expCov",
)
)

filtering

bivCorModel <- mxModel(model=bivCorModel,
mxAlgebra(
expression=omxSelectRowsAndCols(expCov, existenceVector),
name="filteredExpCov",
),
mxAlgebra(
expression=omxSelectCols(expMean, existenceVector),
name="filteredExpMean",
)
)

row objective specification

bivCorModel <- mxModel(model=bivCorModel,
mxMatrix("Full", 1, 1, values = log(2pi), name = "log2pi"),
mxAlgebra(
expression=log2pi %% 2 + log(det(filteredExpCov)),
name ="firstHalfCalc",
),
mxAlgebra(
expression=(filteredDataRow - filteredExpMean) %&% solve(filteredExpCov) ,
name = "secondHalfCalc",
),
mxAlgebra(
expression=(firstHalfCalc + secondHalfCalc),
name="rowAlgebra",
),
mxAlgebra(
expression=sum(rowResults),
name = "reduceAlgebra",
),
mxRowObjective(
rowAlgebra='rowAlgebra',
reduceAlgebra='reduceAlgebra',
dimnames=c('X','Y'),
)
)

model fitting

bivCorFit <- mxRun(bivCorModel)