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(2*pi), 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)

There is an updated script in the repository which uses the sum of the existenceVector so that the number of variables is correctly used for the likelihood.

http://openmx.psyc.virginia.edu/repoview/1/trunk/demo/RowObjectiveFIMLBivariateSaturated.R

However, I would point out that multivariate normal FIML - for continuous, ordinal or binary variables, or any combination thereof, is much more easily accomplished using, e.g.,

mxFIMLObjective(covariance="myModelCovs", means="myMeanMatrix", dimnames = myVarNames, thresholds="myThresholdMatrix")

Sorry for delay in response!