2-class growth mixture model: adding time-invariant covariates & viewing class probabilities

I am wondering if I can get some help on a 2-class growth mixture model I'm having trouble with (script and example dataset attached). The model describes a single linear trajectory across 8 time-points (t1-t8). I'm new to OpenMx and I've looked through this forum/other sites but I'm at a loss. I'm trying to (i) add 3 time-invariant covariates ('Sex', 'Age', 'pooled_VA') to the 2-class growth mixture model and (ii) view the class probabilities for each case.

Regarding problem (i), after I run the code below to fit the model with time-invariant covariates, the output I get when I run 'gmmFit$classProbs' is NULL. This surely can't be right but I don't know where I messed up.

classPM <- mxMatrix(type = "Full", nrow = 2, ncol = 4,
free=c(TRUE,FALSE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE), values=1,
labels = c("p11","p21","p12", "p22",
"p13", "p23","p14", "p24"),
name = "weightsM")
classPV <- mxMatrix(nrow=4, ncol=1, labels=c(NA, "Sex", "pooled_VA", "Age"), values=1, name="weightsV")
# class proportions
classP <- mxAlgebra(weightsM %*% weightsV, name="weights")
expec <- mxExpectationMixture(c('Class1', 'Class2'), weights='weights', scale='softmax')
objective <- mxFitFunctionML()
gmm <- mxModel("Growth Mixture Model", dataRaw, class1, class2, classPM, classPV, classP, expec, objective) # specify model to estimate
gmmFit <- mxRun(gmm) # run model
summary(gmmFit) # model parameter results


Regarding problem (ii), when I run the code below to print class probabilities, the output after running 'print(icp)' is "1" for one class and "0" for the other class for every case (see output below). This seems wrong but I don't know where I went wrong.

gmmFit$classProbs
indClassProbs <- function(model, classProbs, round=NA){
# this function takes a mixture model in OpenMx
# and returns the posterior class probabilities
# using Bayes rule, individual person-class likelihoods
# and the model class probability matrix, as described in
# Ramaswamy, Desarbo, Reibstein, and Robinson, 1993
if (missing(model) || !(isS4(model) && is(model, "MxModel"))) {
stop("'model' argument must be an MxModel object")
}
if (missing(classProbs) || !(is.character(classProbs) && length(classProbs) == 1)) {
stop("'classProbs' argument must be a character string")
}
cp <- eval(substitute(mxEval(x, model), list(x = as.symbol(classProbs))))
cp2 <- as.vector(cp)
cps <- diag(length(cp2))
diag(cps) <- cp2
subs <- model@submodels
if(min(dim(cp))!=1)stop("Class probabilities matrix must be a row or column vector.")
if(max(dim(cp))==1)stop("Class probabilities matrix must contain two or more classes.")
of <- function(num) {
return(mxEval(objective, subs[[num]]))
}
rl <- sapply(1:length(names(subs)), of)
raw <- (rl %*% cps)
tot <- 1 / apply(raw, 1, sum)
div <- matrix(rep(tot, length(cp2)), ncol=length(cp2))
icp <- raw * div
if (is.numeric(round)){icp <- round(icp, round)}
return(icp)
}

icp <- indClassProbs(gmmFit, "classProbs")
print(icp)


[,1] [,2]
[1,] 1 0
[2,] 1 0
[3,] 1 0
[4,] 1 0
[5,] 1 0

I also get the above output when I fit the model without covariates using the code below.

# class proportions
classP <- mxMatrix(type="Full", nrow=2, ncol=1, free=c(TRUE,FALSE), values=1, lbound=0.001,labels=c("p1","p2"),name="Props")
# class probabilities
classS <- mxAlgebra(Props%x%(1/sum(Props)), name="classProbs")
algFit <- mxAlgebra(-2*sum(log(classProbs[1,1]%x%Class1.fitfunction+classProbs[2,1]%x%Class2.fitfunction)), name="mixtureObj")
fit <- mxFitFunctionAlgebra("mixtureObj") #This specifies the log likelhood function used to estimate class probabilities
gmm <- mxModel("Growth Mixture Model", dataRaw, class1, class2, classP, classS, algFit, fit) # specify model to estimate
gmmFit <- mxRun(gmm) # run model
summary(gmmFit) # model parameter results


Any assistance would be great. Thanks in advance!