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# echo 0 > /proc/self/coredump_filter # normally 023
# R --vanilla --no-save -f models/failing/bock-aitkin-1981.R
# R -d gdb --vanilla --no-save -f models/failing/bock-aitkin-1981.R
#options(error = browser)
require(OpenMx)
require(rpf)
#library(mvtnorm)
options(mxCondenseMatrixSlots=TRUE) #<--For regression testing
set.seed(7)
correct.LL <- 48990.17
numItems <- 30
numPeople <- 500
maxDim <- 2
items <- vector("list", numItems)
correct <- vector("list", numItems)
for (ix in 1:numItems) {
items[[ix]] <- rpf.grm(factors=2)
correct[[ix]] <- rpf.rparam(items[[ix]], version=1)
}
correct.mat <- simplify2array(correct)
correct.mat[1,1:10] <- 0
correct.mat[2,20:30] <- 0
if (0) {
startpar <- sapply(items, rpf.rparam, version=1)
} else {
startpar <- matrix(c(1.4, 1, 0), nrow=3, ncol=numItems)
}
startpar[correct.mat==0] <- 0
maxParam <- max(vapply(items, function(i) rpf.numParam(i), 0))
maxOutcomes <- max(vapply(items, function(i) i$outcomes, 0))
data.g1 <- rpf.sample(numPeople, items, correct.mat)
data.g2 <- rpf.sample(numPeople, items, correct.mat, mean=c(-.5,.8), cov=matrix(c(2,.5,.5,2),nrow=2))
data.g3 <- rpf.sample(numPeople, items, correct.mat, mean=c(-.1,-.8), cov=matrix(c(.9,-.5,-.5,.9),nrow=2))
if (0) {
# for flexMIRT, write CSV
write.table(sapply(data.g1, unclass)-1, file="2d-mg-g1.csv", quote=FALSE, row.names=FALSE, col.names=FALSE)
write.table(sapply(data.g2, unclass)-1, file="2d-mg-g2.csv", quote=FALSE, row.names=FALSE, col.names=FALSE)
write.table(sapply(data.g3, unclass)-1, file="2d-mg-g3.csv", quote=FALSE, row.names=FALSE, col.names=FALSE)
fm <- read.flexmirt("/home/joshua/irt/ifa-2d-mg/2d-mg-prm.txt")
}
mkgroup <- function(model.name, data, latent.free) {
ip.mat <- mxMatrix(name="item", values=startpar, free=TRUE)
colnames(ip.mat) <- colnames(data)
rownames(ip.mat) <- c(paste('f', 1:2, sep=""), 'b')
ip.mat$free[correct.mat==0] <- FALSE
for (ix in 1:numItems) {
for (px in 1:3) {
name <- paste(c('p',ix,',',px), collapse='')
ip.mat$labels[px,ix] <- name
}
}
m.mat <- mxMatrix(name="mean", nrow=1, ncol=2, values=0)
colnames(m.mat) <- paste('f', 1:2, sep="")
cov.mat <- mxMatrix(name="cov", nrow=2, ncol=2, values=diag(2))
dimnames(cov.mat) <- list(paste('f', 1:2, sep=""), paste('f', 1:2, sep=""))
mean <- "mean"
cov <- "cov"
if (latent.free) {
lm <- paste(model.name, "latent", sep="")
mean <- paste(lm, "mean", sep=".")
cov <- paste(lm, "cov", sep=".")
}
m1 <- mxModel(model=model.name,
ip.mat, m.mat, cov.mat,
mxData(observed=data, type="raw"),
mxExpectationBA81(ItemSpec=items, mean=mean, cov=cov,
qpoints=21, qwidth=5, verbose=0L),
mxFitFunctionML())
m1
}
mklatent <- function(name) {
mMat <- mxMatrix(nrow=1, ncol=2, free=T, values=0, name="mean")
colnames(mMat) <- paste('f', 1:2, sep="")
cov <- mxMatrix(type="Symm", nrow=2, ncol=2, free=T, values=diag(2), name="cov")
dimnames(cov) <- list(paste('f', 1:2, sep=""), paste('f', 1:2, sep=""))
mask <- c(FALSE,TRUE,TRUE,FALSE)
cov$labels[mask] <- paste(name, "cov",sep="")
mxModel(paste(name, "latent", sep=""),
mxDataDynamic('cov', expectation=paste(name, "expectation", sep=".")),
mMat, cov,
mxExpectationNormal(covariance="cov", means="mean"),
mxFitFunctionML()
)
}
groups <- paste("g", 1:3, sep="")
latent <- mxModel("latent", mxFitFunctionMultigroup(paste0(groups[-1], "latent")))
g2.latent <- mklatent("g2")
g3.latent <- mklatent("g3")
latent.vargroup <- apply(expand.grid(paste(groups[-1], "latent", sep=""), c('mean','cov')),
1, paste, collapse='.')
latent.plan <- NULL # need a plan for latent distribution parameters
if (0) {
# Copy latent distribution parameters from current estimates without transformation.
latent.plan <- mxComputeSequence(list(mxComputeOnce(paste(groups, 'expectation', sep='.')),
mxComputeOnce(paste(groups, 'expectation', sep='.'),
"latentDistribution", "copy"), # c('mean','covariance')
mxComputeOnce('fitfunction', "set-starting-values")),
freeSet=latent.vargroup)
} else {
# Obtain latent distribution parameters via mxExpectationNormal.
# This permits equality constraints (and potentially more complex latent structure).
latent.plan <- mxComputeGradientDescent(latent.vargroup, fitfunction="latent.fitfunction")
mask <- c(FALSE, TRUE, TRUE, FALSE)
g2.latent$cov$labels[mask] <- 'eq1'
g3.latent$cov$labels[mask] <- 'eq1'
}
g1 <- mkgroup("g1", data.g1, FALSE)
g2 <- mkgroup("g2", data.g2, TRUE)
g3 <- mkgroup("g3", data.g3, TRUE)
grpModel <- mxModel(model="groupModel", g1, g2, g3, g2.latent, g3.latent, latent,
mxFitFunctionMultigroup(paste(groups, "fitfunction", sep=".")),
mxComputeEM(paste(groups, 'expectation', sep='.'), 'scores',
mxComputeSequence(list(
mxComputeNewtonRaphson(paste(groups, 'item', sep=".")),
latent.plan))))
grpModel <- mxRun(grpModel, silent=TRUE)
omxCheckCloseEnough(grpModel$output$minimum, correct.LL, .01)
omxCheckCloseEnough(c(grpModel$submodels$g2latent$mean$values), c(-.516, .708), .01)
omxCheckCloseEnough(c(grpModel$submodels$g2latent$cov$values), c(2.114, -.279,-.279, 2.259), .01)
omxCheckCloseEnough(c(grpModel$submodels$g3latent$mean$values), c(-.027, -.823), .01)
omxCheckCloseEnough(c(grpModel$submodels$g3latent$cov$values), c(.779, -.279, -.279, .738), .01)
emstat <- grpModel$compute$output
if (0) {
# TODO too inconsistent
omxCheckCloseEnough(emstat$totalMstep, 222, 10)
}
grp1 <- as.IFAgroup(grpModel$g1)
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