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library(OpenMx)
library(testthat)
# NPSOL trips up on WLS for some reason
if (mxOption(NULL,"Default optimizer") == 'NPSOL') stop("SKIP")
mxOption(NULL, "Standard Errors", "No")
data(demoOneFactor)
mkModel <- function() {
manifests <- sample(names(demoOneFactor), ncol(demoOneFactor))
mxModel("One Factor", type="RAM",
manifestVars = manifests,
latentVars = c("G"),
mxPath(from="G", to=manifests,
labels=paste0("loading_",manifests)),
mxPath(from=manifests, arrows=2),
mxPath(from="G", arrows=2, free=FALSE, values=1.0),
mxPath(from="one", to=manifests,
labels=paste0("mean_",manifests)),
mxData(cov(demoOneFactor), type="cov", numObs=500,
means=colMeans(demoOneFactor)))
}
interest <- c(paste0("loading_x",1:5),paste0("mean_x",1:5))
c1 <- c()
for (rep in 1:5) {
fit <- mxRun(mkModel())
if (is.null(c1)) {
c1 <- coef(fit)[interest]
} else {
expect_equivalent(c1 - coef(fit)[interest],
rep(0,10), tolerance=1e-6)
}
}
# -------
numManifestsPerLatent <- 5
mkModel <- function(shuffle, fellner) {
varStruct <- expand.grid(l=1:3, i=1:numManifestsPerLatent)
manifestVars <- paste0('i', apply(varStruct, 1, paste0, collapse=''))
if (shuffle) {
manifestVars <- sample(manifestVars, length(manifestVars))
}
latentVars <- paste0('l', unique(varStruct$l))
allVars <- c(manifestVars, latentVars)
if (shuffle) {
allVars <- sample(allVars, length(allVars))
}
Fval <- diag(length(manifestVars))[,match(allVars, manifestVars)]
Fval[is.na(Fval)] <- 0
ta1 <- mxModel(
model="tangle",
mxMatrix("Full", length(manifestVars), length(allVars),
values=Fval,
dimnames=list(manifestVars, allVars), name="F"),
mxMatrix("Symm", length(allVars), length(allVars),
values=diag(length(allVars)),
free=diag(length(allVars)) == 1,
dimnames=list(allVars, allVars), name="S"),
mxMatrix("Full", length(allVars), length(allVars),
values=0,
dimnames=list(allVars, allVars), name="A"),
mxMatrix("Full", 1, length(allVars),
free=!is.na(match(allVars, manifestVars)),
dimnames=list(NULL, allVars), name="M"),
mxExpectationRAM(M="M"),
mxFitFunctionML(fellner=fellner))
for (lx in 1:length(latentVars)) {
lvar <- paste0('l', lx)
ivar <- paste0(paste0('i', lx), 1:numManifestsPerLatent)
ta1$A$values[ivar, lvar] <- 1
ta1$A$free[ivar, lvar[-1]] <- TRUE
}
ta1$S$free[latentVars, latentVars] <- TRUE
ta1$expectation$.maxDebugGroups <- 10L
ta1
}
set.seed(1)
ta1 <- mxGenerateData(mkModel(FALSE, FALSE), nrow=25, returnModel=TRUE)
for (useFellner in c(TRUE,FALSE)) {
fit1 <- mxRun(mxModel(mkModel(TRUE, useFellner), ta1$data))
fit2 <- mxRun(mxModel(mkModel(FALSE, useFellner), ta1$data))
omxCheckCloseEnough(fit1$output$fit - fit2$output$fit, 0, 1e-8)
if (useFellner) {
omxCheckEquals(names(fit1$expectation$debug$g1$fullMean[1:ncol(fit1$F)]),
paste0('tangle.', colnames(fit1$F)))
omxCheckTrue(length(rle(fit1$expectation$debug$g1$latentFilter)$lengths) !=
length(rle(fit2$expectation$debug$g1$latentFilter)$lengths))
}
}
if (0) {
# F does two things:
# drops out latent variables
# permutes the manifest variables
all(rownames(fit1$F$values) == fit1$expectation$.runDims)
filter <- !apply(fit1$F$values, 2, function(x) all(x==0))
map1 <- apply(fit1$F$values != 0, 2, function(x) ifelse(any(x), which(x), NA))
man1 <- colnames(fit1$F)[filter]
# permit F input order to F output order
all(man1[ match(rownames(fit1$F), man1) ] == rownames(fit1$F))
# permute data to F input order
all(rownames(fit1$F)[ match(man1, rownames(fit1$F)) ] == man1)
all(match(man1, rownames(fit1$F)) == map1[!is.na(map1)])
}
# ----------------------------------------
data("jointdata", package ="OpenMx", verbose= TRUE)
jointData <- jointdata
jointData[,c(2,4,5)] <- mxFactor(jointData[,c(2,4,5)],
levels=list(c(0,1), c(0, 1, 2, 3), c(0, 1, 2)))
mkModel <- function(shuffle, wls) {
set.seed(shuffle)
myData <- jointData
if (shuffle) {
dperm <- sample.int(ncol(myData), ncol(myData))
myData <- myData[, dperm]
}
thresh <- mxMatrix("Full", 3, 3, FALSE, 0, name="Th")
thresh$free[,1] <- c(TRUE, FALSE, FALSE)
thresh$values[,1] <- c(0, NA, NA)
thresh$labels[,1] <- c("z2t1", NA, NA)
thresh$free[,2] <- TRUE
thresh$values[,2] <- c(-1, 0, 1)
thresh$labels[,2] <- c("z4t1", "z4t2", "z4t3")
thresh$free[,3] <- c(TRUE, TRUE, FALSE)
thresh$values[,3] <- c(-1, 1, NA)
thresh$labels[,3] <- c("z5t1", "z5t2", NA)
colnames(thresh) <- paste0('z',c(2,4,5))
manifestVars <- colnames(jointData)
if (shuffle) manifestVars <- sample(manifestVars, length(manifestVars))
latentVars <- 'l1'
allVars <- c(manifestVars, latentVars)
if (shuffle) allVars <- sample(allVars, length(allVars))
Fval <- diag(length(manifestVars))[,match(allVars, manifestVars)]
Fval[is.na(Fval)] <- 0
freeMean <- !sapply(myData, is.factor)[match(allVars,colnames(myData))]
freeMean <- !is.na(freeMean) & freeMean
ta1 <- mxModel(
model="tangle", thresh,
mxData(myData, 'raw'),
mxMatrix("Full", length(manifestVars), length(allVars),
values=Fval,
dimnames=list(manifestVars, allVars), name="F"),
mxMatrix("Symm", length(allVars), length(allVars),
values=diag(length(allVars)),
free=diag(length(allVars)) == 1, lbound=0, ubound=5,
dimnames=list(allVars, allVars), name="S"),
mxMatrix("Full", length(allVars), length(allVars),
values=0,
dimnames=list(allVars, allVars), name="A"),
mxMatrix("Full", 1, length(allVars),
free=freeMean,
dimnames=list(NULL, allVars), name="M"),
mxExpectationRAM(M="M", thresholds="Th"),
mxComputeGradientDescent())
# mxComputeOnce('fitfunction', 'fit'))
lvar <- 'l1'
ivar <- manifestVars
ta1$A$values[ivar, lvar] <- 1
ta1$A$lbound[ivar, lvar] <- 0
ta1$A$free[ivar, lvar] <- TRUE
ta1$S$free[lvar,lvar] <- FALSE
ta1$M$values[1,'z1'] <- c(.1)
if (wls) {
ta1 <- mxModel(ta1, mxFitFunctionWLS('WLS'))
} else {
ta1 <- mxModel(ta1, mxFitFunctionML(jointConditionOn = "continuous"))
}
ta1
}
for (wls in c(FALSE)) {
fit1 <- mxRun(mkModel(0, wls)) # MLE=2683.071 when wls=false
fit2 <- mxRun(mkModel(1, wls))
omxCheckCloseEnough(fit1$output$fit - fit2$output$fit, 0, 1e-6)
fit3 <- mxRun(mkModel(2, wls))
omxCheckCloseEnough(fit1$output$fit - fit3$output$fit, 0, 1e-6)
}
for (sx in 1:4) {
for (wls in c(TRUE)) {
fit1 <- mxRun(mkModel(0, wls)) # MLE=2683.071 when wls=false
fit2 <- mxRun(mxModel(mkModel(sx, wls), fit1$data))
fit3 <- mxRun(mkModel(sx, wls))
fit4 <- mxRun(mxModel(mkModel(0, wls), fit3$data))
# print(c(fit1$output$fit, fit2$output$fit, fit3$output$fit, fit4$output$fit))
omxCheckCloseEnough(fit1$output$fit - fit2$output$fit, 0, 1e-5)
omxCheckCloseEnough(fit1$output$fit - fit3$output$fit, 0, 1e-5)
omxCheckCloseEnough(fit1$output$fit - fit4$output$fit, 0, 1e-5)
}
}
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