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# MPLUS: TWO-LEVEL CFA WITH CONTINUOUS FACTOR INDICATORS AND COVARIATES
# See https://www.statmodel.com/usersguide/chapter9.shtml
library(OpenMx)
set.seed(1)
ex96 <- suppressWarnings(try(read.table("models/nightly/data/ex9.6.dat")))
if (is(ex96, "try-error")) ex96 <- read.table("data/ex9.6.dat")
ex96$V8 <- as.integer(ex96$V8)
bData <- ex96[!duplicated(ex96$V8), c('V7', 'V8')]
colnames(bData) <- c('w', 'clusterID')
wData <- ex96[,-match(c('V7'), colnames(ex96))]
colnames(wData) <- c(paste0('y', 1:4), paste0('x', 1:2), 'clusterID')
bModel <- mxModel(
'between', type="RAM",
mxData(type="raw", observed=bData, primaryKey="clusterID"),
latentVars = c("lw", "fb"),
mxPath("one", "lw", labels="data.w", free=FALSE),
mxPath("fb", arrows=2, labels="psiB"),
mxPath("lw", 'fb', labels="phi1"))
wModel <- mxModel(
'within', type="RAM", bModel,
mxData(type="raw", observed=wData),
manifestVars = paste0('y', 1:4),
latentVars = c('fw', paste0("xe", 1:2)),
mxPath("one", paste0('y', 1:4), values=runif(4),
labels=paste0("gam0", 1:4)),
mxPath("one", paste0('xe', 1:2),
labels=paste0('data.x',1:2), free=FALSE),
mxPath(paste0('xe', 1:2), "fw",
labels=paste0('gam', 1:2, '1')),
mxPath('fw', arrows=2, values=1.1, labels="varFW"),
mxPath('fw', paste0('y', 1:4), free=c(FALSE, rep(TRUE, 3)),
values=c(1,runif(3)), labels=paste0("loadW", 1:4)),
mxPath('between.fb', paste0('y', 1:4), values=c(1,runif(3)),
free=c(FALSE, rep(TRUE, 3)), labels=paste0("loadB", 1:4),
joinKey="clusterID"),
mxPath(paste0('y', 1:4), arrows=2, values=rlnorm(4),
labels=paste0("thetaW", 1:4)))
mle <- structure(c(
0.9989, 0.9948, 1.0171, 0.9809, 0.9475, 1.0699,
1.0139, 0.9799, -0.0829, -0.0771, -0.0449, -0.0299, 0.9728, 0.5105,
0.9595, 0.9238, 0.9489, 0.361, 0.3445),
.Names = c("loadW2", "loadW3", "loadW4", "thetaW1",
"thetaW2", "thetaW3", "thetaW4", "varFW",
"gam01", "gam02", "gam03", "gam04", "gam11", "gam21",
"loadB2", "loadB3", "loadB4", "psiB", "phi1"))
if (1) {
pt1 <- omxSetParameters(wModel, labels=names(mle), values=mle)
# pt1$expectation$.forceSingleGroup <- TRUE
# pt1$expectation$.rampart <- 0L
plan <- mxComputeSequence(list(
mxComputeOnce('fitfunction', 'fit'),
# mxComputeNumericDeriv(checkGradient=FALSE,
# hessian=FALSE, iterations=2),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
pt1 <- mxRun(mxModel(pt1, plan))
omxCheckCloseEnough(pt1$output$fit, 13088.373, 1e-2)
}
if (1) {
# wModel <- mxRun(mxModel(wModel, mxComputeGradientDescent(verbose=2L)))
wModel <- mxRun(wModel)
summary(wModel)
omxCheckCloseEnough(wModel$output$fit, 13088.373, 1e-2)
omxCheckCloseEnough(mle[names(coef(wModel))], coef(wModel), 1e-3)
omxCheckCloseEnough(wModel$expectation$debug$rampartUsage, 890)
} else {
options(width=120)
plan <- mxComputeSequence(list(
mxComputeOnce('fitfunction', 'fit'),
mxComputeNumericDeriv(checkGradient=FALSE,
hessian=FALSE, iterations=2),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
wModel$expectation$.rampartCycleLimit <- 2L
# wModel$expectation$scaleOverride <- c(6, 1)
rotated <- mxRun(mxModel(wModel, plan))
wModel$expectation$.rampartCycleLimit <- 0L
square <- mxRun(mxModel(wModel, plan))
ex <- rotated$expectation
eo <- ex$output
ed <- ex$debug
print(ed$rampartUsage)
print(abs(rotated$output$fit - square$output$fit))
print(max(abs(rotated$output$gradient - square$output$gradient)))
}
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