Nothing
library(OpenMx)
mxOption(NULL, "Default optimizer", "SLSQP")
if (mxOption(NULL, 'Default optimizer') != "SLSQP") stop("SKIP")
#print(mxOption(NULL, "Default optimizer"))
resVars <- mxPath( from=c("x1","x2","x3","x4","x5"), arrows=2,
free=TRUE, values = 1,
labels=c("residual","residual","residual","residual","residual") )
latVars <- mxPath( from=c("intercept","slope"), arrows=2, connect="unique.pairs",
free=c(TRUE,FALSE,TRUE), values=c(1,0,1), labels=c("vari","cov","vars"))
intLoads <- mxPath( from="intercept", to=c("x1","x2","x3","x4","x5"), arrows=1,
free=FALSE, values=1 )
sloLoads <- mxPath( from="slope", to=c("x1","x2","x3","x4","x5"), arrows=1,
free=FALSE, values=seq(-2,2) )
manMeans <- mxPath( from="one", to=c("x1","x2","x3","x4","x5"), arrows=1,
free=FALSE, values=0)
latMeans <- mxPath( from="one", to=c("intercept", "slope"), arrows=1,
free=FALSE, values=0, labels=c("meani","means") )
growthCurveModel <- mxModel("Linear Growth Curve Model Path Specification",
type="RAM",
manifestVars=c("x1","x2","x3","x4","x5"),
latentVars=c("intercept","slope"),
resVars, latVars, intLoads, sloLoads,
manMeans, latMeans)
result <- expand.grid(rep=1:25000, adj=c(TRUE,FALSE), trueSvar=c(0,.3,.6),
interval=c(.95),
lbound=NA, val=NA, ubound=NA, retries=NA)
if (0) {
load("/tmp/lgc-sim.rda")
}
bounds <- c('lbound','ubound')
for (rx in 1:nrow(result)) {
#for (rx in which(is.na(result$lbound))) {
set.seed(result[rx,'rep'])
true.svar <- result[rx,'trueSvar']
ci.adj <- result[rx,'adj']
growthCurveModel$S$values['slope','slope'] <- true.svar
dset <- mxGenerateData(growthCurveModel, nrows = 150)
m1 <- mxModel(growthCurveModel,
mxData(cov(dset), 'cov', colMeans(dset), nrow(dset)))
m1$S$values['slope','slope'] <- .5
if (ci.adj) {
m1$S$lbound['slope','slope'] <- 0
} else {
m1$S$lbound['slope','slope'] <- NA
}
m1 <- mxModel(m1, mxCI('vars', boundAdj = ci.adj, interval=result[rx,'interval']))
plan <- mxComputeSequence(list(
GD=mxComputeGradientDescent(),
CI=mxComputeConfidenceInterval(
fitfunction="fitfunction",
constraintType='ineq',
plan=mxComputeTryHard(
maxRetries=50L, scale=.05,
plan=mxComputeGradientDescent(nudgeZeroStarts = FALSE,
maxMajorIter = 150L)))))
m1 <- mxModel(m1, plan)
m1 <- try(mxRun(m1, intervals=TRUE, suppressWarnings=TRUE, silent=TRUE))
if (is(m1, "try-error")) {
print(paste("optimizer failed on", rx)) #52128
next
}
detail <- m1$compute$steps[['CI']]$output$detail
ci <- m1$output$confidenceIntervals
result[rx,bounds] <- ci[1,bounds]
result[rx,'val'] <- ci[1,'estimate']
result[rx,'retries'] <- m1$compute$steps[['CI']]$plan$debug$retries
if (rx %% 1000 == 0) {
print(rx)
save(result, file="/tmp/lgc-sim.rda")
}
}
result$region <- NA
result[result[,'lbound'] <= result[,'trueSvar'] &
result[,'trueSvar'] <= result[,'ubound'],'region'] <- 'M'
result[result[,'lbound'] > result[,'trueSvar'], 'region'] <- 'L'
result[result[,'ubound'] < result[,'trueSvar'], 'region'] <- 'U'
library(plyr)
resultSummary <- ddply(result, .(adj, trueSvar, interval), function(sim) {
c(M=sum(sim$region == 'M'), L=sum(sim$region== 'L'), U=sum(sim$region=='U')) /
nrow(sim)
})
print(resultSummary)
# how to estimate the Monte Carlo standard error?
if(0) {
save(resultSummary, file="~/vcu/ci/lgc-sim.rda")
}
if(0) {
lgc.sim <- result
save(lgc.sim, file="~/vcu/ci/lgc-sim.rda")
}
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