library(OpenMx)
#mxOption(NULL, "Default optimizer", "SLSQP")
#print(mxOption(NULL, "Default optimizer"))
if (mxOption(NULL, 'Default optimizer') != "SLSQP") stop("SKIP")
mxOption(key="feasibility tolerance", value = 1e-6)
resVars <- mxPath( from=c("x1","x2","x3","x4","x5"), arrows=2,
free=TRUE, values = c(1,1,1,1,1),
labels=c("residual","residual","residual","residual","residual") )
latVars <- mxPath( from=c("intercept","slope"), arrows=2, connect="unique.pairs",
free=c(TRUE,TRUE,FALSE), values=c(1,0,1),
labels=c("vari","cov","SlopeVarAlg[1,1]"))
intLoads <- mxPath( from="intercept", to=c("x1","x2","x3","x4","x5"), arrows=1,
free=FALSE, values=c(1,1,1,1,1) )
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=runif(5,-.5,.5))
latMeans <- mxPath( from="one", to=c("intercept", "slope"), arrows=1,
free=TRUE, values=runif(2, -.5,.5), labels=c("meani","means") )
SlopeVar <- mxMatrix("Full", 1,1, free=TRUE, values=0, name="SlopeVar")
SlopeVarAlg <- mxAlgebra(-SlopeVar, name="SlopeVarAlg")
growthCurveModel <- mxModel("sym",
type="RAM",
manifestVars=c("x1","x2","x3","x4","x5"),
latentVars=c("intercept","slope"),
resVars, latVars, intLoads, sloLoads,
manMeans, latMeans, SlopeVar, SlopeVarAlg)
result <- expand.grid(sign=c(1,-1), val=seq(-.1, .1, 0.005),
lbound=NA, ubound=NA, retries=NA)
bounds <- c('lbound','ubound')
cov1 <- mxGetExpected(growthCurveModel, "covariance")
mvec <- as.vector(mxGetExpected(growthCurveModel, "means"))
names(mvec) <- paste0('x',1:5)
growthCurveModel <- mxModel(growthCurveModel,
mxData(cov1, 'cov', mvec, 150))
interval <- c(.85,.95)
for (rx in 1:nrow(result)) {
if (result[rx,'sign'] == 1) {
SlopeVarAlg <- mxAlgebra(SlopeVar, name="SlopeVarAlg")
} else {
SlopeVarAlg <- mxAlgebra(-SlopeVar, name="SlopeVarAlg")
}
growthCurveModel <- mxModel(growthCurveModel, SlopeVarAlg)
growthCurveModel$SlopeVar$values[1,1] <- result[rx,'sign'] * result[rx,'val']
gcm <- mxRun(mxModel(growthCurveModel,
mxComputeSequence(list(
mxComputeOnce('expectation'),
mxComputeReportExpectation()))), silent=TRUE)
cov1[,] <- gcm$expectation$output$covariance
mvec[] <- gcm$expectation$output$mean
m1 <- mxModel(growthCurveModel, mxData(cov1, 'cov', mvec, 150))
if (any(eigen(cov1)$val < 0)) next
if (result[rx,'sign'] == 1) {
m1$SlopeVar$lbound[1,1] <- 0
} else {
m1$SlopeVar$ubound[1,1] <- 0
}
m1$SlopeVar$values[1,1] <- result[rx,'sign'] * 0.5
# print(coef(m1))
sinterval <- interval
if (result[rx,'sign'] == -1) {
sinterval <- rev(sinterval)
}
chkpt=FALSE
m1 <- mxModel(m1, mxCI('SlopeVar', 'upper', interval=sinterval[1]))
m2 <- mxModel(m1, mxCI('SlopeVar', 'lower', interval=sinterval[2]))
m1 <- mxRename(m1, paste0('m',rx,'u'))
m2 <- mxRename(m2, paste0('m',rx,'l'))
m1 <- mxRun(m1, intervals=TRUE, suppressWarnings=TRUE, silent=TRUE,
checkpoint=chkpt)
m2 <- mxRun(m2, intervals=TRUE, suppressWarnings=TRUE, silent=TRUE,
checkpoint=chkpt)
detail <- m1$compute$steps[['CI']]$output$detail
ci <- m1$output$confidenceIntervals
result[rx,'ubound'] <- ci[1,'ubound']
ci <- m2$output$confidenceIntervals
result[rx,'lbound'] <- ci[1,'lbound']
result[rx,'retries'] <- (m1$compute$steps[['CI']]$plan$debug$retries +
m2$compute$steps[['CI']]$plan$debug$retries)
}
if (FALSE) {
library(ggplot2)
library(gtable)
library(grid)
presult <- result
presult$sign <- as.factor(presult$sign)
p1 <- ggplot(presult) + geom_ribbon(aes(x=val, ymin=lbound, ymax=ubound, fill=sign), alpha=.3)
p2 <- ggplot() + geom_point(data=presult[,c('val','retries','sign')], aes(x=val, y=retries, color=sign))
pair <- rbind(ggplotGrob(p1), ggplotGrob(p2), size="first")
grid.newpage()
grid.draw(pair)
}
diff <- (result[result$sign == 1,c('lbound','ubound')] +
result[result$sign == -1, c('ubound','lbound')])
omxCheckCloseEnough(sum(is.na(diff)), 0, 2)
omxCheckCloseEnough(max(abs(diff), na.rm=TRUE), 0, 1e-5)
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