Nothing
library(OpenMx)
if (mxOption(NULL, 'Default optimizer') != "SLSQP") stop("SKIP")
mxOption(key="feasibility tolerance", value = 1e-6)
#mxOption(NULL, "Default optimizer", "SLSQP")
#print(mxOption(NULL, "Default optimizer"))
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=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=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") )
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(adj=c(TRUE,FALSE), val=seq(-.1, .1, 0.002),
lbound=NA, ubound=NA, retries=NA)
bounds <- c('lbound','ubound')
for (rx in 1:nrow(result)) {
# rx=125
growthCurveModel$S$values['slope','slope'] <- result[rx,'val']
cov1 <- mxGetExpected(growthCurveModel, "covariance")
if (any(eigen(cov1)$val < 0)) next
mvec <- as.vector(mxGetExpected(growthCurveModel, "means"))
names(mvec) <- paste0('x',1:5)
m1 <- mxModel(growthCurveModel,
mxData(cov1, 'cov', mvec, 150))
m1$S$values['slope','slope'] <- .5
if (result[rx,'adj']) {
m1$S$lbound['slope','slope'] <- 0
} else {
m1$S$lbound['slope','slope'] <- NA
}
m1 <- mxModel(m1, mxCI('vars', boundAdj = result[rx,'adj']))
m1 <- mxRun(m1, intervals=TRUE, suppressWarnings=TRUE, silent=TRUE)
detail <- m1$compute$steps[['CI']]$output$detail
ci <- m1$output$confidenceIntervals
result[rx,bounds] <- ci[1,bounds]
result[rx,'retries'] <- m1$compute$steps[['CI']]$plan$debug$retries
}
omxCheckCloseEnough(table(is.na(result[!result$adj,'lbound']))[[1]],
100,4)
omxCheckCloseEnough(sum(diff(result[!result$adj,'lbound']) > 0, na.rm = TRUE),
99, 5)
omxCheckCloseEnough(table(is.na(result[!result$adj,'ubound']))[[1]],
100,1)
omxCheckCloseEnough(sum(diff(result[!result$adj,'ubound']) > 0, na.rm = TRUE),
99, 1)
omxCheckEquals(fivenum(result[!result$adj, 'retries'])[c(1,3)],
c(2,2))
omxCheckCloseEnough(table(is.na(result[result$adj,'lbound']))[[1]],
100,1)
omxCheckCloseEnough(sum(diff(result[result$adj,'lbound']) > 0, na.rm = TRUE),
38, 3)
omxCheckCloseEnough(table(is.na(result[result$adj,'ubound']))[[1]],
101,1)
omxCheckCloseEnough(sum(diff(result[result$adj,'ubound']) > 0, na.rm = TRUE),
98, 5)
omxCheckEquals(fivenum(result[result$adj, 'retries'])[c(1,3)],
c(2,2))
if (0) {
library(ggplot2)
library(gtable)
library(grid)
p1 <- ggplot(result) + geom_ribbon(aes(x=val, ymin=lbound, ymax=ubound, fill=adj), alpha=.3) + ylim(-.02,.1)
p2 <- ggplot() + geom_point(data=result[,c('val','retries','adj')], aes(x=val, y=retries, color=adj))
pair <- rbind(ggplotGrob(p1), ggplotGrob(p2), size="first")
grid.newpage()
grid.draw(pair)
}
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