profile.rma.uni <- function(fitted,
xlim, ylim, steps=20, lltol=1e-03, progbar=TRUE, parallel="no", ncpus=1, cl, plot=TRUE, ...) {
mstyle <- .get.mstyle()
.chkclass(class(fitted), must="rma.uni", notav=c("rma.ls", "rma.uni.selmodel", "rma.gen"))
if (is.element(fitted$method, c("FE","EE","CE")))
stop(mstyle$stop("Cannot profile tau^2 parameter for equal/fixed-effects models."))
x <- fitted
if (anyNA(steps))
stop(mstyle$stop("No missing values allowed in 'steps' argument."))
if (length(steps) >= 2L) {
if (missing(xlim))
xlim <- range(steps)
stepseq <- TRUE
} else {
if (steps < 2)
stop(mstyle$stop("Argument 'steps' must be >= 2."))
stepseq <- FALSE
}
parallel <- match.arg(parallel, c("no", "snow", "multicore"))
if (parallel == "no" && ncpus > 1)
parallel <- "snow"
if (missing(cl))
cl <- NULL
if (!is.null(cl) && inherits(cl, "SOCKcluster")) {
parallel <- "snow"
ncpus <- length(cl)
}
if (parallel == "snow" && ncpus < 2)
parallel <- "no"
if (parallel == "snow" || parallel == "multicore") {
if (!requireNamespace("parallel", quietly=TRUE))
stop(mstyle$stop("Please install the 'parallel' package for parallel processing."))
ncpus <- as.integer(ncpus)
if (ncpus < 1L)
stop(mstyle$stop("Argument 'ncpus' must be >= 1."))
}
if (!progbar) {
pbo <- pbapply::pboptions(type="none")
on.exit(pbapply::pboptions(pbo), add=TRUE)
}
ddd <- list(...)
if (.isTRUE(ddd$time))
time.start <- proc.time()
pred <- isTRUE(ddd$pred)
blup <- isTRUE(ddd$blup)
newmods <- NULL
if (pred) {
if (!is.null(ddd$newmods))
newmods <- ddd$newmods
### test if predict() works with the given newmods (and to get slab for [a])
predtest <- predict(x, newmods=newmods)
if (length(predtest$pred) == 0L)
stop(mstyle$stop("Cannot compute predicted values."))
}
#########################################################################
if (missing(xlim) || is.null(xlim)) {
### if the user has not specified xlim, set it automatically
vc.ci <- try(suppressWarnings(confint(x)), silent=TRUE)
if (inherits(vc.ci, "try-error")) {
vc.lb <- NA_real_
vc.ub <- NA_real_
} else {
### min() and max() so the actual value is within the xlim bounds
### note: could still get NAs for the bounds if the CI is the empty set
vc.lb <- min(x$tau2, vc.ci$random[1,2])
vc.ub <- max(0.1, x$tau2, vc.ci$random[1,3]) # if CI is equal to null set, then this still gives vc.ub = 0.1
}
if (is.na(vc.lb) || is.na(vc.ub)) {
### if the CI method fails, try a Wald-type CI for tau^2
vc.lb <- max( 0, x$tau2 - qnorm(0.995) * x$se.tau2)
vc.ub <- max(0.1, x$tau2 + qnorm(0.995) * x$se.tau2)
}
if (is.na(vc.lb) || is.na(vc.ub)) {
### if this still results in NA bounds, use simple method
vc.lb <- max( 0, x$tau2/4)
vc.ub <- max(0.1, x$tau2*4)
}
### if that fails, throw an error
if (is.na(vc.lb) || is.na(vc.ub))
stop(mstyle$stop("Cannot set 'xlim' automatically. Please set this argument manually."))
xlim <- c(vc.lb, vc.ub)
if (.isTRUE(ddd$sqrt))
xlim <- sqrt(xlim)
} else {
if (length(xlim) != 2L)
stop(mstyle$stop("Argument 'xlim' should be a vector of length 2."))
xlim <- sort(xlim)
### note: if sqrt=TRUE, then xlim is assumed to be given in terms of tau
}
if (stepseq) {
vcs <- steps
} else {
vcs <- seq(xlim[1], xlim[2], length.out=steps)
}
#return(vcs)
if (length(vcs) <= 1L)
stop(mstyle$stop("Cannot set 'xlim' automatically. Please set this argument manually."))
### if sqrt=TRUE, then the sequence of vcs are tau values, so square them for the actual profiling
if (.isTRUE(ddd$sqrt))
vcs <- vcs^2
if (parallel == "no")
res <- pbapply::pblapply(vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods)
if (parallel == "multicore")
res <- pbapply::pblapply(vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, cl=ncpus, pred=pred, blup=blup, newmods=newmods)
#res <- parallel::mclapply(vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods, mc.cores=ncpus)
if (parallel == "snow") {
if (is.null(cl)) {
cl <- parallel::makePSOCKcluster(ncpus)
on.exit(parallel::stopCluster(cl), add=TRUE)
}
if (.isTRUE(ddd$LB)) {
res <- parallel::parLapplyLB(cl, vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods)
#res <- parallel::clusterApplyLB(cl, vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods)
#res <- parallel::clusterMap(cl, .profile.rma.uni, vcs, MoreArgs=list(obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods), .scheduling = "dynamic")
} else {
res <- pbapply::pblapply(vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods, cl=cl)
#res <- parallel::parLapply(cl, vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods)
#res <- parallel::clusterApply(cl, vcs, .profile.rma.uni, obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods)
#res <- parallel::clusterMap(cl, .profile.rma.uni, vcs, MoreArgs=list(obj=x, parallel=parallel, profile=TRUE, pred=pred, blup=blup, newmods=newmods))
}
}
### if sqrt=TRUE, then transform the tau^2 values back to tau values
if (.isTRUE(ddd$sqrt)) {
vcs <- sqrt(vcs)
vc <- sqrt(x$tau2)
} else {
vc <- x$tau2
}
lls <- sapply(res, function(x) x$ll)
beta <- do.call(rbind, lapply(res, function(x) t(x$beta)))
ci.lb <- do.call(rbind, lapply(res, function(x) t(x$ci.lb)))
ci.ub <- do.call(rbind, lapply(res, function(x) t(x$ci.ub)))
beta <- data.frame(beta)
ci.lb <- data.frame(ci.lb)
ci.ub <- data.frame(ci.ub)
names(beta) <- rownames(x$beta)
names(ci.lb) <- rownames(x$beta)
names(ci.ub) <- rownames(x$beta)
#########################################################################
maxll <- c(logLik(x))
if (x$method %in% c("ML","REML") && any(lls >= maxll + lltol, na.rm=TRUE))
warning(mstyle$warning("At least one profiled log-likelihood value is larger than the log-likelihood of the fitted model."), call.=FALSE)
if (all(is.na(lls)))
warning(mstyle$warning("All model fits failed. Cannot draw profile likelihood plot."), call.=FALSE)
if (.isTRUE(ddd$exp)) {
lls <- exp(lls)
maxll <- exp(maxll)
}
if (missing(ylim)) {
if (any(is.finite(lls))) {
if (xlim[1] <= vc && xlim[2] >= vc) {
ylim <- range(c(maxll,lls[is.finite(lls)]), na.rm=TRUE)
} else {
ylim <- range(lls[is.finite(lls)], na.rm=TRUE)
}
} else {
ylim <- rep(maxll, 2L)
}
if (!.isTRUE(ddd$exp))
ylim <- ylim + c(-0.1, 0.1)
} else {
if (length(ylim) != 2L)
stop(mstyle$stop("Argument 'ylim' should be a vector of length 2."))
ylim <- sort(ylim)
}
if (.isTRUE(ddd$sqrt)) {
xlab <- expression(paste(tau, " Value"))
title <- expression(paste("Profile Plot for ", tau))
} else {
xlab <- expression(paste(tau^2, " Value"))
title <- expression(paste("Profile Plot for ", tau^2))
}
sav <- list(tau2=vcs, ll=lls, beta=beta, ci.lb=ci.lb, ci.ub=ci.ub, comps=1, xlim=xlim, ylim=ylim, method=x$method, vc=vc, maxll=maxll, xlab=xlab, title=title, exp=ddd$exp, sqrt=ddd$sqrt)
class(sav) <- "profile.rma"
if (.isTRUE(ddd$sqrt))
names(sav)[1] <- "tau"
sav$I2 <- sapply(res, function(x) x$I2)
sav$H2 <- sapply(res, function(x) x$H2)
if (pred) {
sav$pred <- do.call(cbind, lapply(res, function(x) x$pred)) # use do.call(cbind, lapply()) instead of sapply() to always get a matrix, even when predicting a single value
sav$pred.ci.lb <- do.call(cbind, lapply(res, function(x) x$pred.ci.lb))
sav$pred.ci.ub <- do.call(cbind, lapply(res, function(x) x$pred.ci.ub))
sav$pred.pi.lb <- do.call(cbind, lapply(res, function(x) x$pred.pi.lb))
sav$pred.pi.ub <- do.call(cbind, lapply(res, function(x) x$pred.pi.ub))
rownames(sav$pred) <- rownames(sav$pred.ci.lb) <- rownames(sav$pred.ci.ub) <- rownames(sav$pred.pi.lb) <- rownames(sav$pred.pi.ub) <- predtest$slab # [a]
}
if (blup) {
sav$blup <- sapply(res, function(x) x$blup)
sav$blup.se <- sapply(res, function(x) x$blup.se)
sav$blup.pi.lb <- sapply(res, function(x) x$blup.pi.lb)
sav$blup.pi.ub <- sapply(res, function(x) x$blup.pi.ub)
rownames(sav$blup) <- x$slab[x$not.na]
rownames(sav$blup.se) <- x$slab[x$not.na]
rownames(sav$blup.pi.lb) <- x$slab[x$not.na]
rownames(sav$blup.pi.ub) <- x$slab[x$not.na]
}
#########################################################################
if (plot)
plot(sav, ...)
#########################################################################
if (.isTRUE(ddd$time)) {
time.end <- proc.time()
.print.time(unname(time.end - time.start)[3])
}
invisible(sav)
}
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