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cooks.distance.rma.mv <- function(model, progbar=FALSE, cluster, reestimate=TRUE, parallel="no", ncpus=1, cl, ...) {
mstyle <- .get.mstyle()
.chkclass(class(model), must="rma.mv")
#if (inherits(model, "robust.rma")) # can compute Cook's distance also for 'robust.rma' objects
# stop(mstyle$stop("Method not available for objects of class \"robust.rma\"."))
na.act <- getOption("na.action")
if (!is.element(na.act, c("na.omit", "na.exclude", "na.fail", "na.pass")))
stop(mstyle$stop("Unknown 'na.action' specified under options()."))
x <- model
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)
}
misscluster <- ifelse(missing(cluster), TRUE, FALSE)
if (misscluster) {
cluster <- seq_len(x$k.all)
} else {
mf <- match.call()
cluster <- .getx("cluster", mf=mf, data=x$data)
}
ddd <- list(...)
.chkdots(ddd, c("btt", "time", "LB"))
btt <- .set.btt(ddd$btt, x$p, int.incl=FALSE, Xnames=colnames(x$X))
m <- length(btt)
if (.isTRUE(ddd$time))
time.start <- proc.time()
#########################################################################
### process cluster variable
### note: cluster variable must be of the same length as the original dataset
### so we have to apply the same subsetting (if necessary) and removing
### of NAs as was done during model fitting
if (length(cluster) != x$k.all)
stop(mstyle$stop(paste0("Length of variable specified via 'cluster' (", length(cluster), ") does not match length of data (", x$k.all, ").")))
cluster <- .getsubset(cluster, x$subset)
cluster.f <- cluster
cluster <- cluster[x$not.na]
### checks on cluster variable
if (anyNA(cluster.f))
stop(mstyle$stop("No missing values allowed in 'cluster' variable."))
if (length(cluster.f) == 0L)
stop(mstyle$stop(paste0("Cannot find 'cluster' variable (or it has zero length).")))
### cluster ids and number of clusters
ids <- unique(cluster)
n <- length(ids)
#########################################################################
### calculate inverse of variance-covariance matrix under the full model
svb <- chol2inv(chol(x$vb[btt,btt,drop=FALSE]))
if (parallel == "no")
res <- pbapply::pblapply(seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt)
if (parallel == "multicore")
res <- pbapply::pblapply(seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt, cl=ncpus)
#res <- parallel::mclapply(seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt, mc.cores=ncpus)
if (parallel == "snow") {
if (is.null(cl)) {
cl <- parallel::makePSOCKcluster(ncpus)
on.exit(parallel::stopCluster(cl), add=TRUE)
}
if (.isTRUE(ddd$LB)) {
res <- parallel::parLapplyLB(cl, seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt)
#res <- parallel::clusterApplyLB(cl, seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt)
} else {
res <- pbapply::pblapply(seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt, cl=cl)
#res <- parallel::parLapply(cl, seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt)
#res <- parallel::clusterApply(cl, seq_len(n), .cooks.distance.rma.mv, obj=x, parallel=parallel, svb=svb, cluster=cluster, ids=ids, reestimate=reestimate, btt=btt)
}
}
cook.d <- sapply(res, function(x) x$cook.d)
#########################################################################
if (na.act == "na.omit") {
out <- cook.d
if (misscluster) {
names(out) <- x$slab[x$not.na]
} else {
names(out) <- ids
out <- out[order(ids)]
}
}
if (na.act == "na.exclude" || na.act == "na.pass") {
ids.f <- unique(cluster.f)
out <- rep(NA_real_, length(ids.f))
out[match(ids, ids.f)] <- cook.d
if (misscluster) {
names(out) <- x$slab
} else {
names(out) <- ids.f
out <- out[order(ids.f)]
}
}
if (na.act == "na.fail" && any(!x$not.na))
stop(mstyle$stop("Missing values in results."))
if (.isTRUE(ddd$time)) {
time.end <- proc.time()
.print.time(unname(time.end - time.start)[3])
}
return(out)
}
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