42 lines
1.1 KiB
Plaintext
42 lines
1.1 KiB
Plaintext
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## not necessarily reproducible examples.
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library(parallel)
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cl <- makeCluster(getOption("cl.cores", 2))
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clusterApply(cl, 1:2, get("+"), 3)
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xx <- 1
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clusterExport(cl, "xx")
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clusterCall(cl, function(y) xx + y, 2)
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## Use clusterMap like an mapply example
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clusterMap(cl, function(x,y) seq_len(x) + y,
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c(a = 1, b = 2, c = 3), c(A = 10, B = 0, C = -10))
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parSapply(cl, 1:20, get("+"), 3)
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## PR14898
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parSapply(cl, 1, identity)
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if(require(boot, quietly = TRUE)) {
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set.seed(11)
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## A bootstrapping example, which can be done in many ways:
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clusterEvalQ(cl, {
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## set up each worker. Could also use clusterExport()
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library(boot)
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cd4.rg <- function(data, mle) MASS::mvrnorm(nrow(data), mle$m, mle$v)
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cd4.mle <- list(m = colMeans(cd4), v = var(cd4))
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NULL
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})
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res <- clusterEvalQ(cl, boot(cd4, corr, R = 100,
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sim = "parametric", ran.gen = cd4.rg, mle = cd4.mle))
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cd4.boot <- do.call(c, res)
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print(boot.ci(cd4.boot, type = c("norm", "basic", "perc"),
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conf = 0.9, h = atanh, hinv = tanh))
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}
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stopCluster(cl)
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