Nothing
# Play with different options and check their effect on running times for bic and vdp
skip <- TRUE
if (!skip) {
Ns <- 100
Nd <- 2
set.seed(3488400)
D <- cbind(
rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd),
matrix(rnorm(Ns*Nd, mean = 2), ncol = Nd),
cbind(rnorm(Ns, mean = -1), rnorm(Ns, mean = 3))
),
rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd),
matrix(rnorm(Ns*Nd, mean = 2), ncol = Nd),
cbind(rnorm(Ns, mean = -1), rnorm(Ns, mean = 3))
)
)
rownames(D) <- paste("R", 1:nrow(D), sep = "-")
colnames(D) <- paste("C", 1:ncol(D), sep = "-")
ts <- c()
for (mm in c("bic", "vdp")) {
# NOTE: no PCA basis needed with mixture.method = "bic"
tt <- system.time(detect.responses(D, verbose = TRUE, max.responses = 5,
mixture.method = mm, information.criterion = "BIC",
merging.threshold = 0, bic.threshold = 0, pca.basis = TRUE))
print(paste(mm, ":", round(tt[["elapsed"]], 3)))
ts[[mm]] <- tt[["elapsed"]]
}
print(paste(names(ts)[[1]], "/", names(ts)[[2]], ": ", round(ts[[1]]/ts[[2]], 3)))
}
# -> VDP is much faster when sample sizes increase
# 1000 samples -> 25-fold speedup with VDP
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