Nothing
## Hierarchical clustering
##
## Created : 18/11/02
## Last Modified : Time-stamp: <2018-05-12 16:48:19 (antoine)>
##
## This function is a "mix" of function dist and function hclust.
##
## Author : Antoine Lucas
##
hclusterpar <- hcluster <- function (x, method = "euclidean", diag = FALSE, upper = FALSE, link = "complete", members = NULL, nbproc = 2, doubleprecision = TRUE)
{
if(inherits(x, "exprSet"))
x <- Biobase::exprs(x)
## take from dist
if (!is.na(pmatch(method, "euclidian")))
method <- "euclidean"
METHODS <- c("euclidean", "maximum", "manhattan", "canberra",
"binary","pearson","correlation","spearman","kendall",
"abspearson","abscorrelation")
method <- pmatch(method, METHODS)
if (is.na(method))
stop("invalid distance method")
if (method == -1)
stop("ambiguous distance method")
N <- nrow(x <- as.matrix(x))
#take from hclust
METHODSLINKS <- c("ward", "single", "complete", "average", "mcquitty",
"median", "centroid","centroid2")
link <- pmatch(link, METHODSLINKS)
if (is.na(link))
stop("invalid clustering method")
if (link == -1)
stop("ambiguous clustering method")
if (N < 2)
stop("Must have n >= 2 objects to cluster")
if (is.null(members))
members <- rep(1, N)
if (length(members) != N)
stop("Invalid length of members")
n <- N
precision <- 1
if(doubleprecision)
precision <- 2
hcl <- .C(C_hcluster,
x = as.double(x),
nr = as.integer(n),
nc = as.integer(ncol(x)),
diag = as.integer(FALSE),
method = as.integer(method),
iopt = as.integer(link),
ia = integer(n),
ib = integer(n),
order = integer(n),
crit = double(n),
members = as.double(members),
nbprocess = as.integer(nbproc),
precision = as.integer(precision),
res = as.integer (1),
NAOK=TRUE,
PACKAGE= "amap")
if(hcl$res == 2)
stop("Cannot allocate memory")
if(hcl$res == 3)
stop("Missing values in distance Matrix")
if(hcl$res == 1)
stop("Error")
tree <- list(merge = cbind(hcl$ia[1:(N - 1)],
hcl$ib[1:(N - 1)]),
height = hcl$crit[1:(N - 1)],
order = hcl$order,
labels = dimnames(x)[[1]],
method = METHODSLINKS[link],
call = match.call(),
dist.method = METHODS[method]
)
class(tree) <- "hclust"
tree
}
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