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INCAncinner <- function(d, K, method,pert){
############### Number of atypical individuals #############################
# Input:
# d: distance matrix (n x n)
# K: partitions in 2:K clusters are considered
# method: method to perform the clustering. If method="partition" a collection
# of M partitions must by given in pert.
#
# Output:
# Totals : INCA index for k=2, ..., K
#############################################################################
d <- as.matrix(d)
n <- dim(d)[1]
pama <- function(d, K){
ppp <- sapply(2:K, function(x){cluster::pam(d, k=x, diss=TRUE)$clustering})
T <- c(NA, apply(ppp, 2, function(x){INCAindex(d, x)$Total}))
return(T)
}
dian <- function(d, K){
aux <- cluster::diana(d, diss=TRUE)
ppp <- sapply(2:K, function(x){stats::cutree(aux, k=x)})
T <- c(NA, apply(ppp, 2, function(x){INCAindex(d, x)$Total}))
return(T)
}
fannya <- function(d, K){
ppp <- sapply(2:K, function(x){cluster::fanny(d, k=x, diss=TRUE)$clustering})
T <- c(NA, apply(ppp, 2, function(x){INCAindex(d, x)$Total}))
return(T)
}
bestela <- function(d, K, method){
aux <- fastcluster::hclust(as.dist(d), method=method)
ppp <- sapply(2:K, function(x){stats::cutree(aux, k=x)})
T <- c(NA, apply(ppp, 2, function(x){INCAindex(d, x)$Total}))
return(T)
}
if (method=="partition"){
pert <- as.matrix(pert)
M <- dim(pert)[2]
K <- max(pert)
# 0 can not be a partitions name
if (any(pert == 0))
stop("pert contains 0 named individuals.Partitions must be named by factors or with numbers from 1 to k.")
T <- rep(NA, K)
# tops <- rep(NA, M)
for (m in 1:M){
tops <- max(pert[,m])
# populations must be named with numbers from 1 to k
if (length(tabulate(as.factor(pert[,m]))) != tops)
stop("Partitions must be named by factors or with numbers from 1 to k.")
T[tops] <- INCAindex(d, pert[,m])$Total
}
} else{
T <- switch(method, pam=pama(d, K), diana=dian(d, K), fanny=fannya(d, K), bestela(d,K, method))
}
out <- list(INCAindex=T, method=method)
#class(out) <- "incanc"
return(out)
} #end of function
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