Nothing

```
INCAindex <- function(d, pert_clus){
############### Number of atypical individuals #############################
# Input:
# d: distance matrix (n x n)
# pert_clus: partition of individuals (n x 1)
# Output:
# Atypicals: Number of atipicals or well classified for each cluster given a
# partition pert_clus
# Total : Mean percentage of atypicals(well classified) for the partition
# pert_clus
# Ni_cluster: number of individuals in each cluster for the given partition
#############################################################################
d <- as.matrix(d)
n <- dim(d)[1]
pert_clus <- as.integer(pert_clus)
nclus<- max(pert_clus)
# populations must be named with numbers from 1 to k
if (length(tabulate(as.factor(pert_clus))) != nclus)
stop("Partitions must be named by factors or with numbers from 1 to k.")
# 0 can not be a partitions name
if (any(pert_clus==0))
stop("pert contains 0 named individuals.Partitions must be named by factors or with numbers from 1 to k.")
k <- nclus-1 # When there are 5 clusters, INCA is applied for 4 clusters
atypicals <- matrix(0,nclus,1)
percent_aty <- matrix(0, nclus,1)
f <- tabulate(pert_clus) #frecuencies of each cluster
# Calculate W for ind. in each cluster and afterwards to compare with the rest
for (tt in 1:nclus){ # se va a calcular INCA para el cluster tt
# Calculate frecuencies in each cluster to apply INCA
ff <- matrix(0,k,1)
aux <-0
for (i in 1:nclus){
if (i !=tt){
aux <- aux+1
ff[aux]<-f[i]
}
}
nn <- sum(ff) # total individual in INCA-typicality
nv <- f[tt] # number of individual to be testes by INCA-typicality
pert <- matrix(0, nn,1) # partition in k clusters
aux <- 0
for (i in 1:n){
if (pert_clus[i] != tt){
aux <- aux +1
if (pert_clus[i]<=tt){
pert[aux] <- pert_clus[i]
}
if (pert_clus[i]>tt){
pert[aux] <- pert_clus[i]-1
}
}
}
selec_xx <- pert_clus != tt
selec_v <- pert_clus == tt
xx_dist <- d[selec_xx,selec_xx ]
v_dist <- d[selec_v,selec_xx ]
# Verified.
# Calculate INCA to each ind. in v_dist with respect data in xx_dist
if (k==1){
atypicals[tt] <- maxW_k1(xx_dist, v_dist)
percent_aty[tt] <- atypicals[tt]/f[tt]
}
if (k>1){
atypicals[tt] <- maxW_k(xx_dist, v_dist, pert)
percent_aty[tt] <- atypicals[tt]/f[tt]
}
} # for (tt in 1:nclus)
total <- sum(percent_aty)/nclus
out <- list(well_class=atypicals, Ni_cluster=f, Total= total)
class(out) <- "incaix"
return(out)
} #end of function
```

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