Nothing
maxW_k <- function(x_d,v_d, pert){
######################### Used in INCAnumclu #################################
# When number of clusters k>=2
# Input:
# x_d <- distance matrix of the partition considered as populations
# v_d <- distance from each individual of the testing cluster to the rest.
# pert <- partition of individual considered as populations.
# Output:
# contar: number of individuals of the testing cluster considered as atipical or # well classified.
##########################################################################
if (is.null(dim(v_d))){dim(v_d) <- c(1, length(v_d))}
nt <- dim(v_d)[1]
k <- max(pert)
if (is.null(dim(x_d))){dim(x_d) <- c(1, length(x_d))}
nn <- dim(x_d)[1]
vg <- vgeo(x_d, pert);
delta <- deltas_simple(x_d,vg,pert)
frec <- tabulate(pert)
# Calculate W for individuals in xx/d_x
phi_d <- apply(x_d, 1, proxi_simple, var=vg, pert=pert, frec=frec)
TW <- apply(phi_d, 2, estW_simple, var=vg, delta= delta, Uout=FALSE)
# Calculate maxumum of W for ind. in xx
M <- max(TW)
############################################
# Calculate W for ind. in v_d and evalutate whether it is atypical or not
phi_x <- apply(v_d, 1, proxi_simple, var=vg, pert=pert, frec=frec)
W0 <- apply(phi_x, 2, estW_simple, var=vg, delta= delta, Uout=FALSE)
atipico <- ifelse(W0 <= M, 0, 1)
out <- sum(atipico)
return(out)
} # end function
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