CARI {bikm1}

CARI function for agreement between co-clustering partitions

Description

Produce a measure of agreement between two pairs of partitions for co-clustering. A value of 1 means a perfect match.

Usage

CARI(v,w,vprime,wprime)

Arguments

v

numeric vector specifying the class of rows.

w

numeric vector specifying the class of columns.

vprime

numeric vector specifying another partition of rows.

wprime

numeric vector specifying another partition of columns.

Value

a list including the arguments:

cari: value of the index (between 0 and 1). A value of 1 corresponds to a perfect match.

nvw: contingency table which the index is based on.

References

Robert, Vasseur and Brault. Comparing high dimensional partitions with the Co-clustering Adjusted Rand Index, Journal of classification 38 (1), 158-186 (2021).

Examples

require(bikm1)
J=200
K=120
h=3
l=2
theta=list()
theta$rho_h=1/h *matrix(1,h,1)
theta$tau_l=1/l *matrix(1,l,1)
theta$gamma_hl=matrix(c(1, 6,4, 1, 7, 1),ncol=2)
data=PoissonBlocRnd(J,K,theta)
res=BIKM1_LBM_Poisson(data$x,4,4,4,init_choice='smallVBayes')
me=CARI(res@model_max$v,res@model_max$w, data$xrow,data$xcol)
me$cari
me$nvw