FARI: Compute the Frobenius (adjusted) Rand index

In Keefe-Murphy/MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Compute the Frobenius (adjusted) Rand index

Description

This function efficiently computes fuzzy generalisations of the Rand and adjusted Rand indices for comparing two partitions, allowing either or both partitions to be "soft" or "hard".

Usage

FARI(z1,
     z2)

Arguments

z1, z2

A n * G matrix representing a hard partition (all entries 0 or 1) or soft cluster-membership probabilities.

Details

If z1 &/or z2 is supplied as a vector of cluster labels, they will be coerced to an appropriate matrix via unmap.

Value

A list with the following named components:

FRI

Measure of Frobenius Rand index between z1 and z2.

FARI

Measure of Frobenius adjusted Rand index between z1 and z2.

Note

The number of columns of the matrices z1 and z2 need not be equal.

Author(s)

Keefe Murphy - <[e-mail]>

References

Andrew, J. L., Browne, R., and Hvingelby, C. D. (2022). On assessments of agreement between fuzzy partitions. Journal of Classification, 39(2): 326-342.

See Also

unmap

Examples

m1 <- MoE_clust(ais[,3:7], G=2, modelNames="EVE",
                gating=~BMI, expert=~sex, network.data=ais)
m2 <- MoE_clust(ais[,3:7], G=2, modelNames="EVE", 
                equalPro=TRUE, expert=~sex, network.data=ais)
m3 <- MoE_clust(ais[,3:7], G=2, modelNames="VEE", algo="CEM", tau0=0.1)

# FARI between two soft partitions
FARI(m1$z, m2$z)
# FARI between soft and hard partitions
FARI(m1$z, m3$z)
# FARI between soft partition and hard classification
FARI(m1$z, m2$classification)
# FARI between hard partition and hard classification
FARI(m3$z, m3$classification)
# FARI between hard classification and hard classification
FARI(m1$classification, m2$classification)

Keefe-Murphy/MoEClust documentation built on Feb. 1, 2024, 4:36 a.m.