condProb: Compute the sensitivities (probability of true positive) of...

In RSKC: Robust Sparse K-Means

Description

The sensitivity or conditional probability of the correct classification of cluster k is calculated as follows: First, the proportions of observations whose true cluster label is k are computed for each classified clusters. Then the largest proportion is selected as the conditional probability of the correct classification. Since this calculation can return 1 for sensitivities of all clusters if all observations belong to one cluster, we also report the observed cluster labels returned by the algorithms.

Usage

Sensitivity(label1, label2)

Arguments

label1	A vector of length N, containing the cluster labels from any clustering algorithms.
label2	A vector of length N, containing the true cluster labels.

Author(s)

Yumi Kondo <[e-mail]>

Examples

vec1<-c(1,1,1,2,3,3,3,2,2)
vec2<-c(3,3,3,1,1,2,2,1,1)
Sensitivity(vec1,vec2)

RSKC documentation built on May 2, 2019, 7:23 a.m.