Compute the sensitivities (probability of true positive) of each cluster

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

Author(s)

Yumi Kondo <y.kondo@stat.ubc.ca>

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(RSKC)
Loading required package: flexclust
Loading required package: grid
Loading required package: lattice
Loading required package: modeltools
Loading required package: stats4
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RSKC/condProb.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Sensitivity
> ### Title: Compute the sensitivities (probability of true positive) of each
> ###   cluster
> ### Aliases: Sensitivity
> 
> ### ** 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)
$prob
                        1   2   3
Sensitivity. (%)       75 100 100
Class label by label1.  2   3   1

$table
      label2
label1 1 2 3
     1 0 0 3
     2 3 0 0
     3 1 2 0

$marginal
      label2
label1    1    2    3
     1 0.00 0.00 1.00
     2 0.75 0.00 0.00
     3 0.25 1.00 0.00

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>