# Precision_Recall: Precision In QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

## Description

Computes the precision based on the clustering

## Usage

 1 Precision_Recall(hx, Truth) 

## Arguments

 hx a numeric vector of cluster selection Truth the ground truth for clusters

## Value

TP

The number of true positive links

TN

The number of true negative links

FP

The number of false positive links

FN

The number of false negative links

Pr

The precision, defined by Pr = \frac{TP}{TP+FP}

R

The recall, defined by R = \frac{TP}{TP+FN}

F1

The F1 index, defined by F1 = \frac{2\times P \times R}{P + R}

RI

Rand Index, defined by RI = \frac{TP+TN}{TP+TN+FP+FN}

validat

Is positives + negatives equal to total number of links - returns absolute difference if false

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 set.seed(123) #1: Cluster data FQC<-FlashQC(QuantumClone::Input_Example,conta = c(0,0),Nclus = 2:10) #2: Compute NMI Precision_Recall(hx = FQC$cluster,Truth = QuantumClone::Input_Example[[1]]$Chr) ### From Stanford NLP example: cluster<-c(rep(1,6),rep(2,6),rep(3,5)) truth<-c(rep(1,5),2, 1,rep(2,4),3, rep(1,2),rep(3,3)) Precision_Recall(cluster,truth) 

QuantumClone documentation built on May 2, 2019, 3:03 a.m.