R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Measures of Accuracy

omission

R Documentation

Measures of Accuracy

Description

Estimates different measures of accurracy given a confusion matrix.

Usage

omission(mat)

sensitivity(mat)

specificity(mat)

prop.correct(mat)

Arguments

mat

a confusion matrix of class 'confusion.matrix' from confusion.matrix

Value

returns single values representing the:

`ommission`	the ommission rate as a proportion of true occurrences misidentified given the defined threshold value
`sensitivity`	the sensitivity given the defined threshold value
`specificity`	the specificity given the defined threshold value
`prop.correct`	the proportion of the presence and absence records correctly identified given the defined threshold value

Author(s)

Jeremy VanDerWal jjvanderwal@gmail.com

Examples

#create some data
obs = c(sample(c(0,1),20,replace=TRUE),NA); obs = obs[order(obs)]
pred = runif(length(obs),0,1); pred = pred[order(pred)]

#calculate the confusion matrix
mat = confusion.matrix(obs,pred,threshold=0.5)

#calculate the accuracy measures
omission(mat)
sensitivity(mat)
specificity(mat)
prop.correct(mat)

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SDMTools)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SDMTools/omission.Rd_%03d_medium.png", width=480, height=480)
> ### Name: omission
> ### Title: Measures of Accuracy
> ### Aliases: omission prop.correct sensitivity specificity
> 
> ### ** Examples
> 
> #create some data
> obs = c(sample(c(0,1),20,replace=TRUE),NA); obs = obs[order(obs)]
> pred = runif(length(obs),0,1); pred = pred[order(pred)]
> 
> #calculate the confusion matrix
> mat = confusion.matrix(obs,pred,threshold=0.5)
Warning message:
In confusion.matrix(obs, pred, threshold = 0.5) :
  1 data points removed due to missing data
> 
> #calculate the accuracy measures
> omission(mat)
[1] 0
> sensitivity(mat)
[1] 1
> specificity(mat)
[1] 1
> prop.correct(mat)
[1] 1
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>