confus: (Fuzzy) Confusion Matrix

In optpart: Optimal Partitioning of Similarity Relations

Description

A confusion matrix is a cross-tabulation of actual class membership with memberships predicted by a discriminant function, classification tree, or other predictive model. A fuzzy confusion matrix is a confusion matrix that corrects for ‘near misses’ in prediction by comparing the similarity of the predicted type to the actual type and giving credit for the similarity.

Usage

confus(clustering,model,diss=NULL)

Arguments

clustering	an object of class ‘clustering’ or a vector of (integer or factor) class membership values
model	a predictive model of class ‘tree’ or ‘randomForest’
diss	optionally, a dissimilarity object of class ‘dist’ from ‘dist’, ‘dsvdis’, or ‘vegdist’

Details

Cross-classifies each sample by actual class membership and predicted membership, computing overall accuracy, and the Kappa statistic of agreement. If a dissimilarity matrix is passed, calculates a fuzzy confusion matrix. In this case, correct predictions are assigned values of 1.0, and other predictions are given the value of the similarity of the two types an placed on the diagonal. The dissimilarity of the two types is added off the diagonal as fuzzy error.

Value

produces a list with elements

matrix	the (fuzzy) cross-tabulation matrix as a data.frame
correct	the fraction of (fuzzily) correctly predicted samples
kappa	the value of the Kappa statistic
legend	the text legend for the cross-tabulation matrix

Note

Confusion matrices are commonly computed in remote sensing applications, but are equally suited to the evaluation of any predictive methods of class membership or factors.

Author(s)

David W. Roberts [e-mail] http://ecology.msu.montana.edu/labdsv/R

References

http://ecology.msu.montana.edu/labdsv/R

Examples

data(shoshveg) # returns a data frame of vegetation data
data(shoshsite) # returns a data frame of site data
dis.bc <- dsvdis(shoshveg,'bray')
opt.5 <- optpart(5,dis.bc)
library(tree)
mod <- tree(factor(opt.5$clustering)~ elevation+slope+av,
            data=shoshsite)
confus(opt.5,mod)
confus(opt.5,mod,dis.bc)

Example output

Loading required package: cluster
Loading required package: labdsv
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
This is labdsv 2.0-1
convert existing ordinations with as.dsvord()

Attaching package: ‘labdsv’

The following object is masked from ‘package:stats’:

    density

Loading required package: MASS
Loading required package: plotrix
$confus
     [,1] [,2] [,3] [,4] [,5]
[1,]   15    3    1    0    0
[2,]    6   42    6    1    1
[3,]    2    9   21    1    1
[4,]    1    2    5    7    3
[5,]    1    3    0    0   19

$correct
[1] 104

$percent
[1] 0.6933333

$kappa
[1] 0.5913533

$confus
           [,1]      [,2]      [,3]       [,4]       [,5]
[1,] 17.3726945  1.144038  0.483268  0.0000000  0.0000000
[2,]  3.9606226 47.976570  2.430329  0.8834618  0.7490170
[3,]  1.4093234  3.429740 27.653490  0.8330640  0.6743818
[4,]  0.7836742  1.412361  2.977090 11.6709577  1.1559174
[5,]  0.8902513  2.245041  0.000000  0.0000000 19.8647074

$correct
[1] 124.5384

$percent
[1] 0.8302561

$kappa
[1] 0.7752168

optpart documentation built on March 26, 2020, 6:18 p.m.