tauW  R Documentation 
tauW
: Computes: (1) unweighted naive, (2) weighted naive, (3) unweighted tau, (4) weighted tau accuracy statistics
tauW(
CM,
W = diag(sqrt(length(as.matrix(CM)))),
P = rep(1/nrow(as.matrix(CM)), nrow(as.matrix(CM)))
)
summaryTauW(result.tau)
CM 
a square confusion (crossclassification) matrix (rows: allocation, columns: reference) 
W 
weights: 1 on diagonals, [1..1] off giving partial credit to this error 
P 
prior probability vector, length = number of rows/columns in 
result.tau 

summaryTauW
: prints a summary of the results from tauW
xtableTauW
: formats a LaTeX table with results from tauW and saves it as a .tex
file for import into a LaTeX document.
Input matrices CM
and W
may be in data.frame
format and will be converted
Weights matrix W
: 0 = no credit; 1 = full credit; 1 = maximum penalty
If absent, default is no partial credit, i.e., unweighted.
Prior probabilities vector P
: If absent, P
are equal priors for each class. Special value P = 0
is interpreted as P
= column marginals.
Error checks: CM
must be square; P
must have correct number of classes and sum to 1 +/ 0.0001; W
& CM
must be conformable
Results are returned in a list with obvious R names
D.G. Rossiter
Rossiter, D. G., Zeng, R., & Zhang, G.L. (2017). Accounting for taxonomic distance in accuracy assessment of soil class predictions. Geoderma, 292, 118–127. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.geoderma.2017.01.012")}
Ma, Z. K., & Redmond, R. L. (1995). Taucoefficients for accuracy assessment of classification of remotesensing data. Photogrammetric Engineering and Remote Sensing, 61(4), 435–439.
Naesset, E. (1996). Conditional tau coefficient for assessment of producer’s accuracy of classified remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 51(2), 91–98. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/09242716(69)000074")}
# example confusion matrix
# rows: allocation (user's counts)
# columns: reference (producer's counts)
crossclass < matrix(data=c(2,1,0,5,0,0,
1,74,2,1,3,6,
0,5,8,6,1,3,
6,1,3,91,0,0,
0,4,0,0,0,4,
0,6,2,2,4,38),
nrow=6, byrow=TRUE)
row.names(crossclass) < c("OP", "SA", "UA", "UC", "AV", "AC")
colnames(crossclass) < row.names(crossclass)
# build the weights matrix
# how much credit for a misallocation
weights < matrix(data=c(1.00,0.05,0.05,0.15,0.05,0.15,
0.05,1.00,0.05,0.05,0.05,0.35,
0.05,0.05,1.00,0.20,0.15,0.15,
0.15,0.05,0.25,1.00,0.10,0.25,
0.05,0.10,0.15,0.10,1.00,0.15,
0.20,0.30,0.10,0.25,0.20,1.00),
nrow=6, byrow=TRUE)
# unweighted accuracy
summaryTauW(nnaive < tauW(crossclass))
# unweighted tau with equal priors, equivalent to Foody (1992) modified Kappa
tauW(crossclass)$tau
# unweighted tau with user's = producer's marginals, equivalent to original kappa
(priors < apply(crossclass, 2, sum)/sum(crossclass))
tauW(crossclass, P=priors)$tau
# weighted accuracy; tau with equal priors
summaryTauW(weighted < tauW(crossclass, W=weights))
# weighted accuracy; tau with user's = producer's marginals
summaryTauW(tauW(crossclass, W=weights, P=priors))
# change in accuracy statistics weighted vs. nonweighted
(weighted$overall.weighted  weighted$overall.naive)
(weighted$user.weighted  weighted$user.naive)
(weighted$prod.weighted  weighted$prod.naive)
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