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BAT (version 1.1)

accuracy: Scaled mean squared error of accumulation curves.

Description

Accuracy (scaled mean squared error) of accumulation curves compared with a known true diversity value (target).

Usage

accuracy(accum, target = -1)

Arguments

accum
A matrix resulting from the alpha.accum or beta.accum functions (sampling units x diversity values).
target
The true known diversity value, with which the curve will be compared. If not specified, default is the diversity observed with all sampling units.

Value

  • Accuracy values for all observed and estimated curves.

Details

Among multiple measures of accuracy (Walther & Moore 2005) the SMSE presents several advantages, as it is (Cardoso et al. 2014): (i) scaled to true diversity, so that similar absolute differences are weighted according to how much they represent of the real value; (ii) scaled to the number of sampling units, so that values are independent of sample size; (iii) squared, so that small, mostly meaningless fluctuations around the true value are down-weighted; and (iv) independent of positive or negative deviation from the real value, as such differentiation is usually not necessary.

References

Cardoso, P., Rigal, F., Borges, P.A.V. & Carvalho, J.C. (2014) A new frontier in biodiversity inventory: a proposal for estimators of phylogenetic and functional diversity. Methods in Ecology and Evolution, in press. Walther, B.A. & Moore, J.L. (2005) The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature reviewof estimator performance. Ecography, 28, 815-829.

Examples

Run this code
comm1 <- matrix(c(2,2,0,0,0,1,1,0,0,0,0,2,2,0,0,0,0,0,2,2), nrow = 4, ncol = 5, byrow = TRUE)
comm2 <- matrix(c(1,1,0,0,0,0,2,1,0,0,0,0,2,1,0,0,0,0,2,1), nrow = 4, ncol = 5, byrow = TRUE)
tree <- hclust(dist(c(1:5), method="euclidean"), method="average")
acc.alpha = alpha.accum(comm1)
accuracy(acc.alpha)
accuracy(acc.alpha, 10)
acc.beta = beta.accum(comm1, comm2, tree)
accuracy(acc.beta)
accuracy(acc.beta, c(1,1,0))

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