enmSdmX (version 1.2.12)

evalTSS: Weighted True Skill Statistic (TSS)

Description

This function calculates the True Skill Statistic (TSS).

Usage

evalTSS(
  pres,
  contrast,
  presWeight = rep(1, length(pres)),
  contrastWeight = rep(1, length(contrast)),
  thresholds = seq(0, 1, by = 0.001),
  na.rm = FALSE,
  ...
)

Value

Numeric value.

Arguments

pres

Numeric vector. Predicted values at test presences

contrast

Numeric vector. Predicted values at background/absence sites.

presWeight

Numeric vector same length as pres. Relative weights of presence sites. The default is to assign each presence a weight of 1.

contrastWeight

Numeric vector same length as contrast. Relative weights of background sites. The default is to assign each presence a weight of 1.

thresholds

Numeric vector. Thresholds at which to calculate the sum of sensitivity and specificity. The default evaluates all values from 0 to 1 in steps of 0.01.

na.rm

Logical. If TRUE then remove any presences and associated weights and background predictions and associated weights with NAs.

...

Other arguments (unused).

Details

This function calculates the maximum value of the True Skill Statistic (i.e., across all thresholds, the values that maximizes sensitivity plus specificity).

References

See Allouche, O., Tsoar, A., and Kadmon, R. 2006. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43:1223-1232. tools:::Rd_expr_doi("10.1111/j.1365-2664.2006.01214.x")

See Also

pa_evaluate, evalAUC, evalMultiAUC, evalContBoyce, evalThreshold, evalThresholdStats, evalTjursR2

Examples

set.seed(123)

# set of bad and good predictions at presences
bad <- runif(30)^2
good <- runif(30)^0.1
hist(good, breaks=seq(0, 1, by=0.1), border='green', main='Presences')
hist(bad, breaks=seq(0, 1, by=0.1), border='red', add=TRUE)
pres <- c(bad, good)
contrast <- runif(1000)
evalTSS(pres, contrast)

# upweight bad predictions
presWeight <- c(rep(1, 30), rep(0.1, 30))
evalTSS(pres, contrast, presWeight=presWeight)

# upweight good predictions
presWeight <- c(rep(0.1, 30), rep(1, 30))
evalTSS(pres, contrast, presWeight=presWeight)