tssWeighted: Weighted True Skill Statistic (TSS)

In adamlilith/enmSdm: Tools for Modeling Niches and Distributions of Species

Weighted True Skill Statistic (TSS)

Description

This function calculates the True Skill Statistic (TSS).

Usage

tssWeighted(
  pres,
  contrast,
  presWeight = rep(1, length(pres)),
  contrastWeight = rep(1, length(contrast)),
  thresholds = seq(0, 1, by = 0.01),
  na.rm = FALSE,
  bg = NULL,
  bgWeight = NULL,
  ...
)

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.
bg	Same as contrast. Included for backwards compatibility. Ignored if contrast is not NULL.
bgWeight	Same as contrastWeight. Included for backwards compatibility. Ignored if contrastWeight is not NULL.
...	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).

Value

Numeric value.

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.

See Also

fpb, aucWeighted, link[enmSdm]{contBoyce}, link[enmSdm]{contBoyce2x}

Examples

set.seed(123)

# set of bad and good predictions at presences
bad <- runif(100)^2
good <- runif(100)^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)
tssWeighted(pres, contrast)

# upweight bad predictions
presWeight <- c(rep(1, 100), rep(0.1, 100))
tssWeighted(pres, contrast, presWeight=presWeight)

# upweight good predictions
presWeight <- c(rep(0.1, 100), rep(1, 100))
tssWeighted(pres, contrast, presWeight=presWeight)

e <- dismo::evaluate(pres, contrast)
max(e@TPR + e@TNR) - 1

# why different from dismo's evaluate() function?
# because uses different thresholds based on values of
# presence and absence predictions
head(e@t)

adamlilith/enmSdm documentation built on Jan. 6, 2023, 11 a.m.