enmSdmX (version 1.2.12)

evalTjursR2: Weighted Tjur's R2

Description

This function calculates Tjur's R2 metric of model discrimination accuracy. Unweighted R2 is simply the difference between the mean predicted value at presence sites and the mean predicted value at absence/background sites. The weighted version allows for differing weights between presences and between absences/contrast values (i.e., the difference between the weighted mean of predictions at presences and weighted mean predictions at absences/contrast locations).

Usage

evalTjursR2(
  pres,
  contrast,
  presWeight = rep(1, length(pres)),
  contrastWeight = rep(1, length(contrast)),
  na.rm = FALSE,
  ...
)

Value

Numeric value.

Arguments

pres

Predictions at presence sites.

contrast

Predictions at absence/background sites.

presWeight

Weights of presence cases. The default is to assign each presence case a weight of 1.

contrastWeight

Weights of absence/background cases. The default is to assign each case a weight of 1.

na.rm

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

...

Other arguments (unused).

References

Tjur, T. 2009. Coefficients of determination in logistic regression models-A new proposal: The coefficient of discrimination. The American Statistician 63:366-372. tools:::Rd_expr_doi("10.1198/tast.2009.08210")

See Also

pa_evaluate, evalAUC, evalMultiAUC, evalContBoyce, evalThreshold, evalThresholdStats, evalTSS

Examples

pres <- seq(0.5, 1, by=0.1)
contrast <- seq(0, 1, by=0.01)

# unweighted
evalTjursR2(pres, contrast)

# weighted (weight presences with low predictions more)
presWeight <- c(1, 1, 1, 0.5, 0.5, 0.5)
evalTjursR2(pres, contrast, presWeight=presWeight)

# weighted (weight presences with high predictions more)
presWeight <- c(0.5, 0.5, 0.5, 1, 1, 1)
evalTjursR2(pres, contrast, presWeight=presWeight)

# weight presences and absences
contrastWeight <- sqrt(contrast)
evalTjursR2(pres, contrast, presWeight=presWeight, contrastWeight=contrastWeight)