th.dep: Spatial threshold-dependent accuracy measures

In levisc8/spind: Spatial Methods and Indices

Spatial threshold-dependent accuracy measures

Description

Calculates spatially corrected, threshold-dependent metrics for an observational data set and model predictions (Kappa and confusion matrix)

Usage

th.dep(data, coord, thresh = 0.5, spatial = TRUE)

Arguments

data	A data frame or matrix with two columns. The first column should contain actual presence/absence data (binary, 0 or 1) and the second column should contain model predictions of probability of occurrence (numeric, between 0 and 1).
coord	A data frame or matrix with two columns containing x,y coordinates for each actual and predicted value. Coordinates must be integer and consecutively numbered.
thresh	A cutoff value for classifying predictions as modeled presences or modeled absences. Default is 0.5.
spatial	A logical indicating whether spatially corrected indices (rather than classical indices) should be computed.

Value

A list with the following components:

kappa

Kappa statistic

cm

Confusion matrix

sensitivity

Sensitivity

specificity

Specificity

actuals

Actual occurrence data or adjusted actual occurrence data

splitlevel.pred

Level splitting of predicted values

splitlevel.act

Level splitting of actuals or adjusted actuals

splitposition.pred

Position splitting of predicted values

splitposition.act

Position splitting of actuals or adjusted actuals

Author(s)

Gudrun Carl

References

Carl G, Kuehn I (2017) Spind: a package for computing spatially corrected accuracy measures. Ecography 40: 675-682. DOI: 10.1111/ecog.02593

See Also

th.indep

Examples

data(hook)
data <- hook[ ,1:2]
coord <- hook[ ,3:4]
si1 <- th.dep(data, coord, spatial = TRUE)
si1$kappa
si1$cm

levisc8/spind documentation built on April 3, 2024, 4:52 a.m.