confuseMatrix: Calculates the confusion matrix

In cghaase/GARPTools: Tools for GARP Data Prep and Model Evaluation

Description

confuseMatrix Returns the confusion matrix containing the sensitivity and specificity of the best model subset of a GARP run.

Usage

confuseMatrix(n, x, points)

Arguments

n	a numeric value specifying the number of models in the best subset output from GARP
x	a raster object of the summated raster of the best models output from GARP
points	a spatial object of presence data to use for testing locations

Details

For discrete cutpoints (n), represents the number of models that agree on a predicted presence location.

The raster object (x) should be a raster representing the number of models that agree on a predicted presence location per pixel and that outtput by sumRasters.

The shapefile points should presence locations that were not used by GARP for model training and those output by splitData

Value

A data.frame object containing:

• a True positive

• b False negative

• c False positive

• d True negative

• sensitivity the proportion of occupied pixels correctly predicted as present

• specificity the proportion of non-occupied pixels correctly predicted as absent

• 1-specificity the proportion of non-occupied pixels incorrectly predicted as present

See Also

aucGARP

Examples

  set.seed(0)
  library(raster)
  r   <- raster(ncols = 100, nrows = 100)
  r[] <- rbinom(5, 10, 0.3)
  hs  <- data.frame("Latitude" = c(-89, 72, 63, 42, 54), "Longitude" = c(-12, 13, 24, 26, 87), "Species" = rep("Homo_sapiens", 5))
  confuseMatrix(n = 10, x = r, points = SpatialPoints(hs[,1:2]))

cghaase/GARPTools documentation built on Aug. 6, 2021, 6:38 a.m.