plot_cv: Visualizations for the prediction diagnostics of an estimated...

In Waller-SUSAN/envi: Environmental Interpolation using Spatial Kernel Density Estimation

Visualizations for the prediction diagnostics of an estimated ecological niche

Description

Create multiple plots of output from the lrren function, specifically for the internal k-fold cross-validation diagnostics.

Usage

plot_cv(input, alpha = 0.05)

Arguments

input	An object of class 'list' from the lrren function.
alpha	Numeric. The two-tailed alpha level for the significance threshold (default is 0.05).

Value

This function produces two plots: 1) area under the receiver operating characteristic curve and 2) precision-recall curve. Each plot shows predictions for the log relative risk surface. The red-colored lines are the average curves.

Examples

if (interactive()) {
  set.seed(1234) # for reproducibility

# Using the 'bei' and 'bei.extra' data within {spatstat.data}

# Covariate data (centered and scaled)
  elev <- spatstat.data::bei.extra[[1]]
  grad <- spatstat.data::bei.extra[[2]]
  elev$v <- scale(elev)
  grad$v <- scale(grad)
  elev_raster <- terra::rast(elev)
  grad_raster <- terra::rast(grad)

# Presence data
  presence <- spatstat.data::bei
  spatstat.geom::marks(presence) <- data.frame("presence" = rep(1, presence$n),
                                               "lon" = presence$x,
                                               "lat" = presence$y)
  spatstat.geom::marks(presence)$elev <- elev[presence]
  spatstat.geom::marks(presence)$grad <- grad[presence]

# (Pseudo-)Absence data
  absence <- spatstat.random::rpoispp(0.008, win = elev)
  spatstat.geom::marks(absence) <- data.frame("presence" = rep(0, absence$n),
                                              "lon" = absence$x,
                                              "lat" = absence$y)
  spatstat.geom::marks(absence)$elev <- elev[absence]
  spatstat.geom::marks(absence)$grad <- grad[absence]

# Combine into readable format
  obs_locs <- spatstat.geom::superimpose(presence, absence, check = FALSE)
  obs_locs <- spatstat.geom::marks(obs_locs)
  obs_locs$id <- seq(1, nrow(obs_locs), 1)
  obs_locs <- obs_locs[ , c(6, 2, 3, 1, 4, 5)]
  
# Run lrren
  test_lrren <- lrren(obs_locs = obs_locs,
                      cv = TRUE)
                      
# Run plot_cv                 
  plot_cv(input = test_lrren)
}

Waller-SUSAN/envi documentation built on Nov. 8, 2024, 12:35 a.m.