plot_cv {envi}

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)
}