plot_obs: Visualizations for an estimated ecological niche in covariate...

In envi: Environmental Interpolation using Spatial Kernel Density Estimation

Visualizations for an estimated ecological niche in covariate space

Description

Create multiple plots of output from the lrren function, specifically for the observation data and estimated ecological niche.

Usage

plot_obs(
  input,
  plot_cols = c("#8B3A3A", "#CCCCCC", "#0000CD"),
  alpha = input$p_critical,
  lower_lrr = NULL,
  upper_lrr = NULL,
  digits = 1,
  ...
)

Arguments

input	An object of class 'list' from the lrren function.
plot_cols	Character string of length three (3) specifying the colors for plotting: 1) presence, 2) neither, and 3) absence. The default colors in hex are c("#8B3A3A", "#CCCCCC", "#0000CD") or c("indianred4", "grey80", "blue3").
alpha	Optional, numeric. The two-tailed alpha level for significance threshold (default is the p_critical value imported from input).
lower_lrr	Optional, numeric. Lower cut-off value for the log relative risk value in the color key (typically a negative value). The default is no limit, and the color key will include the minimum value of the log relative risk surface.
upper_lrr	Optional, numeric. Upper cut-off value for the log relative risk value in the color key (typically a positive value). The default is no limit, and the color key will include the maximum value of the log relative risk surface.
digits	Optional, integer. The number of significant digits for the color key labels using the round function (default is 1).
...	Arguments passed to plot.ppp and image.plot for additional graphical features.

Value

This function produces three plots in a two-dimensional space where the axes are the two specified covariates: 1) observation locations by group, 2) log relative risk surface, and 3) significant p-value surface.

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 = FALSE)
                      
# Run plot_obs   
  plot_obs(input = test_lrren)
}

envi documentation built on Feb. 16, 2023, 7:38 p.m.