plot_perturb: Visualizations for a simulated ecological niche after...

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

Visualizations for a simulated ecological niche after iteratively perturbing the observation coordinates

Description

Create multiple plots of output from the perlrren function, specifically for the four summary statistics in covariate space and geographic space.

Usage

plot_perturb(
  input,
  predict = TRUE,
  mean_cols = c("#8B3A3A", "#CCCCCC", "#0000CD"),
  var_cols = c("#E5E5E5", "#1A1A1A"),
  cov_labs = c("V1", "V2"),
  cref0 = "EPSG:4326",
  cref1 = NULL,
  lower_lrr = NULL,
  upper_lrr = NULL,
  upper_sd = NULL,
  digits = 1,
  ...
)

Arguments

input	An object of class 'list' from the perlrren function.
predict	Logical. If TRUE (the default), will visualize the four summary statistics in geographic space. If FALSE, will not.
mean_cols	Character string of length three (3) specifying the colors for plots with a divergent color palette: 1) presence, 2) neither, and 3) absence. The default colors in hex are c("#8B3A3A", "#CCCCCC", "#0000CD") or c("indianred4", "grey80", "blue3").
var_cols	Character string of length two (2) specifying the colors for plots with a sequential color palette from low to high values. The default colors in hex are c("#E5E5E5", "#1A1A1A") or c("grey90", "grey10").
cov_labs	Character string of length two (2) specifying the x- and y-axis labels in plots of the ecological niche in covariate space. The default values are generic c("V1", "V2").
cref0	Character. The Coordinate Reference System (CRS) for the x- and y-coordinates in geographic space. The default is WGS84 "EPSG:4326".
cref1	Optional, character. The Coordinate Reference System (CRS) to spatially project the x- and y-coordinates in geographic space.
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.
upper_sd	Optional, numeric. Upper cut-off value for the standard deviation of log relative risk value in the color key. The default is no limit, and the color key will include the maximum value of the standard deviation surface.
digits	Optional, integer. The number of significant digits for the color key labels using the round function (default is 1).
...	Arguments passed to image.plot for additional graphical features.

Value

This function produces four plots in a two-dimensional space where the axes are the two specified covariates: 1) mean of the log relative risk, 2) standard deviation of the log relative risk, 3) mean of the asymptotically normal p-value, and 4) proportion of iterations were statistically significant based on a two-tailed alpha-level threshold. If predict = TRUE, this function produces an additional four plots of the summary statistics above in a two-dimensional geographic space where the axes are longitude and latitude.

Examples

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

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

# Covariate data (centered and scaled)
  ims <- spatstat.data::bei.extra
  ims[[1]]$v <- scale(ims[[1]]$v)
  ims[[2]]$v <- scale(ims[[2]]$v)
  
# Presence data
  presence <- spatstat.data::bei
  spatstat.geom::marks(presence) <- data.frame("presence" = rep(1, presence$n),
                                              "lon" = presence$x,
                                              "lat" = presence$y)
                                          
# (Pseudo-)Absence data
  absence <- spatstat.random::rpoispp(0.008, win = ims[[1]])
  spatstat.geom::marks(absence) <- data.frame("presence" = rep(0, absence$n),
                                              "lon" = absence$x,
                                              "lat" = absence$y)
# Combine into readable format
  obs_locs <- spatstat.geom::superimpose(presence, absence, check = FALSE)
  spatstat.geom::marks(obs_locs)$id <- seq(1, obs_locs$n, 1)
  spatstat.geom::marks(obs_locs) <- spatstat.geom::marks(obs_locs)[ , c(4, 2, 3, 1)]
 
# Specify categories for varying degrees of spatial uncertainty
## Creates three groups
  spatstat.geom::marks(obs_locs)$levels <- as.factor(stats::rpois(obs_locs$n,
                                                                  lambda = 0.05))
                                                                  
# Run perlrren
  test_perlrren <- perlrren(obs_ppp = obs_locs,
                            covariates = ims,
                            radii = c(10, 100, 500),
                            n_sim = 10)
                            
# Run plot_perturb                             
  plot_perturb(input = test_perlrren)
}

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