Plot Adaptive Landscape (3D Perspective)

Usage

plot_adaptive_landscape_3d(
  landscape,
  trait_cols,
  theta = -30,
  phi = 30,
  grid_n = 200,
  color_palette = NULL,
  ...
)

Arguments

landscape: Output object of class "adaptive_landscape".
trait_cols: Character vector of length 2 specifying the trait column names.
theta: Numeric azimuthal viewing angle. Default is -30.
phi: Numeric colatitude viewing angle. Default is 30.
grid_n: Ignored; the landscape's own grid is used.
color_palette: Optional vector of colors for the surface. Defaults to viridis plasma.
...: Additional arguments passed to fields::drape.plot().

Value

A 3D plot produced by fields::drape.plot().

Examples

if (requireNamespace("fields", quietly = TRUE)) {
  prep <- prepare_selection_data(bumpus, "survival", c("total_length", "weight"))
  surf <- correlated_fitness_surface(prep, "survival", c("total_length", "weight"), grid_n = 30)
  land <- adaptive_landscape(prep, surf$model, c("total_length", "weight"),
                             simulation_n = 100, grid_n = 15)
  plot_adaptive_landscape_3d(land, c("total_length", "weight"))
}
#> Data type: binary; method: gam; n = 136; k = 29
#> GAM fitting with 136 observations
#>   Trying formula: main
#> Success with formula: main
#> Predictions range: 0.0472 to 0.703
#> Masked 424 of 900 grid points outside the data
#> Population mean grid ranges:
#>   total_length: -2.96 to 2.94
#>   weight: -3.12 to 4.85
#> Estimated within-population variance-covariance:
#>              total_length weight
#> total_length       1.0000 0.5839
#> weight             0.5839 1.0000
#> Calculating mean fitness for 225 grid points
#> Optimal population mean phenotype:
#>   total_length    weight
#> 7   -0.4336396 -3.121579
#> Mean fitness at optimum: 0.6821
#> The highest mean fitness is on the edge of the grid; the landscape may keep rising beyond it
#> 75% of simulated individuals fell outside the data (99% at the optimum)
#> The optimum rests on extrapolation: more than 25% of the population simulated there lies outside the data