Compare Correlated Fitness Surface vs Adaptive Landscape — compare_fitness_surfaces

Compares individual-level fitness (correlated surface) to population-level mean fitness (adaptive landscape).

Usage

compare_fitness_surfaces_data(
  correlated_surface,
  adaptive_landscape,
  trait_cols,
  calculate_correlation = TRUE
)

Arguments

correlated_surface: Output list from correlated_fitness_surface().
adaptive_landscape: Output list from adaptive landscape modeling.
trait_cols: A character vector of length 2 specifying the trait column names.
calculate_correlation: Logical indicating whether to calculate the correlation between the two surfaces.

Value

A list containing combined grid data, summary stats, optima points, and correlation metrics.

Examples

prep <- prepare_selection_data(bumpus, "survival", c("total_length", "weight"))
surf <- correlated_fitness_surface(prep, "survival", c("total_length", "weight"), grid_n = 30)
#> 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
land <- adaptive_landscape(prep, surf$model, c("total_length", "weight"),
                           simulation_n = 100, grid_n = 15)
#> 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: 0.6725
#> The highest mean fitness is on the edge of the grid; the landscape may keep rising beyond it
#> 76% of simulated individuals fell outside the data (97% at the optimum)
#> The optimum rests on extrapolation: more than 25% of the population simulated there lies outside the data
comp <- compare_fitness_surfaces_data(surf, land, c("total_length", "weight"))
#> Warning: Grid sizes differ: correlated surface has 900 points, adaptive landscape has 225 points.
#> Comparison may be affected. Consider using same grid_n.
#> Individual optimum:
#>    total_length    weight   fitness
#> 72   -0.5207948 -1.590066 0.7030432
#> Population optimum:
#>   total_length    weight   fitness
#> 7   -0.4336396 -3.121579 0.6724595
#> Distance between optima: 1.534
#> Summary statistics:
#>              Surface Fitness_Range_Min Fitness_Range_Max Fitness_Mean
#> 1 Correlated Fitness        0.07483220         0.7030432    0.4668537
#> 2 Adaptive Landscape        0.02567243         0.6724595    0.3835101
#>   Fitness_SD N_Points
#> 1  0.1865585      900
#> 2  0.1927433      225
plot_fitness_surfaces_comparison(comp)
#> $side_by_side
#> Warning: Removed 424 rows containing non-finite outside the scale range
#> (`stat_contour_filled()`).

#> 
#> $overlay
#> Warning: Removed 424 rows containing non-finite outside the scale range
#> (`stat_contour()`).

#>