Information retention summary statistics
info_retention_stats(scaling_result, class_name_field)data frame with scaling results returned by function mdgp_scale
column name in the scaling results data frame that contains the scaled class names
A list with two data frames. First data frame contains class-specific cell class name (class_name), class frequencies (freq), class raster id (class_id), class proportion (prop), information retention mean (inf_retention_mn) and standard deviations (inf_retention_sd). The second data frame contains the mean (mean) and standard deviation (sd) for the scaled landscape across all classes.
# load categorical raster
r <- terra::rast(system.file("extdata/nlm_mid_geom_r3_sa0.tif", package = "landscapeScaling"))
# subset the raster to the lower 300 by 300 pixels
r_sub <- terra::crop(r,terra::ext(0,300,0,300))
# generate relative abundance for the scaled grid
rel_abund <- relative_abundance_scaled_grid(r_sub,class_field='cover',scale_factor=15)
head(rel_abund)
#> x y A B C
#> 1 0 0 4.8888889 38.22222 56.88889
#> 2 15 0 10.2222222 38.66667 51.11111
#> 3 30 0 20.0000000 42.66667 37.33333
#> 4 45 0 24.8888889 40.44444 34.66667
#> 5 60 0 0.8888889 28.00000 71.11111
#> 6 75 0 17.7777778 37.33333 44.88889
# classify relative abundance samples to multi-dimensional grid points
mdgp_result <- mdgp_scale(rel_abund,parts=3,rpr_threshold=10,monotypic_threshold=90)
#> [1] "number of cells: 400"
#> [1] "number of grid points: 10"
#> [1] "number of grid points remaining: 5"
head(mdgp_result)
#> cls A B C x_y prc_inf_agr class_name
#> 1 10 0.000 8.444 91.556 285_0 91.556 C100
#> 2 10 0.000 9.333 90.667 195_30 90.667 C100
#> 3 10 0.444 12.444 87.111 60_285 87.111 C100
#> 4 9 4.889 38.222 56.889 0_0 90.222 C67_x_B33
#> 5 9 10.222 38.667 51.111 15_0 84.444 C67_x_B33
#> 6 9 10.667 42.667 46.667 225_0 80.000 C67_x_B33
# generate class-specific and landscape scale information retention statistics
infRetStats <- info_retention_stats(mdgp_result,'class_name')
print(infRetStats)
#> [[1]]
#> class_name freq class_id prop inf_retention_mn inf_retention_sd
#> 1 A33_x_B33_x_C33 165 1 0.4125 84.129 3.814
#> 2 A67_x_B33 122 2 0.3050 87.417 6.519
#> 3 B67_x_A33 29 3 0.0725 82.038 2.214
#> 4 C100 3 4 0.0075 89.778 2.352
#> 5 C67_x_B33 81 5 0.2025 88.093 5.132
#>
#> [[2]]
#> mean sd
#> information_retention_landscape 85.826 5.356
#>