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Prepares variable importance objects for spatial models

Source: R/prepare_importance_spatial.R
prepare_importance_spatial.Rd

Prepares variable importance data frames and plots for models fitted with rf_spatial().

Usage

prepare_importance_spatial(model)

Arguments

model

An importance data frame with spatial predictors, or a model fitted with rf_spatial().

Value

A list with importance data frames in different formats depending on whether the model was fitted with rf() or rf_repeat().

See also

Other utilities: .vif_to_df(), auc(), beowulf_cluster(), objects_size(), optimization_function(), rescale_vector(), root_mean_squared_error(), setup_parallel_execution(), standard_error(), statistical_mode(), thinning(), thinning_til_n()

Examples


data(plants_rf_spatial)

prepare_importance_spatial(plants_rf_spatial) %>%
  head()
#> $per.variable
#>                           variable importance
#> 1                 human_population   2062.953
#> 2          spatial_predictor_100_2   1313.871
#> 3          human_footprint_average   1284.529
#> 4             climate_bio1_average   1268.570
#> 5              climate_hypervolume   1138.002
#> 6                    bias_area_km2    965.209
#> 7          spatial_predictor_100_5    845.060
#> 8         human_population_density    753.136
#> 9                  neighbors_count    731.238
#> 10        spatial_predictor_100_11    621.663
#> 11    climate_velocity_lgm_average    595.414
#> 12         bias_species_per_record    592.786
#> 13        spatial_predictor_100_16    592.116
#> 14                  neighbors_area    568.468
#> 15   neighbors_percent_shared_edge    500.680
#> 16    topography_elevation_average    420.887
#> 17   climate_aridity_index_average    395.820
#> 18        spatial_predictor_100_14    321.777
#> 19          fragmentation_cohesion    302.940
#> 20       spatial_predictor_2000_13    302.758
#> 21       spatial_predictor_1000_66    270.484
#> 22       spatial_predictor_1000_33    222.343
#> 23       spatial_predictor_1000_64    127.906
#> 24       spatial_predictor_1000_34     99.112
#> 25          fragmentation_division     45.441
#> 26 landcover_herbs_percent_average   -196.250
#> 27           climate_bio15_minimum   -202.283
#> 
#> $per.variable.plot

#> 
#> $spatial.predictors
#>                           variable importance
#> 2               spatial_predictors   1313.871
#> 7               spatial_predictors    845.060
#> 10              spatial_predictors    621.663
#> 13              spatial_predictors    592.116
#> 18              spatial_predictors    321.777
#> 20              spatial_predictors    302.758
#> 21              spatial_predictors    270.484
#> 22              spatial_predictors    222.343
#> 23              spatial_predictors    127.906
#> 24              spatial_predictors     99.112
#> 1                 human_population   2062.953
#> 3          human_footprint_average   1284.529
#> 4             climate_bio1_average   1268.570
#> 5              climate_hypervolume   1138.002
#> 6                    bias_area_km2    965.209
#> 8         human_population_density    753.136
#> 9                  neighbors_count    731.238
#> 11    climate_velocity_lgm_average    595.414
#> 12         bias_species_per_record    592.786
#> 14                  neighbors_area    568.468
#> 15   neighbors_percent_shared_edge    500.680
#> 16    topography_elevation_average    420.887
#> 17   climate_aridity_index_average    395.820
#> 19          fragmentation_cohesion    302.940
#> 25          fragmentation_division     45.441
#> 26 landcover_herbs_percent_average   -196.250
#> 27           climate_bio15_minimum   -202.283
#> 
#> $spatial.predictors.plot

#> 
#> $spatial.predictors.stats
#>                              variable importance
#> 1                    human_population  2062.9530
#> 13           spatial_predictors (max)  1313.8710
#> 3             human_footprint_average  1284.5290
#> 4                climate_bio1_average  1268.5700
#> 5                 climate_hypervolume  1138.0020
#> 6                       bias_area_km2   965.2090
#> 8            human_population_density   753.1360
#> 9                     neighbors_count   731.2380
#> 51 spatial_predictors (quantile 0.75)   614.2763
#> 11       climate_velocity_lgm_average   595.4140
#> 12            bias_species_per_record   592.7860
#> 14                     neighbors_area   568.4680
#> 15      neighbors_percent_shared_edge   500.6800
#> 16       topography_elevation_average   420.8870
#> 17      climate_aridity_index_average   395.8200
#> 31        spatial_predictors (median)   312.2675
#> 19             fragmentation_cohesion   302.9400
#> 41 spatial_predictors (quantile 0.25)   234.3782
#> 2            spatial_predictors (min)    99.1120
#> 25             fragmentation_division    45.4410
#> 26    landcover_herbs_percent_average  -196.2500
#> 27              climate_bio15_minimum  -202.2830
#> 
#> $spatial.predictors.stats.plot

#>