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Extract spatial predictors from spatial model

Source: R/get_spatial_predictors.R
get_spatial_predictors.Rd

Extracts the spatial predictors (Moran's Eigenvector Maps) used in a model fitted with rf_spatial().

Usage

get_spatial_predictors(model)

Arguments

model

Model object from rf_spatial() (must have class rf_spatial).

Value

Data frame containing the spatial predictor values for each observation, with predictors ordered by decreasing importance.

Details

Spatial predictors are Moran's Eigenvector Maps (MEMs) automatically generated and selected by rf_spatial() to capture spatial autocorrelation patterns in the data. This function extracts these predictors, which can be useful for understanding spatial structure or for making predictions on new spatial locations.

See also

rf_spatial(), mem(), mem_multithreshold(), get_importance()

Other model_info: get_evaluation(), get_importance(), get_importance_local(), get_moran(), get_performance(), get_predictions(), get_residuals(), get_response_curves(), print.rf(), print_evaluation(), print_importance(), print_moran(), print_performance()

Examples

data(plants_rf_spatial)

# Extract spatial predictors
spatial_preds <- get_spatial_predictors(plants_rf_spatial)
head(spatial_preds)
#>   spatial_predictor_100_2 spatial_predictor_100_14 spatial_predictor_100_11
#> 1            -0.003914286              0.008308019               0.14698873
#> 2            -0.038636431              0.015520890              -0.02946903
#> 3             0.029156255             -0.017986416               0.00471761
#> 4            -0.004807265             -0.029052199              -0.01524803
#> 5            -0.001751023              0.086958559               0.17600097
#> 6            -0.001383862              0.025988150               0.14927785
#>   spatial_predictor_1000_66 spatial_predictor_1000_34 spatial_predictor_2000_13
#> 1               0.026757354               -0.01452556              -0.002455888
#> 2              -0.031106869               -0.08013209              -0.075605529
#> 3              -0.082326152               -0.02198527               0.004448900
#> 4              -0.083501222               -0.05692688              -0.028564751
#> 5               0.003938521                0.02887879               0.028114077
#> 6              -0.038232356                0.05676607               0.017592450
#>   spatial_predictor_100_16 spatial_predictor_1000_64 spatial_predictor_100_5
#> 1               0.16702874              -0.009341237             0.037762397
#> 2               0.04396603              -0.032701838            -0.285607771
#> 3              -0.01153076              -0.047211284             0.001661393
#> 4              -0.08005054              -0.032450026            -0.005621210
#> 5               0.13447905              -0.017794081             0.074424222
#> 6               0.07136303              -0.032329644             0.039083455
#>   spatial_predictor_1000_33
#> 1                0.03137691
#> 2               -0.05337929
#> 3               -0.11382103
#> 4                0.06537506
#> 5               -0.02509947
#> 6                0.02115162

# Check dimensions
dim(spatial_preds)
#> [1] 227  10

# View predictor names (ordered by importance)
colnames(spatial_preds)
#>  [1] "spatial_predictor_100_2"   "spatial_predictor_100_14" 
#>  [3] "spatial_predictor_100_11"  "spatial_predictor_1000_66"
#>  [5] "spatial_predictor_1000_34" "spatial_predictor_2000_13"
#>  [7] "spatial_predictor_100_16"  "spatial_predictor_1000_64"
#>  [9] "spatial_predictor_100_5"   "spatial_predictor_1000_33"