Evaluates the contribution of the predictors to model transferability via spatial cross-validation. The function returns the median increase or decrease in a given evaluation metric (R2, pseudo R2, RMSE, nRMSE, or AUC) when a variable is introduced in a model, by comparing and evaluating via spatial cross-validation models with and without the given variable. This function was devised to provide importance scores that would be less sensitive to spatial autocorrelation than those computed internally by random forest on the out-of-bag data. This function is experimental.

rf_importance( model = NULL, xy = NULL, repetitions = 30, training.fraction = 0.75, metric = c("r.squared", "pseudo.r.squared", "rmse", "nrmse", "auc"), distance.step = NULL, distance.step.x = NULL, distance.step.y = NULL, fill.color = viridis::viridis(100, option = "F", direction = -1, alpha = 1, end = 0.9), line.color = "white", seed = 1, verbose = TRUE, n.cores = parallel::detectCores() - 1, cluster = NULL )

model | Model fitted with |
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xy | Data frame or matrix with two columns containing coordinates and named "x" and "y". If |

repetitions | Integer, number of spatial folds to use during cross-validation. Must be lower than the total number of rows available in the model's data. Default: |

training.fraction | Proportion between 0.5 and 0.9 indicating the proportion of records to be used as training set during spatial cross-validation. Default: |

metric | Character, nams of the performance metric to use. The possible values are: "r.squared" ( |

distance.step | Numeric, argument |

distance.step.x | Numeric, argument |

distance.step.y | Numeric, argument |

fill.color | Character vector with hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. |

line.color | Character string, color of the line produced by |

seed | Integer, random seed to facilitate reproduciblity. If set to a given number, the results of the function are always the same. Default: |

verbose | Logical. If |

n.cores | Integer, number of cores to use for parallel execution. Creates a socket cluster with |

cluster | A cluster definition generated with |

The input model with new data in its "importance" slot. The new importance scores are included in the data frame `model$importance$per.variable`

, under the column names "importance.cv" (median contribution to transferability over spatial cross-validation repetitions), "importance.cv.mad" (median absolute deviation of the performance scores over spatial cross-validation repetitions), "importance.cv.percent" ("importance.cv" expressed as a percent, taking the full model's performance as baseline), and "importance.cv.mad" (median absolute deviation of "importance.cv"). The plot is stored as "cv.per.variable.plot".

if(interactive()){ #loading example data data(plant_richness_df) data(distance_matrix) xy <- plant_richness_df[, c("x", "y")] #fitting random forest model rf.model <- rf( data = plant_richness_df, dependent.variable.name = "richness_species_vascular", predictor.variable.names = colnames(plant_richness_df)[5:21], distance.matrix = distance_matrix, distance.thresholds = 0, xy = xy, n.cores = 1, verbose = FALSE ) #computing predictor contribution to model transferability rf.model <- rf_importance(rf.model) }