Uses rf_evaluate() to compare the performance of several models on independent spatial folds via spatial cross-validation.

rf_compare(
models = NULL,
xy = NULL,
repetitions = 30,
training.fraction = 0.75,
metrics = 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 = 0.8),
line.color = "gray30",
seed = 1,
verbose = TRUE,
n.cores = parallel::detectCores() - 1,
cluster = NULL
)

## Arguments

models Named list with models resulting from rf(), rf_spatial(), rf_tuning(), or rf_evaluate(). Example: models = list(a = model.a, b = model.b). Default: NULL Data frame or matrix with two columns containing coordinates and named "x" and "y". Default: NULL 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: 30 Proportion between 0.5 and 0.9 indicating the proportion of records to be used as training set during spatial cross-validation. Default: 0.75 Character vector, names of the performance metrics selected. The possible values are: "r.squared" (cor(obs, pred) ^ 2), "pseudo.r.squared" (cor(obs, pred)), "rmse" (sqrt(sum((obs - pred)^2)/length(obs))), "nrmse" (rmse/(quantile(obs, 0.75) - quantile(obs, 0.25))). Default: c("r.squared", "pseudo.r.squared", "rmse", "nrmse") Numeric, argument distance.step of thinning_til_n(). distance step used during the selection of the centers of the training folds. These fold centers are selected by thinning the data until a number of folds equal or lower than repetitions is reached. Its default value is 1/1000th the maximum distance within records in xy. Reduce it if the number of training folds is lower than expected. Numeric, argument distance.step.x of make_spatial_folds(). Distance step used during the growth in the x axis of the buffers defining the training folds. Default: NULL (1/1000th the range of the x coordinates). Numeric, argument distance.step.x of make_spatial_folds(). Distance step used during the growth in the y axis of the buffers defining the training folds. Default: NULL (1/1000th the range of the y coordinates). Character vector with hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. viridis::viridis(100)). Default: viridis::viridis(100, option = "F", direction = -1) Character string, color of the line produced by ggplot2::geom_smooth(). Default: "gray30" Integer, random seed to facilitate reproduciblity. If set to a given number, the results of the function are always the same. Default: 1. Logical. If TRUE, messages and plots generated during the execution of the function are displayed, Default: TRUE Integer, number of cores to use for parallel execution. Creates a socket cluster with parallel::makeCluster(), runs operations in parallel with foreach and %dopar%, and stops the cluster with parallel::clusterStop() when the job is done. Default: parallel::detectCores() - 1 A cluster definition generated with parallel::makeCluster(). If provided, overrides n.cores. When cluster = NULL (default value), and model is provided, the cluster in model, if any, is used instead. If this cluster is NULL, then the function uses n.cores instead. The function does not stop a provided cluster, so it should be stopped with parallel::stopCluster() afterwards. The cluster definition is stored in the output list under the name "cluster" so it can be passed to other functions via the model argument, or using the %>% pipe. Default: NULL

## Value

A list with three slots:

• comparison.df: Data frame with one performance value per spatial fold, metric, and model.

• spatial.folds: List with the indices of the training and testing records for each evaluation repetition.

• plot: Violin-plot of comparison.df.

rf_evaluate()

## Examples

if(interactive()){

data(distance_matrix)
data(plant_richness_df)

#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,
n.cores = 1
)

#fitting a spatial model with Moran's Eigenvector Maps
rf.spatial <- rf_spatial(
model = rf.model,
n.cores = 1
)

#comparing the spatial and non spatial models
comparison <- rf_compare(
models = list(
Non spatial = rf.model,
Spatial = rf.spatial
),
xy = plant_richness_df[, c("x", "y")],
metrics = c("r.squared", "rmse"),
n.cores = 1
)

}