Fits several random forest models on the same data in order to capture the effect of the algorithm's stochasticity on the variable importance scores, predictions, residuals, and performance measures. The function relies on the median to aggregate performance and importance values across repetitions. It is recommended to use it after a model is fitted (`rf()`

or `rf_spatial()`

), tuned (`rf_tuning()`

), and/or evaluated (`rf_evaluate()`

). This function is designed to be used after fitting a model with `rf()`

or `rf_spatial()`

, tuning it with `rf_tuning()`

and evaluating it with `rf_evaluate()`

.

```
rf_repeat(
model = NULL,
data = NULL,
dependent.variable.name = NULL,
predictor.variable.names = NULL,
distance.matrix = NULL,
distance.thresholds = NULL,
xy = NULL,
ranger.arguments = NULL,
scaled.importance = FALSE,
repetitions = 10,
keep.models = TRUE,
seed = 1,
verbose = TRUE,
n.cores = parallel::detectCores() - 1,
cluster = NULL
)
```

- model
A model fitted with

`rf()`

. If provided, the data and ranger arguments are taken directly from the model definition (stored in`model$ranger.arguments`

). Default:`NULL`

- data
Data frame with a response variable and a set of predictors. Default:

`NULL`

- dependent.variable.name
Character string with the name of the response variable. Must be in the column names of

`data`

. If the dependent variable is binary with values 1 and 0, the argument`case.weights`

of`ranger`

is populated by the function`case_weights()`

. Default:`NULL`

- predictor.variable.names
Character vector with the names of the predictive variables. Every element of this vector must be in the column names of

`data`

. Default:`NULL`

- distance.matrix
Squared matrix with the distances among the records in

`data`

. The number of rows of`distance.matrix`

and`data`

must be the same. If not provided, the computation of the Moran's I of the residuals is omitted. Default:`NULL`

- distance.thresholds
Numeric vector with neighborhood distances. All distances in the distance matrix below each value in

`dustance.thresholds`

are set to 0 for the computation of Moran's I. If`NULL`

, it defaults to seq(0, max(distance.matrix), length.out = 4). Default:`NULL`

- xy
(optional) Data frame or matrix with two columns containing coordinates and named "x" and "y". It is not used by this function, but it is stored in the slot

`ranger.arguments$xy`

of the model, so it can be used by`rf_evaluate()`

and`rf_tuning()`

. Default:`NULL`

- ranger.arguments
Named list with ranger arguments (other arguments of this function can also go here). All ranger arguments are set to their default values except for 'importance', that is set to 'permutation' rather than 'none'. Please, consult the help file of ranger if you are not familiar with the arguments of this function.

- scaled.importance
Logical. If

`TRUE`

, and 'importance = "permutation', the function scales 'data' with scale and fits a new model to compute scaled variable importance scores. Default:`FALSE`

- repetitions
Integer, number of random forest models to fit. Default:

`10`

- keep.models
Logical, if

`TRUE`

, the fitted models are returned in the`models`

slot. Set to`FALSE`

if the accumulation of models is creating issues with the RAM memory available. Default:`TRUE`

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

`1`

.- verbose
Logical, ff

`TRUE`

, messages and plots generated during the execution of the function are displayed, Default:`TRUE`

- n.cores
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`

- cluster
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`

A ranger model with several new slots:

`ranger.arguments`

: Stores the values of the arguments used to fit the ranger model.`importance`

: A list containing a data frame with the predictors ordered by their importance, a ggplot showing the importance values, and local importance scores.`performance`

: out-of-bag performance scores: R squared, pseudo R squared, RMSE, and normalized RMSE (NRMSE).`pseudo.r.squared`

: computed as the correlation between the observations and the predictions.`residuals`

: residuals, normality test of the residuals computed with`residuals_test()`

, and spatial autocorrelation of the residuals computed with`moran_multithreshold()`

.

```
if(interactive()){
#loading example data
data(plant_richness_df)
data(distance_matrix)
#fitting 5 random forest models
out <- rf_repeat(
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,
repetitions = 5,
n.cores = 1
)
#data frame with ordered variable importance
out$importance$per.variable
#per repetition
out$importance$per.repetition
#variable importance plot
out$importance$per.repetition.plot
#performance
out$performance
#spatial correlation of the residuals for different distance thresholds
out$spatial.correlation.residuals$per.distance
#plot of the Moran's I of the residuals for different distance thresholds
out$spatial.correlation.residuals$plot
#using a model as an input for rf_repeat()
rf.model <- rf(
data = plant_richness_df,
dependent.variable.name = "richness_species_vascular",
predictor.variable.names = colnames(plant_richness_df)[8:21],
distance.matrix = distance_matrix,
distance.thresholds = 0,
n.cores = 1
)
#repeating the model 5 times
rf.repeat <- rf_repeat(
model = rf.model,
n.cores = 1
)
rf.repeat$performance
rf.repeat$importance$per.repetition.plot
}
```