A convenient wrapper for ranger that completes its output by providing the Moran's I of the residuals for different distance thresholds, the rmse and nrmse (as computed by `root_mean_squared_error()`

), and variable importance scores based on a scaled version of the data generated by scale.

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

. Optionally, the result of`auto_cor()`

or`auto_vif()`

. 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'. The ranger arguments

`x`

,`y`

, and`formula`

are disabled. Please, consult the help file of ranger if you are not familiar with the arguments of this function.- scaled.importance
Logical, if

`TRUE`

, the function scales`data`

with scale and fits a new model to compute scaled variable importance scores. This makes variable importance scores of different models somewhat comparable. Default:`FALSE`

- seed
Integer, random seed to facilitate reproducibility. If set to a given number, the returned model is always the same. Default:

`1`

- verbose
Boolean. If TRUE, messages and plots generated during the execution of the function are displayed. Default:

`TRUE`

- n.cores
Integer, number of cores to use. Default:

`parallel::detectCores() - 1`

- cluster
A cluster definition generated with

`parallel::makeCluster()`

. This function does not use the cluster, but can pass it on to other functions when using the`%>%`

pipe. It will be stored in the slot`cluster`

of the output list. Default:`NULL`

A ranger model with several extra 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 (difference in accuracy between permuted and non permuted variables for every case, computed on the out-of-bag data).`performance`

: performance scores: R squared on out-of-bag data, R squared (cor(observed, predicted) ^ 2), pseudo R squared (cor(observed, predicted)), RMSE, and normalized RMSE (NRMSE).`residuals`

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

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

.

Please read the help file of ranger for further details. Notice that the `formula`

interface of ranger is supported through `ranger.arguments`

, but variable interactions are not allowed (but check `the_feature_engineer()`

).

```
if(interactive()){
#loading example data
data("plant_richness_df")
data("distance_matrix")
#fittind random forest model
out <- 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
)
class(out)
#data frame with ordered variable importance
out$importance$per.variable
#variable importance plot
out$importance$per.variable.plot
#performance
out$performance
#spatial correlation of the residuals
out$spatial.correlation.residuals$per.distance
#plot of the Moran's I of the residuals for different distance thresholds
out$spatial.correlation.residuals$plot
#predictions for new data as done with ranger models:
predicted <- stats::predict(
object = out,
data = plant_richness_df,
type = "response"
)$predictions
#alternative data input methods
###############################
#ranger.arguments can contain ranger arguments and any other rf argument
my.ranger.arguments <- list(
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 = c(0, 1000)
)
#fitting model with these ranger arguments
out <- rf(
ranger.arguments = my.ranger.arguments,
n.cores = 1
)
}
```