Run random forest, returning only diagnostic values from spatial cross validation.

Random forest is run via randomForest::randomForest().

Usage

make_rf_diagnostics_spatialcv(
  env_df,
  clust_col = "cluster",
  folds = 3L,
  reps = 5L,
  range_min = 20000,
  range_max = 1e+05,
  coords = c("long", "lat"),
  crs_df = 4283,
  crs_analysis = 3577,
  ...
)

Arguments

env_df

Dataframe with clusters and environmental columns.

clust_col

Character. Name of column with cluster membership.

folds

Numeric. How many folds to use in cross-validation?

reps

Numeric. How many repeats of cross-validation?

range_min, range_max

Numeric. Metres. Minimum and maximum grid sizes for spatial tiles. Reps will be made using range <- seq(range_min, range_max , length.out = reps)

coords

Character vector of length 2. Names of columns in env_df with x and y coordinates.

crs_df

Coordinate reference system for coords. Passed to the crs argument of sf::st_as_sf().

...

passed to make_rf_good().