Fit a random forest via ranger

Fits a regression random forest on an sf dataset and returns it as a spatial_fit, so it works with cv_spatial, predict_surface, area_of_applicability and the plot() method like any other backend.

Usage

fit_rf_model(
  data_sf,
  response_var,
  predictor_vars,
  num_trees = 500L,
  mtry = NULL,
  min_node_size = NULL,
  importance = c("permutation", "impurity", "none"),
  include_coords = FALSE,
  replace = TRUE,
  sample_fraction = NULL,
  seed = 123L,
  num_threads = NULL,
  .already_prepped = FALSE,
  ...
)

Arguments

data_sf: An sf object with response, predictors and geometry.
response_var: Response column name.
predictor_vars: Predictor column names.
num_trees: Number of trees. Default 500.
mtry: Predictors sampled per split. NULL uses ranger's default.
min_node_size: Minimum node size. NULL uses ranger's default.
importance: "permutation" (default), "impurity" or "none". Impurity importance is biased toward continuous and high-cardinality predictors (Strobl et al. 2007), which is why permutation is the default despite costing more.
include_coords: Add the coordinates as predictors. Default FALSE; see above.
replace: Logical; grow each tree on a bootstrap sample drawn with replacement (TRUE, ranger's default and this one) or on a subsample drawn without replacement (FALSE). Strobl et al. (2007) show that bootstrap sampling with replacement is itself a source of bias in variable importance toward predictors with many distinct values or categories, and recommend subsampling without replacement. The forest's predictions change slightly with the choice; the default is kept at ranger's so that an existing script fits the same forest, and the setting is recorded in $info and printed with the fit.
sample_fraction: Fraction of rows drawn for each tree. NULL (default) uses ranger's rule: all rows when replace = TRUE, 0.632 (the expected share of distinct rows in a bootstrap sample) when replace = FALSE. A single number in (0, 1] overrides it. replace = FALSE with sample_fraction = 1 grows every tree on every row, so nothing is out of bag: the fit warns, and see What fitted() returns.
seed: Seed passed to ranger. Default 123.
num_threads: Threads for ranger. Default NULL means getOption("mc.cores", 1L): one thread unless the session has opted in to more, the convention brms and parallel use. ranger's own default is every core on the machine, which is the wrong default for a package function (a check farm limits jobs to two cores, and cv_rf(parallel = ) would multiply it by the worker count). Predictions do not depend on the thread count, only speed does; pass parallel::detectCores() to use them all.
.already_prepped: Internal; skip prep_model_data() because the caller has already projected and cleaned the data.
...: Passed to ranger::ranger(). ranger's own spellings of the arguments this function already sets (num.trees, min.node.size, num.threads, mtry, importance, seed, replace, sample.fraction, x, y) are rejected with a message naming the wrapper argument to use instead. Passing them here would reach ranger() twice and fail the call.

Value

An rf_fit object (inherits from spatial_fit). $info carries num_trees, mtry, min_node_size, importance_type, importance (a named numeric, or NULL when importance = "none"), include_coords, replace and sample_fraction (the sampling each tree was grown on, with sample_fraction resolved to the number ranger used), oob_rmse and oob_r_squared (each NA_real_ when ranger did not compute it; forwarding oob.error = FALSE through ... is one way to get there), fitted_are_oob (always TRUE; summary() reads it to label its metrics), seed and n_dropped (the rows prep_model_data() removed for missing or non-finite values or a bad geometry, so $n can be read against nrow(data_sf)). The raw forest is in $engine.

Coordinates are not predictors by default

Handing a random forest the x and y coordinates lets it reproduce the training surface almost exactly by memorising location, and then fail badly anywhere it has not seen. Random cross-validation will not catch this: nearby points leak between folds, so the memorised surface scores well. That is how the practice became common. Meyer et al. (2019) show the collapse directly. include_coords therefore defaults to FALSE, and setting it to TRUE logs a caution, once per session (it is a deliberate choice, so it is not raised as an R warning, and it is not repeated for every fold of a cross-validation). If you do use it, score the model with cv_spatial and blocked folds, never with the out-of-bag error.

The out-of-bag error is a random hold-out

ranger's OOB error holds each observation out of the trees that did not sample it. That is a random hold-out, so under spatial autocorrelation it is optimistic for exactly the reason random k-fold is: the trees that "did not see" a point almost certainly saw its neighbours. It is reported as $info$oob_rmse and $info$oob_r_squared and labelled as OOB everywhere it appears. Use cv_rf for a spatial estimate.

What fitted() returns

fitted() on an rf_fit returns out-of-bag predictions, not in-sample ones, following the convention of the random forest packages themselves. In-sample predictions from a forest are close to memorisation and would make summary() report a fictitious R-squared. The consequence is that summary() means something different here than for a gwr_fit or bayesian_fit, whose fitted values are in-sample: do not compare the two directly. compare_models_cv exists for that.

A row that every tree sampled has no out-of-bag prediction, and ranger reports NaN for it. That is every row under replace = FALSE with sample_fraction = 1, and a few under a small num_trees. The fit warns with the count; fitted() and residuals() are NaN on those rows, and summary() says how many rows its metrics were computed on. With no row out of bag at all, the OOB error (NA in $info$oob_rmse and $info$oob_r_squared) and the permutation importance (NaN) are undefined too, and print() says so. cv_rf() scores its fold forests on the held-out rows, never out of bag, so it warns once with the number of folds affected rather than once per fold.

References

Meyer, H., Reudenbach, C., Wöllauer, S. and Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications – moving from data reproduction to spatial prediction. Ecological Modelling 411, 108815. doi:10.1016/j.ecolmodel.2019.108815

Strobl, C., Boulesteix, A.-L., Zeileis, A. and Hothorn, T. (2007). Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 8, 25. doi:10.1186/1471-2105-8-25

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
  library(sf)
  set.seed(1)
  n <- 150
  dat <- st_as_sf(
    data.frame(x = 5e5 + runif(n, 0, 1000), y = 5e6 + runif(n, 0, 1000),
               a = rnorm(n), b = rnorm(n)),
    coords = c("x", "y"), crs = 32632
  )
  dat$z <- 2 * dat$a - dat$b + rnorm(n, 0, 0.3)
  fit <- fit_rf_model(dat, "z", c("a", "b"))
  print(fit)              # print(): only a block's last value shows on its own
  fit$info$importance     # permutation importance, a and b
}
#> <Random Forest (ranger)> spatial model fit
#>   Formula : z ~ a + b
#>   n       : 150
#>   CRS     : EPSG:32632
#>   Trees   : 500 (mtry = 1, min node = 5)
#>   Coords as predictors: no
#>   Sampling: bootstrap, with replacement (100.0% of rows per tree)
#>   OOB RMSE: 0.6995   OOB R^2: 0.9093
#>   Importance (permutation): a=6.886, b=1.583
#> 
#>   OOB is a random hold-out and is optimistic under spatial
#>   autocorrelation; use cv_rf() for a spatial estimate.
#>        a        b 
#> 6.886377 1.583079