aorsf (version 0.1.4)

orsf_update: Update Forest Parameters

Description

Update Forest Parameters

Usage

orsf_update(object, ..., modify_in_place = FALSE, no_fit = NULL)

Value

an ObliqueForest object.

Arguments

object

(ObliqueForest) an oblique random forest object (see orsf).

...

arguments to plug into orsf that will be used to define the update. These arguments include:

• data

• formula

• control

• weights

• n_tree

• n_split

• n_retry

• n_thread

• mtry

• sample_with_replacement

• sample_fraction

• leaf_min_events

• leaf_min_obs

• split_rule

• split_min_events

• split_min_obs

• split_min_stat

• pred_type

• oobag_pred_horizon

• oobag_eval_every

• oobag_fun

• importance

• importance_max_pvalue

• group_factors

• tree_seeds

• na_action

• verbose_progress

Note that you can update control, but you cannot change the type of forest. For example, you can't go from classification to regression with orsf_update.

modify_in_place

(logical) if TRUE, object will be modified by the inputs specified in .... Be cautious, as modification in place will overwrite existing data. If FALSE (the default), object will be copied and then the modifications will be applied to the copy, leaving the original object unmodified.

no_fit

(logical) if TRUE, model fitting steps are defined and saved, but training is not initiated. The object returned can be directly submitted to orsf_train() so long as attach_data is TRUE.

Details

There are several dynamic inputs in orsf with default values of NULL. Specifically, these inputs are control, weights, mtry, split_rule, split_min_stat, pred_type, pred_horizon, oobag_eval_function, tree_seeds, and oobag_eval_every. If no explicit value is given for these inputs in the call, they will be re-formed. For example, if an initial forest includes 17 predictors, the default mtry is the smallest integer that is greater than or equal to the square root of 17, i.e., 5. Then, if you make an updated forest with 1 less predictor and you do not explicitly say mtry = 5, then mtry will be re-initialized in the update based on the available 16 predictors, and the resulting value of mtry will be 4. This is done to avoid many potential errors that would occur if the dynamic outputs were not re-initialized.

Examples

if (FALSE) {
# initial fit has mtry of 5
fit <- orsf(pbc_orsf, time + status ~ . -id)

# note that mtry is now 4 (see details)
fit_new <- orsf_update(fit, formula = . ~ . - edema, n_tree = 100)

# prevent dynamic updates by specifying inputs you want to freeze.
fit_newer <- orsf_update(fit_new, mtry = 2)
}