# Re-initialize a forest model (tracking data structures) from a specific forest in a `ForestContainer` ```r resetForestModel(forest_model, forest, dataset, residual, is_mean_model) ``` ## Arguments - `forest_model`: Forest model with tracking data structures - `forest`: Forest from which to re-initialize forest model - `dataset`: Training dataset object - `residual`: Residual which will also be updated - `is_mean_model`: Whether the model being updated is a conditional mean model ## Returns None ## Examples ```r n <- 100 p <- 10 num_trees <- 100 leaf_dimension <- 1 is_leaf_constant <- TRUE is_exponentiated <- FALSE alpha <- 0.95 beta <- 2.0 min_samples_leaf <- 2 max_depth <- 10 feature_types <- as.integer(rep(0, p)) leaf_model <- 0 sigma2 <- 1.0 leaf_scale <- as.matrix(1.0) variable_weights <- rep(1/p, p) a_forest <- 1 b_forest <- 1 cutpoint_grid_size <- 100 X <- matrix(runif(n*p), ncol = p) forest_dataset <- createForestDataset(X) y <- -5 + 10*(X[,1] > 0.5) + rnorm(n) outcome <- createOutcome(y) rng <- createCppRNG(1234) global_model_config <- createGlobalModelConfig(global_error_variance=sigma2) forest_model_config <- createForestModelConfig(feature_types=feature_types, num_trees=num_trees, num_observations=n, num_features=p, alpha=alpha, beta=beta, min_samples_leaf=min_samples_leaf, max_depth=max_depth, variable_weights=variable_weights, cutpoint_grid_size=cutpoint_grid_size, leaf_model_type=leaf_model, leaf_model_scale=leaf_scale) forest_model <- createForestModel(forest_dataset, forest_model_config, global_model_config) active_forest <- createForest(num_trees, leaf_dimension, is_leaf_constant, is_exponentiated) forest_samples <- createForestSamples(num_trees, leaf_dimension, is_leaf_constant, is_exponentiated) active_forest$prepare_for_sampler(forest_dataset, outcome, forest_model, 0, 0.) forest_model$sample_one_iteration( forest_dataset, outcome, forest_samples, active_forest, rng, forest_model_config, global_model_config, keep_forest = TRUE, gfr = FALSE ) resetActiveForest(active_forest, forest_samples, 0) resetForestModel(forest_model, active_forest, forest_dataset, outcome, TRUE) ```

resetForestModel function