vismi packagedrop.unused.levels = FALSE in fac2sparse() to prevent dropping unused levels in factor or ordinal factor.
save_yhatobs() for Type 1 pmm.mixgb() for large datasets:
mixgb(). Users can still use bootstrap in the archived function mixgb0().PMM is now set to NULL by default.xgb.save() and xgb.load() from XGBoost.device.gpu_id and predictor.tree_method = "hist" by default, aligning with XGBoost 2.0.0.save.models.folder in mixgb().
xgb.save(), a method recommended by XGBoost for future compatibility.save.models.folder is specified, the return object of mixgb() includes the current imputed datasets, directories for imputation models, and relevant parameters. This object can save using saveRDS() as it doesn't directly contain the models. Users can later load this object into R and employ impute_new(object, newdata, ...) for new data imputation.Enhances mixgb(data,...) to support datasets with diverse data types:
Note: Users must manually convert character variables to factors.
default_params(), an auxiliary function for mixgb(), to validate the list of XGBoost hyperparameters supplied by the user. It simplifies hyperparameter modifications without requiring explicit specification of all default values.plot_hist() and plot_bar() to align with changes in ggplot2 3.4.0:
..density.. with after_stat(density) in plot_hist()...prop.. with after_stat(prop) in plot_bar().nthread = 2 to comply with CRAN policies.subsample = 0.7, becomes the default method due to identified biases with bootstrapping in certain scenarios.
mixgb():
subsample = 0.7.bootstrap = FALSE.createNA() function.mixgb():
ordinalAsInteger: Changes from TRUE to FALSE.max_depth: Changes from 6 to 3.nrounds: Changes from 50 to 100.bootstrap: Sets to TRUE by default.mixgb as xgboost requires OpenMP for multi-core operations. For details, please refer to OpenMP for Mac.