data_clean() now becomes check_data(). This function performs preliminary sanity check before imputation. A few checks have been added.show_var() function for compatibility with vismi package.nhanes3_newborn has been replaced with newborn, with variables renamed for clarity and to align with the vismi package.nhanes3 have been updated to maintain consistency with newborn.save.models = TRUE in mixgb(), the return object now includes the mean and variance of imputed values for each variable for each iteration. This would allow users to plot convergence diagnostics using package vismi.mixgb()xgb.train():
num_class now passed through params list, need to set num_class=NULL for non-multiclass imputation.mixgb_null() function for better readability and maintainability - using helper functions in impute_each.R. Other relevant functions will be updated later.-framework Accelerate flag for Linux builds in Makevars filexgb.train():
num_class now passed through params list.reshape = TRUE argument in predict() function as it is deprecated in XGBoost >= 2.0.0.related to xgb.train():
watchlist is changed to evalsobjective and eval_metric are now passed through params list.related to xgb.cv():
sparse.model.matrix into xgb.DMatrix format. Note, this is a quick fix for minimal safe change, and we plan to further optimise data handling in future releases.best.nrounds is now obtained via cv.train$early_stop$best_iteration instead of cv.train$best_iteration.cbind2(Mis.m, Obs.m) that occurred when the imputed dataset had only a single incomplete variable.
Mis.m (a matrix of all other incomplete variables except the currently imputed one) was a 0x0 matrix, which caused error during binding.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.