sce()
and sca())."sce" and
"sca" (previously "SCE" and
"SCA"). Method names follow (print.sce,
predict.sca, etc.).Training_data -> training_data,
Testing_data -> testing_dataX -> x, Y ->
y, X_sample -> x_sampleNmin -> nmin, Ntree ->
ntreePredictors -> predictors,
Predictant -> predictant,
Simulations -> simulationsOOB_weight -> oob_weightMax_merge_iter ->
max_merge_iter, Weak_L ->
weak_lsce(), sca(),
rfe_sce(), model_simulation(),
sca_tree_predict(), sce_model_evaluation(),
sca_model_evaluation(), wilks_importance(), S3
methods predict.*, importance.*,
evaluate.*, etc.model_simulation(), sca_tree_predict(),
sce_model_evaluation(),
sca_model_evaluation(), wilks_importance(),
sca_importance(), rfe_sce(),
plot_rfe(), and internal helpers such as
sce_prediction(), training_prediction(),
oob_validation(), gof(),
nse_equation(), kge_equation(),
inference()..RDS / .RData
models created with older versions store the previous class
names; assign new classes before using S3 methods,
e.g. class(obj) <- "sce" or
class(obj) <- "sca" as appropriate.sce(parallel = TRUE) no longer fails on single-core
machines (or when Ntree == 1); it now falls back to
sequential execution instead of leaving the result vector undefined. The
cluster is also released via on.exit() so a worker error no
longer leaks the cluster.README.md, tutorial script, and manual pages to
match the new function and dataset names.digits argument for variable importance outputs
Wilks_importance() and SCA_importance()
now round Relative_Importance to digits
decimal places (default: 2)importance.SCE() and
importance.SCA() accept and forward the digits
argumentman/importance.Rd to document the
digits parameter and defaultimportance(..., digits = 2)digits to control roundingevaluate() S3
methods
evaluate.SCA() now automatically retrieves predictants
from the model object (no need to specify)evaluate.SCA() now specifically warns if
Training_data is provided (not needed for SCA
evaluation)evaluate.SCE() now automatically retrieves predictants
from the model object (no need to specify)print(), summary(),
predict(), importance(), and
evaluate() methods for S3 objectsPlot_RFE() function for visualizing recursive
feature elimination resultsModel_simulation() and
SCA_tree_predict() functionslegend,
lines) for plotting
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