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This guide provides comprehensive parameter documentation for all E2E functions.
E2E includes example datasets for both diagnostic and prognostic modeling:
Trains base classification models for diagnostic tasks. Parameters:
data
(required): Data frame with sample names
(column 1), outcomes 0/1 (column 2), features (columns 3+)
model
(required): Character vector of model names or
“all_dia” for all models
tune
: Logical (default FALSE). Whether to perform
hyperparameter tuning
threshold_choices
: Threshold selection method
seed
: Integer (default 123). Random seed for
reproducibility
Bootstrap aggregating ensemble method. Parameters:
data
(required): Training data frame
base_model_name
(required): Base model name (e.g.,
“xb”, “rf”)
n_estimators
: Integer (default 50). Number of base
models
subset_fraction
: Numeric (default 0.632). Bootstrap
sampling fraction
tune_base_model
: Logical (default FALSE). Tune base
models
threshold_choices
: Same as models_dia()
seed
: Integer (default 123). Random seed
Voting ensemble combining multiple models. Parameters:
results_all_models
(required): Output from
models_dia()
data
(required): Training data
type
: Voting type
weight_metric
: String (default “AUROC”). Metric for
soft voting weights
top
: Integer (default 5). Number of top models to
use
threshold_choices
: Same as models_dia()
seed
: Integer (default 123). Random seed
Stacking ensemble with meta-model. Parameters:
results_all_models
(required): Output from
models_dia()
data
(required): Training data
meta_model_name
(required): Meta-model name (e.g.,
“lasso”, “gbm”)
top
: Integer (default 5). Number of top base
models
tune_meta
: Logical (default FALSE). Tune
meta-model
threshold_choices
: Same as models_dia()
seed
: Integer (default 123). Random seed
Handles imbalanced datasets using EasyEnsemble-like algorithm. Parameters:
data
(required): Imbalanced training data
base_model_name
(required): Base model for balanced
subsets
n_estimators
: Integer (default 10). Number of
balanced subsets
tune_base_model
: Logical (default FALSE). Tune base
models
threshold_choices
: Same as models_dia()
seed
: Integer (default 123). Random seed
Applies trained model to new data. Parameters:
trained_model_object
(required): Trained model
object from E2E functions
new_data
(required): New data for prediction (sample
IDs in column 1)
label_col_name
: String (default NULL). True label
column name if available
Evaluates model predictions. Parameters:
prediction_df
(required): Prediction data frame from
apply_dia()
threshold_choices
: Same as models_dia()
Trains base survival models. Parameters:
data
(required): Data frame with sample ID, survival
status, time, features
model
(required): Model names or “all_pro” for all
models
tune
: Logical (default FALSE). Hyperparameter
tuning
time_unit
: String (default “day”). Time unit (“day”,
“month”, “year”)
years_to_evaluate
: Numeric vector (default
c(1,3,5)). Time points for time-dependent AUROC
seed
: Integer (default 789). Random seed
Stacking ensemble for survival analysis. Parameters:
results_all_models
(required): Output from
models_pro()
data
(required): Training data
meta_model_name
(required): Meta-model name
top
: Integer (default 3). Number of top base
models
tune_meta
: Logical (default FALSE). Tune
meta-model
time_unit
: String (default “day”). Time
unit
years_to_evaluate
: Numeric vector (default
c(1,3,5)). Evaluation time points
seed
: Integer (default 789). Random seed
Bootstrap aggregating for survival analysis. Parameters:
data
(required): Training data
base_model_name
(required): Base model name
n_estimators
: Integer (default 10). Number of base
models
subset_fraction
: Numeric (default 0.632). Bootstrap
sampling fraction
tune_base_model
: Logical (default FALSE). Tune base
models
time_unit
: String (default “day”). Time
unit
years_to_evaluate
: Numeric vector (default
c(1,3,5)). Evaluation time points
seed
: Integer (default 456). Random seed
Applies trained survival model to new data. Parameters:
trained_model_object
(required): Trained model
object
new_data
(required): New data with same structure as
training data
time_unit
: String (default “day”). Time
unit
Evaluates survival model predictions. Parameters:
prediction_df
(required): Prediction data frame from
apply_pro()
years_to_evaluate
: Numeric vector (default
c(1,3,5)). Evaluation time points
Creates diagnostic model evaluation plots. Parameters:
type
(required): Plot type
data
(required): Model results objectCreates prognostic model evaluation plots. Parameters:
type
(required): Plot type
data
(required): Model results object
time_unit
: String (default “days”). Time unit for
axis labels
Creates SHAP interpretation plots. Parameters:
data
(required): Model results with sample_score
data frame
raw_data
(required): Original feature data
target_type
(required): Data type
Registers custom algorithms.
Usage: 1. Define custom function following E2E
conventions 2. Register with
register_model_dia("model_name", custom_function)
3. Use
registered model in E2E workflows
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.