Trains and evaluates one or more registered diagnostic models on a given dataset.
Usage
models_dia(
data,
model = "all_dia",
tune = FALSE,
seed = 123,
threshold_choices = "default",
positive_label_value = 1,
negative_label_value = 0,
new_positive_label = "Positive",
new_negative_label = "Negative"
)Arguments
- data
A data frame where the first column is the sample ID, the second is the outcome label, and subsequent columns are features.
- model
A character string or vector of character strings, specifying which models to run. Use "all_dia" to run all registered models.
- tune
Logical, whether to enable hyperparameter tuning for individual models.
- seed
An integer, for reproducibility of random processes.
- threshold_choices
A character string (e.g., "f1", "youden", "default") or a numeric value (0-1), or a named list/vector allowing different threshold strategies/values for each model.
- positive_label_value
A numeric or character value in the raw data representing the positive class.
- negative_label_value
A numeric or character value in the raw data representing the negative class.
- new_positive_label
A character string, the desired factor level name for the positive class (e.g., "Positive").
- new_negative_label
A character string, the desired factor level name for the negative class (e.g., "Negative").
Value
A named list, where each element corresponds to a run model and
contains its trained model_object, sample_score data frame, and
evaluation_metrics.
Examples
# \donttest{
# This example assumes your package includes a dataset named 'train_dia'.
# If not, you should create a toy data frame similar to the one below.
#
# train_dia <- data.frame(
# ID = paste0("Patient", 1:100),
# Disease_Status = sample(c(0, 1), 100, replace = TRUE),
# FeatureA = rnorm(100),
# FeatureB = runif(100)
# )
# Ensure the 'train_dia' dataset is available in the environment
# For example, if it is exported by your package:
# data(train_dia)
# Check if 'train_dia' exists, otherwise skip the example
if (exists("train_dia")) {
# 1. Initialize the modeling system
initialize_modeling_system_dia()
# 2. Run selected models
results <- models_dia(
data = train_dia,
model = c("rf", "lasso"), # Run only Random Forest and Lasso
threshold_choices = list(rf = "f1", lasso = 0.6), # Different thresholds
positive_label_value = 1,
negative_label_value = 0,
new_positive_label = "Case",
new_negative_label = "Control",
seed = 42
)
# 3. Print summaries
for (model_name in names(results)) {
print_model_summary_dia(model_name, results[[model_name]])
}
}
#> Diagnostic modeling system already initialized.
#> Running model: rf
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
#> Running model: lasso
#>
#> --- rf Model (on Training Data) Metrics ---
#>
#> AUROC: 1.0000 (95% CI: 1.0000 - 1.0000)
#> AUPRC: 1.0000
#> Accuracy: 1.0000
#> F1: 1.0000
#> Precision: 1.0000
#> Recall: 1.0000
#> Specificity: 1.0000
#> --------------------------------------------------
#>
#> --- lasso Model (on Training Data) Metrics ---
#>
#> AUROC: 0.9946 (95% CI: 0.9910 - 0.9983)
#> AUPRC: 0.9995
#> Accuracy: 0.9722
#> F1: 0.9847
#> Precision: 0.9822
#> Recall: 0.9872
#> Specificity: 0.8250
#> --------------------------------------------------
# }