find_best_combination_logistic

This function searches for the best combination of cutoffs that minimizes the Akaike Information Criterion (AIC).

Provides a recursive gradient scanning algorithm for
discretizing continuous variables in Logistic and Cox regression
models. This algorithm is especially effective in identifying
optimal cut-points for variables with U-shaped relationships to
'lnOR' (the natural logarithm of the odds ratio) or 'lnHR' (the
natural logarithm of the hazard ratio), thereby enhancing model
fit, interpretability, and predictive power. By iteratively
scanning and calculating gradient changes, the method accurately
pinpoints critical cut-points within nonlinear relationships,
transforming continuous variables into categorical ones. This
approach improves risk classification and regression analysis
performance, increasing interpretability and practical relevance
in clinical and risk management settings.

Shuo Yang

Recursive Gradient Scanning Algorithm

Yi Fei

Jinxin Zhang

find_best_combination_logistic function

<dl><dt>my_data</dt>
<dd>A dataframe containing the data.</dd>
<dt>cutoffs_list</dt>
<dd>A list of dataframes containing the cutoffs for each variable.</dd>
<dt>formula</dt>
<dd>An object of class "formula" describing the model structure.</dd></dl>

Arguments

Find best combination of cutoffs for logistic regression — find_best_combination_logistic

<dl>

<dt>my_data</dt>
<dd>A dataframe containing the data.</dd>


<dt>cutoffs_list</dt>
<dd>A list of dataframes containing the cutoffs for each variable.</dd>


<dt>formula</dt>
<dd>An object of class "formula" describing the model structure.</dd>

</dl>

find_best_combination_logistic: Find best combination of cutoffs for logistic regression

Description

Usage

Value

Arguments