median_effect

Calculates a number that represents the overall gradient measurement between the predictor and log-odds of the risk
Calculates a number that represents the overall gradient measurement between the predictor and log-odds of the risk

Implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.

Li Xing

lboxcox

Implementation of Logistic Box-Cox Regression

Shiyu Xu

Jing Wang

Kohlton Booth

Xuekui Zhang

Igor Burstyn

Paul Gustafson

median_effect function

<dl><dt>formula</dt>
<dd>the formula used to train the logistic box-cox model</dd>
<dt>weight_column_name</dt>
<dd>the name of the column in `data` containing the survey weights</dd>
<dt>data</dt>
<dd>dataframe containing the dataset to train on</dd>
<dt>trained_model</dt>
<dd>the already trained model. The output of `lbc_train`</dd></dl>

Arguments

Calculates the "slope" of the Logistic Box-Cox model — median_effect

<dl>

<dt>formula</dt>
<dd>the formula used to train the logistic box-cox model</dd>


<dt>weight_column_name</dt>
<dd>the name of the column in `data` containing the survey weights</dd>


<dt>data</dt>
<dd>dataframe containing the dataset to train on</dd>


<dt>trained_model</dt>
<dd>the already trained model. The output of `lbc_train`</dd>

</dl>

median_effect: Calculates the "slope" of the Logistic Box-Cox model

Description

Usage

Arguments