cat2cat_ml

apply the ml models to the cat2cat data

internal

Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table.
The main rule is to replicate the observation if it could be assigned to a few categories.
Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them.
This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.

Maciej Nasinski

cat2cat

Handling an Inconsistently Coded Categorical Variable in a
Longitudinal Dataset

cat2cat_ml function

<dl><dt>ml</dt>
<dd>`list` the same `ml` argument as provided to `cat2cat` function.</dd>
<dt>mapp</dt>
<dd>`list` a mapping table</dd>
<dt>target_data</dt>
<dd>`data.frame`</dd>
<dt>cat_var_target</dt>
<dd>`character(1)` name of the categorical variable
in the target period.</dd></dl>

Arguments

The internal function used in the cat2cat one — cat2cat_ml

<dl>

<dt>ml</dt>
<dd>`list` the same `ml` argument as provided to `cat2cat` function.</dd>


<dt>mapp</dt>
<dd>`list` a mapping table</dd>


<dt>target_data</dt>
<dd>`data.frame`</dd>


<dt>cat_var_target</dt>
<dd>`character(1)` name of the categorical variable
in the target period.</dd>

</dl>

cat2cat_ml: The internal function used in the cat2cat one

Description

Usage

Arguments