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cat2cat (version 0.4.7)

Handling an Inconsistently Coded Categorical Variable in a Longitudinal Dataset

Description

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) .

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Install

install.packages('cat2cat')

Monthly Downloads

301

Version

0.4.7

License

GPL (>= 2) | file LICENSE

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Repository

https://github.com/Polkas/cat2cat

Homepage

https://polkas.github.io/cat2cat/

Maintainer

Maciej Nasinski

Last Published

January 22nd, 2024

Functions in cat2cat (0.4.7)

Adjusted summary for linear regression when based on replicated dataset

trans dataset containing mappings (transitions) between old (2008) and new (2010) occupational codes. This table could be used to map encodings in both directions.

validate_cover_cats

Validate if the trans table contains all proper mappings

verticals2 dataset

verticals dataset

Summary plots for cat2cat results

Occupational dataset - small one

Add default cat2cat columns to a `data.frame`

Applying frequencies to the object returned by the `get_mappings` function

Make a combination of weights from different methods

Automatic mapping in a panel dataset

The internal function used in the cat2cat one

Transforming a mapping (transition) table to two associative lists

Function to check cat2cat ml models performance

Manual mapping for an aggregated panel dataset

Pruning which could be useful after the mapping process

Getting frequencies from a vector with an optional multiplier

Occupational dataset

resolve_frequencies

Resolve the frequencies