summary.metComp

Print summary of the comparison of the imputation methods.

Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.

Maciej Romaniuk

FuzzyImputationTest

Imputation Procedures and Quality Tests for Fuzzy Data

summary.metComp function

<dl><dt>object</dt>
<dd>Object of S3 class <code>metComp</code>.</dd>
<dt>...</dt>
<dd>Additional parameters passed to other functions.</dd></dl>

Arguments

Print summary of the comparison of the imputation methods. — summary.metComp

<dl>

<dt>object</dt>
<dd>Object of S3 class <code>metComp</code>.</dd>


<dt>...</dt>
<dd>Additional parameters passed to other functions.</dd>

</dl>

summary.metComp: Print summary of the comparison of the imputation methods.

Description

Usage

Arguments