Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10282862" target="_blank" >RIV/00216208:11320/14:10282862 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31160/14:00045373

Výsledek na webu

<a href="http://www.jstatsoft.org/v59/i12/" target="_blank" >http://www.jstatsoft.org/v59/i12/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data

Popis výsledku v původním jazyce

R package mixAK originally implemented routines primarily for Bayesian estimation of finite normal mixture models for possibly interval-censored data. The functionality of the package was considerably enhanced by implementing methods for Bayesian estimation of mixtures of multivariate generalized linear mixed models proposed in Komarek and Komarkova (2013). Among other things, this allows for a cluster analysis (classification) based on multivariate continuous and discrete longitudinal data that arise whenever multiple outcomes of a different nature are recorded in a longitudinal study. This package also allows for a data-driven selection of a number of clusters as methods for selecting a number of mixture components were implemented. A model and clustering methodology for multivariate continuous and discrete longitudinal data is overviewed. Further, a step-by-step cluster analysis based jointly on three longitudinal variables of different types (continuous, count, dichotomous) is give

Název v anglickém jazyce

Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data

Popis výsledku anglicky

R package mixAK originally implemented routines primarily for Bayesian estimation of finite normal mixture models for possibly interval-censored data. The functionality of the package was considerably enhanced by implementing methods for Bayesian estimation of mixtures of multivariate generalized linear mixed models proposed in Komarek and Komarkova (2013). Among other things, this allows for a cluster analysis (classification) based on multivariate continuous and discrete longitudinal data that arise whenever multiple outcomes of a different nature are recorded in a longitudinal study. This package also allows for a data-driven selection of a number of clusters as methods for selecting a number of mixture components were implemented. A model and clustering methodology for multivariate continuous and discrete longitudinal data is overviewed. Further, a step-by-step cluster analysis based jointly on three longitudinal variables of different types (continuous, count, dichotomous) is give

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GAP403%2F12%2F1557" target="_blank" >GAP403/12/1557: Přístupy k identifikaci faktorů výkonnosti podniků s důrazem na metody výběru příznaků ve statistickém rozpoznávání.</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Statistical Software [online]

ISSN

1548-7660

e-ISSN

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Svazek periodika

59

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

38

Strana od-do

1-38

Kód UT WoS článku

000341807200001

EID výsledku v databázi Scopus

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