Multivariate Analysis of Mixed Data: The R Package PCAmixdata
| hal.structure.identifier | Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL] | |
| dc.contributor.author | CHAVENT, Marie | |
| hal.structure.identifier | Environnement, territoires en transition, infrastructures, sociétés [UR ETTIS] | |
| dc.contributor.author | KUENTZ-SIMONET, Vanessa | |
| hal.structure.identifier | Environnement, territoires en transition, infrastructures, sociétés [UR ETTIS] | |
| dc.contributor.author | LABENNE, Amaury | |
| hal.structure.identifier | Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL] | |
| dc.contributor.author | SARACCO, Jérôme | |
| dc.date.accessioned | 2024-04-04T03:07:37Z | |
| dc.date.available | 2024-04-04T03:07:37Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193459 | |
| dc.description.abstractEn | Mixed data arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends to this type of data standard multivariate analysis methods which allow description, exploration and visualization of the data. The key techniques/methods included in the package are principal component analysis for mixed data (PCAmix), varimax-like orthogonal rotation for PCAmix, and multiple factor analysis for mixed multi-table data. This paper proposes a unified mathematical presentation of the different methods with common notations, as well as providing a summarised presentation of the three algorithms, with details to help the user understand graphical and numerical outputs of the corresponding R functions. This then allows the user to easily provide relevant interpretations of the results obtained. The three main methods are illustrated on a real dataset composed of four data tables characterizing living conditions in different municipalities in the Gironde region of southwest France. | |
| dc.language.iso | en | |
| dc.publisher | ESE - Salento University Publishing | |
| dc.subject.en | mixture of numerical and categorical data | |
| dc.subject.en | PCA | |
| dc.subject.en | multiple correspondence anal- ysis | |
| dc.subject.en | multiple factor analysis | |
| dc.subject.en | varimax rotation | |
| dc.subject.en | R | |
| dc.title.en | Multivariate Analysis of Mixed Data: The R Package PCAmixdata | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1285/i20705948v15n3p606 | |
| dc.subject.hal | Statistiques [stat]/Calcul [stat.CO] | |
| dc.subject.hal | Statistiques [stat]/Machine Learning [stat.ML] | |
| dc.identifier.arxiv | 1411.4911 | |
| bordeaux.journal | Electronic Journal of Applied Statistical Analysis | |
| bordeaux.volume | 15 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.issue | 3 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01662595 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01662595v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Electronic%20Journal%20of%20Applied%20Statistical%20Analysis&rft.date=2022&rft.volume=15&rft.issue=3&rft.au=CHAVENT,%20Marie&KUENTZ-SIMONET,%20Vanessa&LABENNE,%20Amaury&SARACCO,%20J%C3%A9r%C3%B4me&rft.genre=article |
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