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Multiple factor analysis by example using R /
Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoreti...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
©2015.
©2015 |
| Colección: | Chapman & Hall/CRC the R series (CRC Press)
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Pagès, Jérôme, |e author. | |
| 245 | 1 | 0 | |a Multiple factor analysis by example using R / |c Jérôme Pagès. |
| 260 | |a Boca Raton : |b CRC Press, |c ©2015. | ||
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed November 10, 2014). | |
| 505 | 0 | |a 1. Principal component analysis -- 2. Multiple correspondence analysis -- 3. Factorial analysis of mixed data -- 4. Weighting groups of variables -- 5. Comparing clouds of partial individuals -- 6. Factors common to different groups of variables -- 7. Comparing groups variables and Indscal model -- 8. Qualitative and mixed data -- 9. Multiple factor analysis and Procrustes analysis -- 10. Hierarchial multiple factor analysis -- 11. Matrix calculus and Euclidean vector space. | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed December 16, 2014). | |
| 520 | |a Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a R (Computer program language) |x Statistical methods. | |
| 650 | 0 | |a Factor analysis. | |
| 650 | 6 | |a R (Langage de programmation) |x Méthodes statistiques. | |
| 650 | 6 | |a Analyse factorielle. | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Factor analysis |2 fast | |
| 650 | 7 | |a Analyse factorielle. |2 ram | |
| 650 | 7 | |a R (logiciel) |2 ram | |
| 776 | 0 | 8 | |i Print version: |a Pagès, Jérôme. |t Multiple Factor Analysis by Example Using R. |d Hoboken : Taylor and Francis, ©2014 |z 9781482205473 |
| 830 | 0 | |a Chapman & Hall/CRC the R series (CRC Press) | |
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