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100 | 1 | ▼aPagès, Jérôme.▲ | |
245 | 1 | 0 | ▼aMultiple factor analysis by example using R▼h[electronic resource] /▼cJérôme Pagès.▲ |
260 | ▼aBoca Raton :▼bCRC Press, ▼c©2015.▲ | ||
300 | ▼a1 online resource :▼billustrations.▲ | ||
336 | ▼atext▼btxt▼2rdacontent▲ | ||
337 | ▼acomputer▼bc▼2rdamedia▲ | ||
338 | ▼aonline resource▼bcr▼2rdacarrier▲ | ||
490 | 1 | ▼aChapman & Hall/CRC the R series▲ | |
504 | ▼aIncludes bibliographical references.▲ | ||
505 | 0 | ▼a1. 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.▲ | |
506 | ▼aOwing to Legal Deposit regulations this resource may only be accessed from within National Library of Scotland. For more information contact enquiries@nls.uk.▼5StEdNL▲ | ||
520 | ▼aMultiple 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.▲ | ||
588 | 0 | ▼aOnline resource; title from PDF title page (ebrary, viewed November 10, 2014).▲ | |
588 | 0 | ▼aOnline resource; title from PDF title page (EBSCO, viewed December 16, 2014).▲ | |
590 | ▼aeBooks on EBSCOhost▼bAll EBSCO eBooks▲ | ||
650 | 0 | ▼aR (Computer program language)▼xStatistical methods.▲ | |
650 | 0 | ▼aFactor analysis.▲ | |
650 | 7 | ▼aMATHEMATICS▼xApplied.▼2bisacsh▲ | |
650 | 7 | ▼aMATHEMATICS▼xProbability & Statistics▼xGeneral.▼2bisacsh▲ | |
650 | 7 | ▼aFactor analysis.▼2fast▼0(OCoLC)fst01432040▲ | |
650 | 7 | ▼aR (Computer program language)▼2fast▼0(OCoLC)fst01086207▲ | |
655 | 4 | ▼aElectronic books.▲ | |
776 | 0 | 8 | ▼iPrint version:▼aPagès, Jérôme.▼tMultiple Factor Analysis by Example Using R.▼dHoboken : Taylor and Francis, ©2014▼z9781482205473▲ |
830 | 0 | ▼aChapman & Hall/CRC the R series (CRC Press)▲ | |
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Multiple factor analysis by example using R
자료유형
국외eBook
서명/책임사항
Multiple factor analysis by example using R [electronic resource] / Jérôme Pagès.
발행사항
Boca Raton : CRC Press , ©2015.
형태사항
1 online resource : illustrations.
서지주기
Includes bibliographical references.
내용주기
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.
요약주기
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.
주제
기타형태저록
ISBN
9781482205480 1482205483 1482205475 9781482205473 9781322635521 1322635528
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