000			02006nam a2200265Ia 4500
003			OSt
005			20250812101330.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9781482205473
037			_cTextual
040			_aCSL _beng _cCSL
041			_aeng
084			_aB28 Q5 TB _qCSL
100			_aPages, Jerome _eauthor _9817766
245		0	_aMultiple factor analysis by example using R
260			_aBoca Raton : _bCRC Press. _c2015,
300			_axiv, 257p. _b: ill.
500			_aBibliography 249-251p.; Index 253-257p.
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 next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.
650			_a Matrix calculus _9817767
650			_a Qualitative and mixed data _9817768
650			_aWeighting groups of variables _9817769
942			_hB28 Q5 TB _cTEXL _2CC _n0
999			_c6560 _d6560