MFA | R Documentation |
Perform Multiple Factor Analysis (MFA) on groups of variables. The groups of variables can be quantitative, qualitative, frequency (MFACT) data, or mixed data.
MFA(data, groups, typegroups = rep("n",length(groups)), namegroups = NULL)
data |
Data to be analyzed. |
groups |
Number of columns for each group in order following the order of data in 'data'. |
typegroups |
Type of group: |
namegroups |
Names for each group. |
vtrG |
Vector with the sizes of each group. |
vtrNG |
Vector with the names of each group. |
vtrplin |
Vector with the values used to balance the lines of the Z matrix. |
vtrpcol |
Vector with the values used to balance the columns of the Z matrix. |
mtxZ |
Matrix concatenated and balanced. |
mtxA |
Matrix of the eigenvalues (variances) with the proportions and proportions accumulated. |
mtxU |
Matrix U of the singular decomposition of the matrix Z. |
mtxV |
Matrix V of the singular decomposition of the matrix Z. |
mtxF |
Matrix global factor scores where the lines are the observations and the columns the components. |
mtxEFG |
Matrix of the factor scores by group. |
mtxCCP |
Matrix of the correlation of the principal components with original variables. |
mtxEV |
Matrix of the partial inertias / scores of the variables |
Paulo Cesar Ossani
Marcelo Angelo Cirillo
Abdessemed, L.; Escofier, B. Analyse factorielle multiple de tableaux de frequencies: comparaison avec l'analyse canonique des correspondences. Journal de la Societe de Statistique de Paris, Paris, v. 137, n. 2, p. 3-18, 1996..
Abdi, H. Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In: SALKIND, N. J. (Ed.). Encyclopedia of measurement and statistics. Thousand Oaks: Sage, 2007. p. 907-912.
Abdi, H.; Valentin, D. Multiple factor analysis (MFA). In: SALKIND, N. J. (Ed.). Encyclopedia of measurement and statistics. Thousand Oaks: Sage, 2007. p. 657-663.
Abdi, H.; Williams, L. Principal component analysis. WIREs Computational Statatistics, New York, v. 2, n. 4, p. 433-459, July/Aug. 2010.
Abdi, H.; Williams, L.; Valentin, D. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Computational Statatistics, New York, v. 5, n. 2, p. 149-179, Feb. 2013.
Becue-Bertaut, M.; Pages, J. A principal axes method for comparing contingency tables: MFACT. Computational Statistics & data Analysis, New York, v. 45, n. 3, p. 481-503, Feb. 2004
Becue-Bertaut, M.; Pages, J. Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data. Computational Statistics & data Analysis, New York, v. 52, n. 6, p. 3255-3268, Feb. 2008.
Bezecri, J. Analyse de l'inertie intraclasse par l'analyse d'un tableau de contingence: intra-classinertia analysis through the analysis of a contingency table. Les Cahiers de l'Analyse des Donnees, Paris, v. 8, n. 3, p. 351-358, 1983.
Escofier, B. Analyse factorielle en reference a un modele: application a l'analyse d'un tableau d'echanges. Revue de Statistique Appliquee, Paris, v. 32, n. 4, p. 25-36, 1984.
Escofier, B.; Drouet, D. Analyse des differences entre plusieurs tableaux de frequence. Les Cahiers de l'Analyse des Donnees, Paris, v. 8, n. 4, p. 491-499, 1983.
Escofier, B.; Pages, J. Analyse factorielles simples et multiples. Paris: Dunod, 1990. 267 p.
Escofier, B.; Pages, J. Analyses factorielles simples et multiples: objectifs, methodes et interpretation. 4th ed. Paris: Dunod, 2008. 318 p.
Escofier, B.; Pages, J. Comparaison de groupes de variables definies sur le meme ensemble d'individus: un exemple d'applications. Le Chesnay: Institut National de Recherche en Informatique et en Automatique, 1982. 121 p.
Escofier, B.; Pages, J. Multiple factor analysis (AFUMULT package). Computational Statistics & data Analysis, New York, v. 18, n. 1, p. 121-140, Aug. 1994
Greenacre, M.; Blasius, J. Multiple correspondence analysis and related methods. New York: Taylor and Francis, 2006. 607 p.
Ossani, P. C.; Cirillo, M. A.; Borem, F. M.; Ribeiro, D. E.; Cortez, R. M. Quality of specialty coffees: a sensory evaluation by consumers using the MFACT technique. Revista Ciencia Agronomica (UFC. Online), v. 48, p. 92-100, 2017.
Pages, J. Analyse factorielle multiple appliquee aux variables qualitatives et aux donnees mixtes. Revue de Statistique Appliquee, Paris, v. 50, n. 4, p. 5-37, 2002.
Pages, J.. Multiple factor analysis: main features and application to sensory data. Revista Colombiana de Estadistica, Bogota, v. 27, n. 1, p. 1-26, 2004.
Plot.MFA
data(DataMix) # mixed dataset
data <- DataMix[,2:ncol(DataMix)]
rownames(data) <- DataMix[1:nrow(DataMix),1]
group.names = c("Grade Cafes/Work", "Formation/Dedication", "Coffees")
mf <- MFA(data = data, c(2,2,2), typegroups = c("n","c","f"), group.names) # performs MFA
print("Principal Component Variances:"); round(mf$mtxA,2)
print("Matrix of the Partial Inertia / Score of the Variables:"); round(mf$mtxEV,2)
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