FA_English: Factor Analysis (FA).

In MVar: Multivariate Analysis

Factor Analysis (FA).

Description

Performs factorial analysis (FA) in a data set.

Usage

FA(data, method = "PC", type = 2, nfactor = 1, rotation = "None",
   scoresobs = "Bartlett", converg = 1e-5, iteracao = 1000, 
   testfit = TRUE) 

Arguments

data	Data to be analyzed.
method	Method of analysis: "PC" - Principal Components (default), "PF" - Principal Factor, "ML" - Maximum Likelihood.
type	1 for analysis using the covariance matrix, 2 for analysis using the correlation matrix (default).
rotation	Type of rotation: "None" (default), "Varimax" and "Promax".
nfactor	Number of factors (default = 1).
scoresobs	Type of scores for the observations: "Bartlett" (default) or "Regression".
converg	Limit value for convergence to sum of the squares of the residuals for Maximum likelihood method (default = 1e-5).
iteracao	Maximum number of iterations for Maximum Likelihood method (default = 1000).
testfit	Tests the model fit to the method of Maximum Likelihood (default = TRUE).

Value

mtxMC	Matrix of correlation / covariance.
mtxAutvlr	Matrix of eigenvalues.
mtxAutvec	Matrix of eigenvectors.
mtxvar	Matrix of variances and proportions.
mtxcarga	Matrix of factor loadings.
mtxvaresp	Matrix of specific variances.
mtxcomuna	Matrix of commonalities.
mtxresidue	Matrix of residues.
vlrsqrs	Upper limit value for sum of squares of the residues.
vlrsqr	Sum of squares of the residues.
mtxresult	Matrix with all associated results.
mtxscores	Matrix with scores of the observations.
coefscores	Matrix with the scores of the coefficients of the factors.

Author(s)

Paulo Cesar Ossani

Marcelo Angelo Cirillo

References

MINGOTI, S. A. Analise de dados atraves de metodos de estatistica multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005. 297 p.

Kaiser, H. F.The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187-200, 1958.

RENCHER, A. C. Methods of multivariate analysis. 2th. ed. New York: J.Wiley, 2002. 708 p.

FERREIRA, D. F. Estatistica Multivariada. 2a ed. revisada e ampliada. Lavras: Editora UFLA, 2011. 676 p.

See Also

Plot.FA

Examples

data(DataQuan) # data set

data <- DataQuan[,2:ncol(DataQuan)]

rownames(data) <- DataQuan[,1]

res <- FA(data, method = "PC", type = 2, nfactor = 3, rotation = "None",
          scoresobs = "Bartlett", converg = 1e-5, iteracao = 1000, 
          testfit = TRUE) 

print("Matrix with all associated results:"); round(res$mtxresult,3)

print("Sum of squares of the residues:"); round(res$vlrsqr,3)

print("Matrix of the factor loadings.:"); round(res$mtxcarga,3)

print("Matrix with scores of the observations:"); round(res$mtxscores,3)

print("Matrix with the scores of the coefficients of the factors:"); round(res$coefscores,3)

MVar documentation built on Aug. 19, 2023, 5:12 p.m.