FA {MVar}

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

Mingot, 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)