Correlplot (version 1.1.0)

pfa: Principal factor analysis

Description

Program pfa performs (iterative) principal factor analysis, which is based on the computation of eigenvalues of the reduced correlation matrix.

Usage

pfa(X, option = "data", m = 2, initial.communality = "R2", crit = 0.001, verbose = FALSE)

Value

Res

Matrix of residuals

Psi

Diagonal matrix with specific variances

La

Matrix of loadings

Shat

Estimated correlation matrix

Fs

Factor scores

Arguments

X

A data matrix or correlation matrix

option

Specifies the type of matrix supplied by argument X. Values for option are data, cor or cov. data is the default.

m

The number of factors to extract (2 by default)

initial.communality

Method for computing initial communalites. Possibilities are R2 or maxcor.

crit

The criterion for convergence. The default is 0.001. A smaller value will require more iterations before convergence is reached.

verbose

When set to TRUE, additional numerical output is shown.

Author

Jan Graffelman ([e-mail])

References

Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979) Multivariate analysis.

Rencher, A.C. (1995) Methods of multivriate analysis.

Satorra, A. and Neudecker, H. (1998) Least-Squares Approximation of off-Diagonal Elements of a Variance Matrix in the Context of Factor Analysis. Econometric Theory 14(1) pp. 156--157.

See Also

princomp

Examples

   X <- matrix(rnorm(100),ncol=2)
   out.pfa <- pfa(X)
#  based on a correlation matrix
   R <- cor(X)
   out.pfa <- pfa(R,option="cor")