pfa: Principal factor analysis

In Correlplot: A Collection of Functions for Graphing Correlation Matrices

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)

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.

Value

Res	Matrix of residuals
Psi	Diagonal matrix with specific variances
La	Matrix of loadings
Shat	Estimated correlation matrix
Fs	Factor scores

Author(s)

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")

Correlplot documentation built on June 22, 2024, 11:34 a.m.