facanal: Factor analysis via Expectation Maximization

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

This function fits an exploratory factor analysis model using expectation maximization using the method of Bai & Li (2012) to estimate the maximum likelihood solution.

Usage

facanal(
  Y,
  nfac = min(nrow(Y) - 3, ncol(Y) - 2),
  rotate = GPArotation::Varimax,
  scale = T,
  max.iter = 4000,
  tol = 1e-10
)

Arguments

Y	a numeric matrix or data frame of only numeric variables.
nfac	the number of factors to extract.
rotate	a rotation function from the GPArotation package. Defaults to Varimax.
scale	should the variables be scaled prior to analysis? Defaults to TRUE.
max.iter	maximum number of iterations. defaults to 4000.
tol	a tolerance value for convergence. defaults to 1e-10.

References

Bai, J. and Li, K. (2012). Statistical analysis of factor models of high dimension. The Annals of Statistics 40, 436-465.

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.