factor.analysis: Factor analysis

In cate: High Dimensional Factor Analysis and Confounder Adjusted Testing and Estimation

Description

The main function for factor analysis with potentially high dimensional variables. Here we implement some recent algorithms that is optimized for the high dimensional problem where the number of samples n is less than the number of variables p.

Usage

factor.analysis(Y, r, method = c("ml", "pc", "esa"))

Arguments

Y	data matrix, a n*p matrix
r	number of factors
method	algorithm to be used

Details

The three methods are quasi-maximum likelihood (ml), principal component analysis (pc), and factor analysis using an early stopping criterion (esa).

The ml is iteratively solved the Expectation-Maximization algorithm using the PCA solution as the initial value. See Bai and Li (2012) and for more details. For the esa method, see Owen and Wang (2015) for more details.

Value

a list of objects

Gamma

estimated factor loadings

Z

estimated latent factors

Sigma

estimated noise variance matrix

References

Bai, J. and Li, K. (2012). Statistical analysis of factor models of high dimension. The Annals of Statistics 40, 436-465. Owen, A. B. and Wang, J. (2015). Bi-cross-validation for factor analysis. arXiv:1503.03515.

See Also

fa.pc, fa.em, ESA

Examples

## a factor model
n <- 100
p <- 1000
r <- 5
Z <- matrix(rnorm(n * r), n, r)
Gamma <- matrix(rnorm(p * r), p, r)
Y <- Z %*% t(Gamma) + rnorm(n * p)

## to check the results, verify the true factors are in the linear span of the estimated factors.
pc.results <- factor.analysis(Y, r = 5, "pc")
sapply(summary(lm(Z ~ pc.results$Z)), function(x) x$r.squared)

ml.results <- factor.analysis(Y, r = 5, "ml")
sapply(summary(lm(Z ~ ml.results$Z)), function(x) x$r.squared)

esa.results <- factor.analysis(Y, r = 5, "esa")
sapply(summary(lm(Z ~ esa.results$Z)), function(x) x$r.squared)

cate documentation built on July 2, 2020, 4:08 a.m.