| factor.analysis {SEMgraph} | R Documentation |
Factor analysis for high dimensional data
Description
Wrapper for Factor Analysis with potentially high dimensional variables implement in the "cate" R package (Author: Jingshu Wang [aut], Qingyuan Zhao [aut, cre] Maintainer: Qingyuan Zhao <qz280@cam.ac.uk>) 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 = 1, method = "pc")
Arguments
Y |
data matrix, a n*p matrix |
r |
number of factors (default, r =1) |
method |
algorithm to be used, "pc" (default) or "ml" |
Details
The two methods extracted from "cate" are quasi-maximum likelihood (ml), and principal component analysis (pc). The ml is iteratively solved the EM algorithm using the PCA solution as the initial value. See Bai and Li (2012) for more details.
Value
a list of objects
- Gamma
estimated factor loadings
- Z
estimated latent factors
- Sigma
estimated noise variance matrix
References
Jushan Bai and Kunpeng Li (2012). Statistical Analysis of Factor Models of High Dimension. The Annals of Statistics, 40 (1), 436-465 <https://doi.org/10.1214/11-AOS966>
Jingshu Wang and Qingyuan Zhao (2020). cate: High Dimensional Factor Analysis and Confounder Adjusted Testing and Estimation. R package version 1.1.1. <https://CRAN.R-project.org/package=cate>
Examples
# Nonparanormal(npn) transformation
als.npn <- transformData(alsData$exprs)$data
## pc
pc<- factor.analysis(Y = als.npn, r = 2, method = "pc")
head(pc$Gamma)
head(pc$Z)
head(pc$Sigma)
## ml
ml <- factor.analysis(Y = als.npn, r = 2, method = "ml")
head(ml$Gamma)
head(ml$Z)
head(ml$Sigma)