apca: Asymptotic Principal Component Analysis

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Asymptotic Principal Component Analysis

Description

Perform asymptotic PCA for a data set. Typically for cases in which the number of variables is greater than the number of data points.

Usage

apca(da, m)

Arguments

da	A T-by-k data set matrix, where T is the sample size and k is the dimension
m	The number of common factors

Details

Perform the PCA analysis of interchanging the roles of variables and observations.

Value

sdev	Square root of the eigenvalues
factors	The common factors
loadings	The loading matrix

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 6). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Examples

rtn=matrix(rnorm(1200),12,100)
sp100=apca(rtn,3)

MTS documentation built on April 11, 2022, 5:07 p.m.