SPC: Apply the SPC method to the Laplace factor model

In LFM: Laplace Factor Model Analysis and Evaluation

Apply the SPC method to the Laplace factor model

Description

This function performs Sparse Principal Component Analysis (SPC) on the input data. It estimates factor loadings and uniquenesses while calculating mean squared errors and loss metrics for comparison with true values.

Usage

SPC(data, m, gamma)

Arguments

data	The data used in the SPC analysis.
m	is the number of principal component
gamma	is a sparse parameter

Value

A list containing:

As	Estimated factor loadings, a matrix of estimated factor loadings from the SPC analysis.
Ds	Estimated uniquenesses, a vector of estimated uniquenesses corresponding to each variable.

Examples

library(LaplacesDemon)
library(MASS)
n=1000
p=10
m=5
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
lanor <- rlaplace(n*p,0,1)
epsilon=matrix(lanor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- SPC(data, m, gamma=0.03)
print(results)

LFM documentation built on Dec. 6, 2025, 5:06 p.m.