PPC1: Apply the PPC method to the Laplace factor model

In LFM: Laplace Factor Model Analysis and Evaluation

Apply the PPC method to the Laplace factor model

Description

This function computes Perturbation Principal Component Analysis (PPC) for the provided input data, estimating factor loadings and uniquenesses. It calculates mean squared errors and loss metrics for the estimated values compared to true values.

Usage

PPC1(data, m)

Arguments

data	A matrix of input data.
m	The number of principal components.

Value

Apro,Dpro,Sigmahatpro

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 <- PPC1(data, m)
print(results)

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