PPC2: 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 performs Projected Principal Component Analysis (PPC) on a given data set to reduce dimensionality. It calculates the estimated values for the loadings, specific variances, and the covariance matrix.

Usage

PPC2(data, m)

Arguments

data	The total data set to be analyzed.
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 <- PPC2(data, m)
print(results)

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