LFM (version 0.3.2)

PPC2: 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)

Value

Apro,Dpro,Sigmahatpro

Arguments

data

The total data set to be analyzed.

m

The number of principal components.

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)