OPC.SFM {SFM} | R Documentation |
Apply the OPC method to the Skew factor model
Description
This function computes Online Principal Component Analysis (OPC) for the provided input data, estimating factor loadings and uniquenesses. It calculates mean squared errors and sparsity for the estimated values compared to true values.
Usage
OPC.SFM(data, m = m, A, D, p)
Arguments
data |
A matrix of input data. |
m |
The number of principal components. |
A |
The true factor loadings matrix. |
D |
The true uniquenesses matrix. |
p |
The number of variables. |
Value
A list containing:
Ao |
Estimated factor loadings. |
Do |
Estimated uniquenesses. |
MSEA |
Mean squared error for factor loadings. |
MSED |
Mean squared error for uniquenesses. |
tau |
The sparsity. |
Examples
library(SOPC)
library(matrixcalc)
library(MASS)
library(psych)
library(sn)
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)
r <- rsn(n*p,0,1)
epsilon=matrix(r,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- OPC.SFM(data, m, A, D, p)
print(results)
[Package SFM version 0.2.1 Index]