pesel-package: Automatic estimation of number of principal components in PCA

In pesel: Automatic Estimation of Number of Principal Components in PCA

Automatic estimation of number of principal components in PCA

Description

Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL).

Details

Version: 0.7.5

Author(s)

Piotr Sobczyk, Julie Josse, Malgorzata Bogdan

Maintainer: Piotr Sobczyk [e-mail]

References

Piotr Sobczyk, Malgorzata Bogdan, Julie Josse "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood", Journal of Computational and Graphical Statistics 2017

Examples

# EXAMPLE 1 - noise
with(set.seed(23), pesel(matrix(rnorm(10000), ncol = 100), npc.min = 0))

# EXAMPLE 2 - fixed effects PCA model
sigma <- 0.5
k <-  5
n <- 100
numb.vars <- 10
# factors are drawn from normal distribution
factors <- replicate(k, rnorm(n, 0, 1))
# coefficients are drawn from uniform distribution
coeff <- replicate(numb.vars, rnorm(k, 0, 1))
SIGNAL <- scale(factors %*% coeff)
X <- SIGNAL + replicate(numb.vars, sigma * rnorm(n))
pesel(X)

pesel documentation built on Oct. 17, 2023, 5:14 p.m.