INFO_ppca: Information Criterion for PPCA.

In WeiAkaneDeng/SPAC2: Statistical and Probabilistic Algorithms for Classification and Clustering

Description

The function returns the dimension that minimized the AIC or BIC based on the profile log-likelihood while considering all possible dimensions.

Usage

INFO_ppca(lambda, M, verbose = FALSE, tau = 0.001, AIC = TRUE)

Arguments

lambda	a numeric vector of positive sample eigenvalues pf length n
M	a positive integer for the number of observations or features.
verbose	a logical specifying whether the posterior evidence or the integer that minimized the evidence should be returned
tau	a tolerance threshold for the smallest eigenvalue, the default value is 0.001.
AIC	a logical indicator to use AIC as the information criterion, if FALSE, then BIC is used; the default option is AIC.

Value

an integer K between 1 and n that minimizes the AIC or BIC.

References

Akaike, H. (1974), A new look at the statistical model identification, **IEEE Transactions on Automatic Control**, *19*(6): 716–723, <doi:10.1109/TAC.1974.1100705>

Examples

## Not run: 
library(MASS)
X <- mvrnorm(1000, mu = rep(0,10), Sigma = diag(1,10))
eigen_values <- eigen(as.matrix(Matrix::nearPD(stats::cov(scale((X))))$mat))$val
INFO_ppca(lambda = eigen_values, M = 100)
INFO_ppca(lambda = eigen_values, M = 100, AIC = FALSE)
INFO_ppca(lambda = eigen_values, M = 5000)
INFO_ppca(lambda = eigen_values, M = 5000, AIC = FALSE)

## End(Not run)

WeiAkaneDeng/SPAC2 documentation built on Jan. 15, 2022, 5:01 a.m.