VBsparsePCA (version 0.1.0)

VBsparsePCA: The main function for the variational Bayesian method for sparse PCA

Description

This function employs the PX-CAVI algorithm proposed in Ning (2021). The method uses the sparse spiked-covariance model and the spike and slab prior (see below). Two different slab densities can be used: independent Laplace densities and a multivariate normal density. In Ning (2021), it recommends choosing the multivariate normal distribution. The algorithm allows the user to decide whether she/he wants to center and scale their data. The user is also allowed to change the default values of the parameters of each prior.

Usage

VBsparsePCA(
  dat,
  r,
  lambda = 1,
  slab.prior = "MVN",
  max.iter = 100,
  eps = 0.001,
  jointly.row.sparse = TRUE,
  center.scale = FALSE,
  sig2.true = NA,
  threshold = 0.5,
  theta.int = NA,
  theta.var.int = NA,
  kappa.para1 = NA,
  kappa.para2 = NA,
  sigma.a = NA,
  sigma.b = NA
)

Arguments

dat

Data an \(n*p\) matrix.

r

Rank.

lambda

Tuning parameter for the density \(g\).

slab.prior

The density \(g\), the default is "MVN", the multivariate normal distribution. Another choice is "Laplace".

max.iter

The maximum number of iterations for running the algorithm.

eps

The convergence threshold; the default is \(10^{-4}\).

jointly.row.sparse

The default is true, which means that the jointly row sparsity assumption is used; one could not use this assumptio by changing it to false.

center.scale

The default if false. If true, then the input date will be centered and scaled.

sig2.true

The default is false, \(\sigma^2\) will be estimated; if sig2 is known and its value is given, then \(\sigma^2\) will not be estimated.

threshold

The threshold to determine whether \(\gamma_j\) is 0 or 1; the default value is 0.5.

theta.int

The initial value of theta mean; if not provided, the algorithm will estimate it using PCA.

theta.var.int

The initial value of theta.var; if not provided, the algorithm will set it to be 1e-3*diag(r).

kappa.para1

The value of \(\alpha_1\) of \(\pi(\kappa)\); default is 1.

kappa.para2

The value of \(\alpha_2\) of \(\pi(\kappa)\); default is \(p+1\).

sigma.a

The value of \(\sigma_a\) of \(\pi(\sigma^2)\); default is 1.

sigma.b

The value of \(\sigma_b\) of \(\pi(\sigma^2)\); default is 2.

Value

iter

The number of iterations to reach convergence.

selection

A vector (if \(r = 1\) or with the jointly row-sparsity assumption) or a matrix (if otherwise) containing the estimated value for \(\boldsymbol \gamma\).

loadings

The loadings matrix.

uncertainty

The covariance of each non-zero rows in the loadings matrix.

scores

Score functions for the \(r\) principal components.

sig2

Variance of the noise.

obj.fn

A vector contains the value of the objective function of each iteration. It can be used to check whether the algorithm converges

Details

The model is $$X_i = \theta w_i + \sigma \epsilon_i$$ where \(w_i \sim N(0, I_r), \epsilon \sim N(0,I_p)\).

The spike and slab prior is given by

$$\pi(\theta, \boldsymbol \gamma|\lambda_1, r) \propto \prod_{j=1}^p \left(\gamma_j \int_{A \in V_{r,r}} g(\theta_j|\lambda_1, A, r) \pi(A) d A+ (1-\gamma_j) \delta_0(\theta_j)\right)$$ $$g(\theta_j|\lambda_1, A, r) = C(\lambda_1)^r \exp(-\lambda_1 \|\beta_j\|_q^m)$$ $$\gamma_j| \kappa \sim Bernoulli(\kappa)$$ $$\kappa \sim Beta(\alpha_1, \alpha_2)$$ $$\sigma^2 \sim InvGamma(\sigma_a, \sigma_b)$$ where \(V_{r,r} = \{A \in R^{r \times r}: A'A = I_r\}\) and \(\delta_0\) is the Dirac measure at zero. The density \(g\) can be chosen to be the product of independent Laplace distribution (i.e., \(q = 1, m =1\)) or the multivariate normal distribution (i.e., \(q = 2, m = 2\)).

References

Ning, B. (2021). Spike and slab Bayesian sparse principal component analysis. arXiv:2102.00305.

Examples

# NOT RUN {
#In this example, the first 20 rows in the loadings matrix are nonzero, the rank is 2
set.seed(2021)
library(MASS)
library(pracma)
n <- 200
p <- 1000
s <- 20
r <- 2
sig2 <- 0.1
# generate eigenvectors
U.s <- randortho(s, type = c("orthonormal"))
if (r == 1) {
  U <- rep(0, p)
  U[1:s] <- as.vector(U.s[, 1:r])
} else {
  U <- matrix(0, p, r)
  U[1:s, ] <- U.s[, 1:r]
}
s.star <- rep(0, p)
s.star[1:s] <- 1
eigenvalue <- seq(20, 10, length.out = r)
# generate Sigma
if (r == 1) {
  theta.true <- U * sqrt(eigenvalue)
  Sigma <- tcrossprod(theta.true) + sig2*diag(p)
} else {
  theta.true <- U %*% sqrt(diag(eigenvalue))
  Sigma <- tcrossprod(theta.true) + sig2 * diag(p)
}
# generate n*p dataset
X <- t(mvrnorm(n, mu = rep(0, p), Sigma = Sigma))
result <- VBsparsePCA(dat = t(X), r = 2, jointly.row.sparse = TRUE, center.scale = FALSE)
loadings <- result$loadings
scores <- result$scores
# }