DSFM (version 1.0.1)

DSPC: Distributed Sparse Principal Component Analysis

Description

Performs distributed sparse principal component analysis (DSPC) on a numeric dataset split across multiple nodes. Estimates sparse loading matrices, residual variances, and covariance matrices for each node.

Usage

DSPC(data, m, gamma, n1, K)

Value

A list with the following components:

Aspro

List of sparse loading matrices for each node.

Dspro

List of diagonal residual variance matrices for each node.

Sigmahatpro

List of covariance matrices for each node.

Arguments

data

A numeric matrix containing the total dataset.

m

An integer specifying the number of principal components.

gamma

A numeric value specifying the sparsity parameter for SPC.

n1

An integer specifying the length of each data subset.

K

An integer specifying the number of nodes.

Examples

set.seed(123)
data <- matrix(rnorm(500), nrow = 100, ncol = 5)
DSPC(data = data, m = 3, gamma = 0.03, n1 = 20, K = 5)