SparsePCA: Sparse Principal Component Analysis

In sharp: Stability-enHanced Approaches using Resampling Procedures

Sparse Principal Component Analysis

Description

Runs a sparse Principal Component Analysis model using implementation from spca (if algo="sPCA") or spca (if algo="rSVD"). This function is not using stability.

Usage

SparsePCA(
  xdata,
  Lambda,
  ncomp = 1,
  scale = TRUE,
  keepX_previous = NULL,
  algorithm = "sPCA",
  ...
)

Arguments

xdata	data matrix with observations as rows and variables as columns.
Lambda	matrix of parameters controlling the number of selected variables at current component, as defined by ncomp.
ncomp	number of components.
scale	logical indicating if the data should be scaled (i.e. transformed so that all variables have a standard deviation of one).
keepX_previous	number of selected predictors in previous components. Only used if ncomp > 1.
algorithm	character string indicating the name of the algorithm to use for sparse PCA. Possible values are: "sPCA" (for the algorithm proposed by Zou, Hastie and Tibshirani and implemented in spca) or "rSVD" (for the regularised SVD approach proposed by Shen and Huang and implemented in spca).
...	additional arguments to be passed to spca (if algorithm="sPCA") or spca (if algorithm="rSVD").

Value

A list with:

selected	matrix of binary selection status. Rows correspond to different model parameters. Columns correspond to predictors.
beta_full	array of model coefficients. Rows correspond to different model parameters. Columns correspond to predictors (starting with "X") or outcomes (starting with "Y") variables for different components (denoted by "PC").

References

\insertRef

sparsePCAsharp

\insertRef

sparsePCASVDsharp

See Also

VariableSelection, BiSelection

Other penalised dimensionality reduction functions: GroupPLS(), SparseGroupPLS(), SparsePLS()

Examples

# Data simulation
set.seed(1)
simul <- SimulateRegression(n = 100, pk = 50, family = "gaussian")
x <- simul$xdata

# Sparse PCA (by Zou, Hastie, Tibshirani)
if (requireNamespace("elasticnet", quietly = TRUE)) {
  mypca <- SparsePCA(
    xdata = x, ncomp = 2,
    Lambda = c(1, 2), keepX_previous = 10, algorithm = "sPCA"
  )
}

# Sparse PCA (by Shen and Huang)
if (requireNamespace("mixOmics", quietly = TRUE)) {
  mypca <- SparsePCA(
    xdata = x, ncomp = 2,
    Lambda = c(1, 2), keepX_previous = 10, algorithm = "rSVD"
  )
}

sharp documentation built on April 11, 2025, 5:44 p.m.