spca: Sparse principal component analysis

In iSFun: Integrative Dimension Reduction Analysis for Multi-Source Data

Sparse principal component analysis

Description

This function provides penalty-based integrative sparse principal component analysis to obtain the direction of first principal component of a given dataset with high dimensions.

Usage

spca(x, mu1, eps = 1e-04, scale.x = TRUE, maxstep = 50,
  trace = FALSE)

Arguments

x	data matrix of explanatory variables.
mu1	numeric, sparsity penalty parameter.
eps	numeric, the threshold at which the algorithm terminates.
scale.x	character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE.
maxstep	numeric, maximum iteration steps. The default value is 50.
trace	character, "TRUE" or "FALSE". If TRUE, prints out its screening results of variables.

Value

An 'spca' object that contains the list of the following items.

• x: data matrix of explanatory variables with centered columns. If scale.x is TRUE, the columns of data matrix are standardized to have mean 0 and standard deviation 1.

• eigenvalue: the estimated first eigenvalue.

• eigenvector: the estimated first eigenvector.

• component: the estimated first principal component.

• variable: the screening results of variables.

• meanx: column mean of the original dataset x.

• normx: column standard deviation of the original dataset x.

See Also

See Also as ispca, meta.spca.

Examples

library(iSFun)
data("simData.pca")
x.spca <- do.call(rbind, simData.pca$x)
res_spca <- spca(x = x.spca, mu1 = 0.08, eps = 1e-3, scale.x = TRUE,
                 maxstep = 50, trace = FALSE)

iSFun documentation built on March 18, 2022, 7:41 p.m.