spca: Sparse principal component analysis

Description Usage Arguments Value See Also Examples

View source: R/spca.R

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

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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.

See Also

See Also as ispca.

Examples

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library(iSFun)
data("simData.pca")
x <- simData.pca$x[[1]]

res <- spca(x = x, mu1 = 0.08, trace = TRUE)

iSFun documentation built on Oct. 4, 2021, 9:06 a.m.

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