meta.spca: Meta-analytic sparse principal component analysis method in...

Description Usage Arguments Value References See Also Examples

View source: R/meta.spca.R

Description

This function provides penalty-based sparse principal component meta-analytic method to handle the multiple datasets with high dimensions generated under similar protocols, which is based on the principle of maximizing the summary statistics S.

Usage

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

Arguments

x

list of data matrices, L datasets of explanatory variables.

L

numeric, number of datasets.

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

A 'meta.spca' object that contains the list of the following items.

References

See Also

See Also as ispca.

Examples

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library(iSFun)
data("simData.pca")
x <- simData.pca$x
L <- length(x)

res <- meta.spca(x = x, L = L, mu1 = 0.5, trace = TRUE)

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

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