meta.spls: Meta-analytic sparse partial least squares method in...

Description Usage Arguments Value See Also Examples

View source: R/meta.spls.R

Description

This function provides penalty-based sparse canonical correlation 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.

Usage

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meta.spls(x, y, L, mu1, eps = 1e-04, kappa = 0.05, scale.x = TRUE,
  scale.y = TRUE, maxstep = 50, trace = FALSE)

Arguments

x

list of data matrices, L datasets of explanatory variables.

y

list of data matrices, L datasets of dependent variables.

L

numeric, number of datasets.

mu1

numeric, sparsity penalty parameter.

eps

numeric, the threshold at which the algorithm terminates.

kappa

numeric, 0 < kappa < 0.5 and the parameter reduces the effect of the concave part of objective function.

scale.x

character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE.

scale.y

character, "TRUE" or "FALSE", whether or not to scale the variables y. 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.spls' object that contains the list of the following items.

See Also

See Also as ispls.

Examples

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

res <- meta.spls(x = x, y = y, L = L, mu1 = 0.03, trace = TRUE)

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

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