getCanWeightsMulti: Get Canonical Weight SmCCA Algorithm (No Subsampling)

In SmCCNet: Sparse Multiple Canonical Correlation Network Analysis Tool

Get Canonical Weight SmCCA Algorithm (No Subsampling)

Description

Run Sparse multiple Canonical Correlation Analysis (SmCCA) and return canonical weight vectors.

Usage

getCanWeightsMulti(
  X,
  Trait = NULL,
  Lambda,
  CCcoef = NULL,
  NoTrait = TRUE,
  trace = FALSE,
  TraitWeight = FALSE
)

Arguments

X	A list of omics data each with n subjects.
Trait	An n by 1 trait (phenotype) data for the same samples.
Lambda	Lasso penalty vector with length equals to the number of omics data (X). Lambda needs to be between 0 and 1.
CCcoef	Optional scaling factors for the SmCCA pairwise canonical correlations. If CCcoef = NULL (default), then the objective function is the total sum of all pairwise canonical correlations. It follows the column order of combn(T+1, 2), where T is the total number of omics data.
NoTrait	Whether or not trait (phenotype) information is provided, default is set to TRUE.
trace	Whether to display CCA algorithm trace, default is set to FALSE.
TraitWeight	Whether to return canonical weight for trait (phenotype), default is set to FALSE.

Value

A canonical weight vector with size of p by 1.

Examples

# This function is typically used as an internal function.
# It is also used when performing cross-validation,
# refer to multi-omics vignette for more detail.
# X <- list(X1,X2)
# result <- getCanWeightsMulti(X, Trait = as.matrix(Y), Lambda = c(0.5,0.5), NoTrait = FALSE)
# result <- getCanWeightsMulti(X, Trait = NULL, Lambda = c(0.5,0.5), NoTrait = TRUE)
# cccoef <- c(1,10,10)
# result <- getCanWeightsMulti(X, Trait = as.matrix(Y), CCcoef = cccoef, 
#                              Lambda = c(0.5,0.5), NoTrait = FALSE)

SmCCNet documentation built on May 29, 2024, 10:49 a.m.