# getCanWeightsMulti: Get Canonical Weight SmCCA Algorithm (No Subsampling) In SmCCNet: Sparse Multiple Canonical Correlation Network Analysis Tool

 getCanWeightsMulti R Documentation

## 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.