Univariate elastic net on all columns — univariate

The function trains unvariate elastic models individually for all isoform transcripts

Usage

univariate_elasticnet(
  X,
  Y,
  Omega,
  family = "gaussian",
  scale = F,
  alpha = 0.5,
  nfolds = 5,
  verbose,
  par = F,
  n.cores = NULL,
  tx_names = NULL,
  seed
)

Arguments

X: matrix, design matrix of SNP dosages
Y: matrix, matrix of G isoform expression across columns
Omega: matrix, precision matrix of Y
family: character, glmnet glm family
scale: logical, T/F to scale Y by Omega
alpha: numeric, elastic net mixing parameter
nfolds: int, number of CV folds
verbose: logical
par: logical, uses mclapply to parallelize model fit
n.cores: int, number of parallel cores
tx_names: vector, character vector of tx names in order of columns of Y
seed: int, random seed

Value

data frame of elastic net, lasso, and LMM based predictions