Cross-validated Sparse Group LASSO with automatic alpha selection

Runs SGL over a grid of alpha values and selects the best combination of alpha and lambda via cross-validation.

Usage

multivariate_sgl_cv(
  X,
  Y,
  alpha_seq = c(0.1, 0.5, 0.9),
  nlambda = 15,
  nfolds = 5,
  verbose = FALSE,
  seed = 123,
  par = FALSE,
  n.cores = 1
)

Arguments

X

matrix, design matrix of SNP dosages

Y

matrix, matrix of G isoform expression across columns

alpha_seq

numeric vector, sequence of alpha values to try

nlambda

int, number of lambda values per alpha

nfolds

int, number of CV folds

verbose

logical

seed

int, random seed

par

logical, use parallel processing for CV folds

n.cores

int, number of cores for parallel processing

Value

isotwas_model object with best alpha and lambda