Sparse regression for related problems

Usage

sparselink(
  x,
  y,
  family,
  alpha.init = 0.95,
  alpha = 1,
  type = "exp",
  nfolds = 10
)

Arguments

x: n x p matrix (multi-task learning) or list of n_k x p matrices (transfer learning)
y: n x q matrix (multi-task learning) or list of n_k-dimensional vectors (transfer learning)
family: character "gaussian" or "binomial"
alpha.init: elastic net mixing parameter for initial regressions, default: 0.95 (lasso-like elastic net)
alpha: elastic net mixing parameter of final regressions, default: 1 (lasso)
type: character
nfolds: number of cross-validation folds

Examples

# multi-task learning
n <- 100
p <- 50
q <- 3
family <- "gaussian"
x <- matrix(data=rnorm(n=n*p),nrow=n,ncol=p)
y <- matrix(data=rnorm(n*q),nrow=n,ncol=q)
object <- sparselink(x=x,y=y,family=family)
#> alpha.init=0.95, alpha=1, trial=FALSE, type=exp
#> mode: multi-target learning