Estimates sparse regression models (i.e., selecting few variables) in multi-task learning or transfer learning
Usage
sparselink(
x,
y,
family,
alpha.init = 0.95,
alpha = 1,
type = "exp",
nfolds = 10,
trial = FALSE
)Arguments
- x
\(n \times p\) matrix (multi-task learning) or list of \(n_k \times p\) matrices (transfer learning)
- y
\(n \times q\) 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
- trial
experimental argument (to be removed): Should exponents above 1 be used? Default: FALSE
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
# transfer learning
n <- c(100,50)
p <- 50
x <- lapply(X=n,function(x) matrix(data=stats::rnorm(n*p),nrow=x,ncol=p))
y <- lapply(X=n,function(x) stats::rnorm(x))
family <- "gaussian"
object <- sparselink(x=x,y=y,family=family)
#> alpha.init=0.95, alpha=1, trial=FALSE, type=exp
#> mode: transfer learning