Train one model
COPY_biglasso_part(
  X,
  y.train,
  ind.train,
  ind.col,
  covar.train,
  family,
  lambda,
  center,
  scale,
  resid,
  alpha,
  eps,
  max.iter,
  dfmax,
  ind.val,
  covar.val,
  y.val,
  n.abort,
  nlam.min,
  base.train,
  base.val,
  pf
)A named list with following variables:
A vector of intercepts, corresponding to each lambda.
The vector of coefficients that minimized the loss on the validation set.
A vector of length nlambda containing the number of
iterations until convergence at each value of lambda.
The sequence of regularization parameter values in the path.
Input parameter.
A vector containing either the residual sum of squares
(for linear models) or negative log-likelihood (for logistic models)
of the fitted model at each value of lambda.
A vector containing the loss for the corresponding validation set.
Reason the fitting has stopped.
The number of active (non-zero) variables along the regularization path.
The number of candidate variables (used in the gradient descent) along the regularization path.
Indices of training set.