starting_point(beta, intercept, lambda, alpha) as_starting_point(object, specific = FALSE, ...) # S3 method for enpy_starting_points as_starting_point(object, specific = FALSE, ...) # S3 method for pense_fit as_starting_point(object, specific = FALSE, alpha, lambda, ...) # S3 method for pense_cvfit as_starting_point( object, specific = FALSE, alpha, lambda = c("min", "se"), se_mult = 1, ... )
intercept coefficient at the starting point.
optionally either a string specifying which penalty level to use
optional value for the
an object with estimates to use as starting points.
whether the estimates should be used as starting points only at the penalization level they are computed for. Defaults to using the estimates as starting points for all penalization levels.
further arguments passed to or from other methods.
an object of type
starting_points to be used as starting point for
A starting points can either be shared, i.e., used for every penalization level PENSE
estimates are computed for, or specific to one penalization level.
To create a specific starting point, provide the penalization parameters
alpha are missing, a shared starting point is created.
Shared and specific starting points can all be combined into a single list of starting points,
pense() handling them correctly.
Note that specific starting points will lead to the
lambda value being added to the
grid of penalization levels.
pense() for details.
Starting points computed via
enpy_initial_estimates() are by default shared starting points
but can be transformed to specific starting points via
as_starting_point(..., specific = TRUE).
When creating starting points from cross-validated fits, it is possible to extract only the
estimate with best CV performance (
lambda = "min"), or the estimate with CV performance
statistically indistinguishable from the best performance (
lambda = "se").
This is determined to be the estimate with prediction performance within
se_mult * cv_se from the best model.