Create a starting point for starting the PENSE algorithm in pense(). Multiple starting points can be created by combining starting points via c(starting_point_1, starting_point_2, ...).

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,
...
)

## Arguments

beta beta coefficients at the starting point. Can be a numeric vector, a sparse vector of class dsparseVector, or a sparse matrix of class dgCMatrix with a single column. intercept coefficient at the starting point. optionally either a string specifying which penalty level to use ("min" or "se") or a numeric vector of the penalty levels to extract from object. Penalization levels not present in object are ignored with a warning. If NULL, all estimates in object are extracted. If a numeric vector, alpha must be given and a single number. optional value for the alpha hyper-parameter. If given, only estimates with matching alpha values are extracted. Values not present in object are ignored with a warning. 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. If lambda = "se", the multiple of standard errors to tolerate.

## Value

an object of type starting_points to be used as starting point for pense().

## Details

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 lambda and alpha. If lambda or 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, with pense() handling them correctly. Note that specific starting points will lead to the lambda value being added to the grid of penalization levels. See 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.

Other functions for initial estimates: enpy_initial_estimates(), prinsens()