R: Fits the adaptively scaled SGS model (AS-SGS).

as_sgs {sgs}

R Documentation

Fits the adaptively scaled SGS model (AS-SGS).

Description

Fits an SGS model using the noise estimation procedure, termed adaptively scaled SGS (Algorithm 2 from Feser and Evangelou (2023)). This adaptively estimates \lambda and then fits the model using the estimated value. It is an alternative approach to cross-validation (fit_sgs_cv()). The approach is only compatible with the SGS penalties.

Usage

as_sgs(
  X,
  y,
  groups,
  type = "linear",
  pen_method = 2,
  alpha = 0.95,
  vFDR = 0.1,
  gFDR = 0.1,
  standardise = "l2",
  intercept = TRUE,
  verbose = FALSE
)

Arguments

`X`	Input matrix of dimensions `n \times p`. Can be a sparse matrix (using class `"sparseMatrix"` from the `Matrix` package).
`y`	Output vector of dimension `n`. For `type="linear"` should be continuous and for `type="logistic"` should be a binary variable.
`groups`	A grouping structure for the input data. Should take the form of a vector of group indices.
`type`	The type of regression to perform. Supported values are: `"linear"` and `"logistic"`.
`pen_method`	The type of penalty sequences to use. `"1"` uses the vMean and gMean SGS sequences. `"2"` uses the vMax and gMax SGS sequences.
`alpha`	The value of `\alpha`, which defines the convex balance between SLOPE and gSLOPE. Must be between 0 and 1.
`vFDR`	Defines the desired variable false discovery rate (FDR) level, which determines the shape of the variable penalties. Must be between 0 and 1.
`gFDR`	Defines the desired group false discovery rate (FDR) level, which determines the shape of the group penalties. Must be between 0 and 1.
`standardise`	Type of standardisation to perform on `X`: `"l2"` standardises the input data to have `\ell_2` norms of one. `"l1"` standardises the input data to have `\ell_1` norms of one. `"sd"` standardises the input data to have standard deviation of one. `"none"` no standardisation applied.
`intercept`	Logical flag for whether to fit an intercept.
`verbose`	Logical flag for whether to print fitting information.

Value

An object of type "sgs" containing model fit information (see fit_sgs()).

References