Tune the lasso penalty for sparse quantile regression

tune_lambda_quantile() tunes a penalty profile once on the original design and returns the selected penalty vector. The default grid rescales the quantreg::LassoLambdaHat() profile rather than using a single scalar, which keeps the tuning step aligned with the underlying quantile-lasso routine.

Usage

tune_lambda_quantile(
  x,
  y = NULL,
  tau = 0.5,
  method = c("cv", "bic"),
  rule = c("min", "one_se"),
  lambda_factors = NULL,
  lambda_inflation = 1,
  nlambda = 20,
  lambda_min_ratio = 0.05,
  folds = 5,
  repeats = 1,
  selector = quantile_lasso_selector,
  standardize = TRUE,
  eps = 1e-06,
  seed = NULL,
  data = NULL,
  subset = NULL,
  na.action = stats::na.fail,
  verbose = interactive(),
  ...
)

Arguments

x

Numeric design matrix or a formula.

y

Numeric response vector when x is a matrix.

tau

Quantile level in (0, 1). Can be a vector.

method

One of "cv" or "bic".

rule

Selection rule for choosing the tuned penalty from the candidate grid. "min" takes the minimizer of the tuning score, while "one_se" applies the one-standard-error rule when method = "cv".

lambda_factors

Optional positive multipliers applied to the default penalty profile.

lambda_inflation

Optional multiplier applied after tuning to enforce a stronger penalty for selection than for prediction.

nlambda

Number of tuning candidates when lambda_factors is NULL.

lambda_min_ratio

Smallest multiplier in the default grid.

folds

Number of folds when method = "cv".

repeats

Number of repeated fold assignments when method = "cv".

selector

Function used to fit the sparse quantile model.

standardize

Should tuning use the SelectBoost-normalized design?

eps

Numerical tolerance used to count active coefficients for the BIC heuristic.

seed

Optional random seed.

data

Optional data frame used when x is a formula.

subset

Optional subset expression used with the formula interface.

na.action

Missing-data handler used with the formula interface.

verbose

Should tuning report progress?

...

Additional arguments forwarded to selector.

Value

An object of class "tuned_lambda_quantile".

Examples

sim <- simulate_quantile_data(n = 60, p = 10, active = 1:3, seed = 2)
tuned <- tune_lambda_quantile(
  sim$x,
  sim$y,
  tau = 0.5,
  method = "bic",
  nlambda = 6
)
tuned$factor
#> [1] 0.05