SLISE for robust regression (using a formula).

It is highly recommended that you normalise the data, either before using SLISE or by setting normalise = TRUE.

Usage

slise.formula(
  formula,
  data,
  epsilon,
  lambda1 = 0,
  lambda2 = 0,
  weight = NULL,
  normalise = FALSE,
  initialisation = slise_initialisation_candidates,
  ...
)

Arguments

formula

formula

data

data for the formula

epsilon

Error tolerance

lambda1

L1 regularisation coefficient (default: 0)

lambda2

L2 regularisation coefficient (default: 0)

weight

Optional weight vector (default: NULL)

normalise

Preprocess X and Y by scaling, note that epsilon is not scaled (default: FALSE)

initialisation

Function that gives the initial alpha and beta, or a list containing the initial alpha and beta (default: slise_initialisation_candidates)

...

Arguments passed on to graduated_optimisation, slise_initialisation_candidates

beta_max

Stopping sigmoid steepness (default: 20 / epsilon^2)

max_approx

Approximation ratio when selecting the next beta (default: 1.15)

max_iterations

Maximum number of OWL-QN iterations (default: 300)

debug

Should debug statement be printed each iteration (default: FALSE)

num_init

the number of initial subsets to generate (default: 500)

beta_max_init

the maximum sigmoid steepness in the initialisation

pca_treshold

the maximum number of columns without using PCA (default: 10)

Value

slise.object

Examples

data <- data.frame(y = rnorm(8), a = rnorm(8), b = rnorm(8))
model <- slise.formula(y ~ a * b + abs(a), data, 0.1, normalise = TRUE)